Linköping Studies in Science and Technology

Thesis No. 1356

Mobility and Routing in a Delay-tolerant Network of Unmanned Aerial Vehicles

by

Erik Kuiper

Submitted to Linköping Institute of Technology at Linköping University in partial fulfilment of the requirements for the degree of Licentiate of Engineering

Department of Computer and Information Science Linköpings universitet

SE-581 83 Linköping, Sweden

Linköping 2008

Department of Computer and Information Science Linköpings universitet

SE-581 83 Linköping, Sweden

Mobility and Routing in a Delay-tolerant Network of Unmanned Aerial Vehicles

by

Erik Kuiper

April 2008 ISBN 978-91-7393-937-9

Linköping Studies in Science and Technology Thesis No. 1356 ISSN 0280-7971

LiU-Tek-Lic-2008:14

ABSTRACT

Technology has reached a point where it has become feasible to develop unmanned aerial vehicles (UAVs), that is aircraft without a human pilot on board. Given that future UAVs can be autonomous and cheap, applications of swarming UAVs are possible. In this thesis we have studied a reconnaissance application using swarming UAVs and how these UAVs can communicate the reconnaissance data. To guide the UAVs in their reconnaissance mission we have proposed a pheromone based mobility model that in a distributed manner guides the UAVs to areas not recently visited. Each UAV has a local pheromone map that it updates based on its reconnaissance scans. The information in the local map is regularly shared with a UAV’s neighbors. Evaluations have shown that the pheromone logic is very good at guiding the UAVs in their cooperative reconnaissance mission in a distributed manner. Analyzing the connectivity of the UAVs we found that they were heavily partitioned which meant that contemporaneous communication paths generally were not possible to establish. This means that traditional mobile ad hoc network (MANET) routing protocols like AODV, DSR and GPSR will generally fail. By using node mobility and the store-carry-forward principle of delay-tolerant routing the transfer of messages between nodes is still possible. In this thesis we propose location aware routing for delay-tolerant networks (LAROD). LAROD is a beacon-less geographical routing protocol for intermittently connected mobile ad hoc networks. Using static destinations we have shown by a comparative study that LAROD has almost as good delivery rate as an epidemic routing scheme, but at a substantially lower overhead.

This work has been supported by LinkLab, a research center for future aviation systems, established by Saab and Linköping University, and the KK foundation through the industrial graduate school SAVE-IT.

v

AcknowledgementsAcknowledgementsAcknowledgementsAcknowledgements

First I want to thank Saab, and then especially Anders Pettersson and Gunnar

Holmberg, for giving me this opportunity to pursue a PhD. With this thesis I

should be half way. Without the connection to a practically applicable

problem domain I would probably not have chosen to pursue a PhD. I also

want to thank my industrial advisor Mats Ekman. I might not have sought

your advice that extensively, but the discussions we had gave me some things

to think about.

To my academic advisor and supervisor Simin Nadjm-Tehrani I would like to

extend a thank for guiding me to this point. You especially taught me how to

write for an academic audience and not only to report my findings. I might

not entirely agree with the anatomy of academic articles, but hopefully I now

understand it reasonably well.

I also want to thank the other members of RTSlab for your friendship and

valuable comments. You helped me to become aware of some of the

assumptions I hade made and you pushed me to clarify and further

investigate some issues. I hope you will continue to take a coffee break at

three even after I am gone.

I am grateful to SAVE-IT and the KK foundation for partially funding my

research. You might not be in my thoughts every day, but without you this

research might never have been done.

Finally I want to thank my friend C. Without you I might never have selected

to work for Saab, and then this would never have happened.

Erik Kuiper

vi

vii

ContentsContentsContentsContents

1 Introduction .................................................................................................... 1 1.1 Mobile Ad-hoc Networks ..................................................................... 1 1.2 Intermittently connected MANETs..................................................... 3 1.3 Problem Description.............................................................................. 4 1.4 Contributions ......................................................................................... 6 1.5 Thesis Outline ........................................................................................ 7

2 Background ..................................................................................................... 9 2.1 Mobility Models..................................................................................... 9

2.1.1 Synthetic Mobility Models............................................................. 10 2.1.2 Real-World Mobility Models......................................................... 14

2.2 Routing.................................................................................................. 17 2.2.1 DTN Routing in Opportunistic Networks................................... 18 2.2.2 Beacons-less Routing ...................................................................... 20

3 Reconnaissance Mobility............................................................................ 23 3.1 Scenario................................................................................................. 23 3.2 Random Mobility Model .................................................................... 24 3.3 Distributed Pheromone Repel Mobility Model ............................... 25 3.4 Evaluation............................................................................................. 29

3.4.1 Scan Coverage ................................................................................. 30 3.4.2 Scan Characteristic.......................................................................... 34 3.4.3 Communication............................................................................... 37

4 Routing in DTNs.......................................................................................... 41 4.1 LAROD ................................................................................................. 41 4.2 Broadcast Delay-tolerant Routing (BDTR) ....................................... 46 4.3 Broadcast Routing (BR)....................................................................... 48 4.4 Evaluation............................................................................................. 48

4.4.1 Node density ................................................................................... 50 4.4.2 Node speed...................................................................................... 52 4.4.3 Time to live ...................................................................................... 53 4.4.4 Network load................................................................................... 55

5 Conclusions and Future Work................................................................... 57 5.1 Conclusions .......................................................................................... 57 5.2 Future Work ......................................................................................... 58

6 Acronyms....................................................................................................... 59

7 References ..................................................................................................... 61

viii

ix

List of FiguresList of FiguresList of FiguresList of Figures

Figure 1. Change of average direction near the edges. ................................. 12 Figure 2. Forwarding areas............................................................................... 21 Figure 3. Local pheromone map after 3600 seconds of simulation. ............ 26 Figure 4. Global pheromone view after 3600 seconds of simulation. ......... 26 Figure 5. Local pheromone map after 7200 seconds of simulation. ............ 27 Figure 6. Global pheromone view after 7200 seconds of simulation. ......... 27 Figure 7. Pheromone search pattern................................................................ 28 Figure 8. Pheromone mobility coverage with global pheromone map. ..... 31 Figure 9. Pheromone mobility coverage with 100% transfer probability. .. 32 Figure 10. Pheromone mobility coverage with 50% transfer probability. .... 32 Figure 11. Pheromone mobility coverage with 10% transfer probability. .... 32 Figure 12. Pheromone mobility coverage with 0% transfer probability....... 33 Figure 13. Random mobility coverage .............................................................. 33 Figure 14. Random Waypoint mobility coverage............................................ 33 Figure 15. Comparison of average coverage. ................................................... 34 Figure 16. Pheromone mobility with global pheromone map. ...................... 35 Figure 17. Pheromone mobility with 100% transfer probability.................... 35 Figure 18. Pheromone mobility with 50% transfer probability...................... 36 Figure 19. Pheromone mobility with 10% transfer probability...................... 36 Figure 20. Pheromone mobility with 0% transfer probability. ...................... 36 Figure 21. Random mobility. .............................................................................. 37 Figure 22. Random Waypoint mobility............................................................. 37 Figure 23. Pheromone mobility with global pheromone map. ...................... 38 Figure 24. Pheromone mobility with 100% transfer probability.................... 39 Figure 25. Pheromone mobility with 50% transfer probability...................... 39 Figure 26. Pheromone mobility with 10% transfer probability...................... 39 Figure 27. Pheromone mobility with 0% transfer probability. ...................... 40 Figure 28. Random mobility. .............................................................................. 40 Figure 29. Random Waypoint mobility............................................................. 40 Figure 30. LAROD forwarding areas. ............................................................... 42 Figure 31. Illustration of vectors used for delay time computations. ........... 44 Figure 32. LAROD pseudo code. ....................................................................... 45 Figure 33. BDTR pseudo code............................................................................ 47 Figure 34. Delivery ratio for different node densities..................................... 51 Figure 35. Overhead for different node densities............................................ 51 Figure 36. Delay for different node densities................................................... 51

x

Figure 37. Delivery ratio for different node speeds. ....................................... 52 Figure 38. Overhead for different node speeds. .............................................. 53 Figure 39. Delay for different node speeds. ..................................................... 53 Figure 40. Delivery ratio for different packet life times.................................. 54 Figure 41. Overhead for different packet life times. ....................................... 54 Figure 42. Delay for different packet life times................................................ 55 Figure 43. Delivery ratio for different network loads. .................................... 56 Figure 44. Overhead for different network loads............................................ 56 Figure 45. Delay for different network loads. .................................................. 56

xi

List of TablesList of TablesList of TablesList of Tables

Table 1. Random waypoint parameters ........................................................ 11 Table 2. Gauss-Markov parameters. .............................................................. 12 Table 3. Scenario parameters .......................................................................... 24 Table 4. Random action table.......................................................................... 24 Table 5. Pheromone map parameters. ........................................................... 25 Table 6. Pheromone parameter definition. ................................................... 28 Table 7. UAV pheromone action table. ......................................................... 29 Table 8. Never scanned area........................................................................... 35 Table 9. LAROD parameter definitions. ....................................................... 44 Table 10. BDTR parameter definitions. ........................................................... 46 Table 11. Scenario parameters .......................................................................... 49 Table 12. LAROD parameter values. ............................................................... 49 Table 13. BDTR parameter values.................................................................... 49

1

1111 IntroductionIntroductionIntroductionIntroduction

The sharing of information is vital for many tasks and the faster information

can be disseminated the sooner or better a task can be completed. With the

development of cheap wireless technologies like GSM and Wi-Fi information

is often available anytime and anywhere. The limitation of these technologies

is that they require an infrastructure of base stations to function. In

environments such as disaster areas or during wartime this type of

infrastructure is generally not available, but information exchange is still

desired. An option to communicate in these environments is to use long

range radios that enable point-to-point communication. The problems with

these are that they are often expensive, bulky and only provide low

bandwidth communication.

At the other end of the spectrum there are cheap, small, low power, high

bandwidth, but short range radio technologies. If a lot of nodes were

equipped with this type of radio then they could automatically form a

network and cooperate to forward messages for each other. These types of

networks that are cooperatively formed and do not rely on any infrastructure

are often called ad-hoc networks. To create local ad-hoc networks there exist

technologies like Bluetooth [45] and ZigBee [56], but the creation of larger ad-

hoc networks is still in the research domain.

1.11.11.11.1 Mobile AdMobile AdMobile AdMobile Ad----hoc Networkshoc Networkshoc Networkshoc Networks

A mobile ad-hoc network (MANET) is a self-organizing network where

nodes with wireless radios cooperate to provide network connectivity. The

opposite of an ad-hoc network is a managed network where network

connectivity is provided by dedicated wireless access points. These access

points are generally non-mobile fixed installations and they are

preconfigured to efficiently share the wireless medium. Examples of

managed networks are GSM and Wi-Fi hotspots.

2

A basic service required from any network is the ability to route messages1

from source to destination. In wireline networks, such as the internet, routing

is performed by dedicated equipment that exchange information with each

other to build up a view of the network links between routers and end

stations. This works well since links are stable and change infrequently. In

MANETs links are less stable due to mobility, interference and fading. This

means that the network layout is constantly changing and this has to be

handled by the routing protocol. These constant network changes are

challenging since it becomes practically impossible to distribute a consistent

view of the network to all nodes.

In IP based networking the IP address is both a machine unique identifier and

a hierarchical location identifier. This means that the address is the mean to

locate the node. In a network of mobile nodes hierarchical addressing is not

practical since nodes would constantly have to change address due to node

mobility. This means that each node will have to be a separate entry in a

routing table resulting in large routing tables for large networks.

The routing protocols suggested for MANETs can be classified into the two

dimensions proactive–reactive and topological–geographic. Proactive

protocols constantly maintain routing tables in the nodes while reactive

protocols only acquire routing information when it is actually needed.

Topological protocols build a view of the network based on how the nodes

can communicate with each other while geographical protocols route

information based on the geographical location of the nodes.

The most widely accepted topology based routing protocols for MANETs are

those accepted by the Internet Engineering Task Force (IETF). They have

published two proactive routing protocols (Optimized link state routing

(OLSR) [9], Topology dissemination based on reverse-path forwarding

(TBRPF) [35]) and two reactive protocols (Ad hoc on-demand distance vector

routing (AODV) [38], Dynamic source routing (DSR) [23]). They are currently

working on replacing these protocols with one proactive and one reactive

protocol.

There have been some published reports on the relative performance of these

protocols [8][18] and though far from conclusive they find that OLSR does

1 In this thesis the words message and packet are generally used interchangeably. If there is a

separation then a message is some coherent data that needs to be transmitted and a packet is the

physical format of (a part of) a message that is transmitted in the network.

3

not perform well in mobile environments compared to AODV and DSR

(TBRPF is not evaluated). The main reason is the functioning of the proactive

OLSR. With mobile nodes the protocol has to spend a lot of effort trying to

update the routing tables, tables that never will be accurate.

To remove the need to maintain routing information in routing tables

geographical routing protocols have been suggested where Greedy perimeter

stateless routing (GPSR) [24] is the most widely known. Instead of

maintaining a route to a destination a geographical routing protocol forwards

packets to the geographical location of the destination by locally at each hop

selecting a node that will reduce the distance to the destination. This

generally means that no global routing information needs to be maintained

by the nodes which it is good for highly dynamic environments. The Achilles

heel of geographical routing is how to distribute node positions in the

network so the sources know where the destinations are. For this there need

to be a location service the sources can access. A main problem with location

services is how to balance between the overhead of distributing the location

information to location servers and the cost of a location query [10]. A related

problem that has to be managed is node position errors in packets due to

node movement.

1.21.21.21.2 Intermittently connected MANETsIntermittently connected MANETsIntermittently connected MANETsIntermittently connected MANETs

The routing protocols described in the previous section all assume that the

node density is high enough to guarantee the existence of a contemporaneous

path between any sender and receiver. A contemporaneous path is a

sequence of wireless links between two communicating nodes such that they

can communicate with each other instantaneously if the bandwidth was

infinite and there were no transmission delays. If such a path does not exist

they will fail to deliver messages. This does not mean that it is impossible to

route messages in the absence of contemporaneous paths, only that other

principles need to be used.

An intermittently connected MANET is a network where the nodes are so

sparse or moving in such a way that there exists at least two groups of nodes

for which there is no contemporaneous path between the nodes. If the node

movement is such that the intercontact times are unknown and unpredictable

then the node contacts are called opportunistic. The enabler to route in

intermittently connected networks is node mobility. To overcome

communication gaps messages are stored in nodes and carried until they can

4

be forwarded. A consequence of this store-carry-forward principle is that

delivery times will be longer than if a contemporaneous path existed2.

Due to the long delays in these networks the connectivity assumptions made

in most networks are no longer valid. As a consequence the responsibility for

reliable transfers should be moved from the source-destination pair to a

system of custodians. The reason to not use the source-destination pair as is

normally done is that this generally requires many round-trip exchanges,

exchanges that take much time. Instead the responsibility of reliably

transferring a message is moved to the network and a system of custodians.

In such a system a message is transferred between custodians that take over

delivery responsibility of the message.

The most straightforward method to send a packet to a node whose location

is unknown and where the best path to reach it is unknown is to send it to all

nodes in the network. This is done by Epidemic Routing [51]. The problem

with this method is that it requires much bandwidth and storage resources.

The other extreme is to keep the packet in the source node until the source

node meets the destination node due to mobility as is done by Direct

Transmission (no relaying) [13]. To be able to better guide a packet to the

destination several routing protocols have been proposed that use historical

encounter data to decide how to forward a packet. Examples are Utility-

based Routing [46], MaxProp [3] and Context-aware routing (CAR) [30].

As for MANETs geographical routing can also be done in intermittently

connected networks if the nodes are aware of their geographical position.

Two examples of protocols that perform geographical routing in

intermittently connected MANETs are Disruption-tolerant geographic

routing for wireless ad hoc networks (DTGR) [27] and Motion vector (MoVe)

[26].

1.31.31.31.3 Problem DescriptionProblem DescriptionProblem DescriptionProblem Description

A challenging aspect in the study of routing algorithms is that the node

mobility pattern affects the routing performance [21][28][41][55]. This means

that a routing protocol should be tested in an environment that as close as

possible resembles the environment it will be deployed in. To verify that a

2 This assumes that the transfer rate using wireless transfers (wireless transfer rate * distance) is

much higher than the rate using node mobility (speed * message size).

5

routing protocol is suitable for general use it should be tested under several

reasonable, but characteristically different, environments.

As the main scenario under which to evaluate routing we have chosen a

reconnaissance mission in which a group of unmanned aerial vehicles

(UAVs) shall detect units on the ground by regularly scanning all parts of a

specified area. Since it is probable that the units on the ground do not want to

be detected by the UAVs there may not be any apparent pattern to when a

UAV scans a particular area. The same type of requirements can be found on

the searching behavior in the FOPEN (Foliage Penetration) scenario reported

in the work by Parunak et al. [37]. Since most mobility models used in

MANET research are either synthetic [4] or based on human mobility

[20][25][41][49] we have had to develop a mobility model for this scenario.

Depending on used node density and communication range the nodes

moving under our mobility model will form an opportunistic intermittently

connected network. The challenge is then to route messages in a reliable

manner while limiting the use of system resources, such as storage, power

and wireless bandwidth, in the realistic mobility scenarios envisioned. Since

UAVs are location aware due to navigational requirements this could be used

to perform geographical routing if the position of the destination is known.

To conserve bandwidth and to make it possible to use our routing algorithm

in energy-constrained systems we will explore the viability of beacon-less

geographical routing in opportunistic intermittently connected MANETs.

Beacons are regularly transmitted special messages that are used by many

routing algorithms to determine a node’s neighbors. The reason to evaluate

beacon-less routing is that beacons as commonly used by routing protocols

have the following problems [15]:

• They consume a lot of bandwidth

• The consume energy from nodes even if they are not performing any

routing.

• Beacon information may not accurately represent actual communication

possibilities due to node mobility since beacon reception and due to

fading.

6

1.41.41.41.4 ContributionsContributionsContributionsContributions

The main contributions in this thesis are twofold; the study of mobility

models and a proposed geographical routing algorithm for intermittently

connected MANETs.

A distributed pheromone mobility model for reconnaissance applications

To evaluate our suggested routing protocol we have used a military

reconnaissance scan scenario. The objective in the scenario is that a group of

UAVs shall cooperatively scan an area regularly to detect units on the

ground. Since it is probable that the units on the ground do not want to be

detected by the UAVs they should not move in a deterministic pattern.

To coordinate the UAVs we have designed a distributed pheromone mobility

model. By using pheromones and localized search the UAVs are guided to

areas not recently visited by other UAVs. When a UAV moves around it

places pheromones on the areas it has scanned. Since it is not possible to place

these pheromones in the environment as would have be done in a natural

system the UAV places them in a local pheromone map. To share this

pheromone information with the other UAVs each UAV regularly broadcasts

a local area pheromone map. All UAVs that receive the broadcast merge this

information into their pheromone map.

The distributed pheromone mobility results presented in this thesis extends

the results presented in the following paper:

Erik Kuiper, Simin Nadjm-Tehrani. Mobility Models for UAV Group

Reconnaissance Applications. Proceedings of International Conference on Wireless

and Mobile Communications. July 2006. IEEE

A beacon-less geographical routing protocol for intermittently connected

networks

Most routing protocols use beacons to know who their neighbors are. While it

is a quite simple and effective method it has the problem of creating a lot of

overhead, and the information gathered by beacons is always to some extent

out of date.

In this thesis we present location aware routing for delay-tolerant networks

(LAROD). LAROD forwards messages using greedy geographical routing

without the use of beacons and employs the store-carry-forward principle

when a message cannot be forwarded due to network partitions.

7

LAROD is previously presented in:

Erik Kuiper, Simin Nadjm-Tehrani, Geographical Routing in Intermittently

Connected Ad Hoc Networks. The First IEEE International Workshop on

Opportunistic Networking. March 2008. IEEE

1.51.51.51.5 Thesis OutlineThesis OutlineThesis OutlineThesis Outline

This thesis is organized as follows. In Chapter 2 other work relating to

mobility models and routing in intermittently connected MANETs are

presented. In Chapter 3 our distributed pheromone model is described and

evaluated. In Chapter 4 LAROD is described and evaluated. Finally Chapter 5

presents our conclusions and ideas for future work.

8

9

2222 BackgroundBackgroundBackgroundBackground

This chapter gives an overview of the current state regarding mobility models

for MANET research and routing in intermittently connected MANETs.

2.12.12.12.1 Mobility ModelsMobility ModelsMobility ModelsMobility Models

A natural way to simulate wireless networks is to place all the nodes in a

Euclidian space and simulate the radio communication based on the nodes’

placement using a radio model. To control the placement and mobility of the

nodes a model or real-world mobility trace is used. The mobility models used

in ad hoc network research are usually synthetic constructs [4], but recently

some mobility models have been suggested that are based on data from

mobility traces [25][54]. The choice of mobility description is important in

MANET routing research since it has been shown that the mobility affect the

performance of routing protocols [21][28][32][41][55].

Even if a real-world trace has the actual movement of the nodes under study

the problem is that a trace only represents one possible movement pattern. To

be able to statistically verify a routing protocol more data is generally needed

than traces can give. To provide this a mobility model is needed that can

generate traces with the same properties as real collected traces would have.

Such a model can then generate as many traces as needed providing

statistical diversity while still maintaining realistic properties. Any mobility

model claiming to model real-world mobility should be verified against real-

world traces as done by Kim et al. [25] and Zhang et al. [54]. Even though the

mobility models used by Marfia et al. [28] have not been validated against

real traces the routing results reported illustrate the impact small differences

in mobility can have on routing results.

Another aspect of mobility models is if they are intended to be descriptive or

prescriptive. A descriptive mobility model tries to describe how nodes move

in some kind of environment. A prescriptive mobility model on the other

hand describes how a node should move. The main difference between the

two types is how they are verified. A descriptive model needs to be verified

10

against the real mobility it tries to describe. A prescriptive model on the other

hand needs to be verified against what the nodes try to achieve with their

mobility. A good example of descriptive mobility models are the vehicle

models used by Marfia et al. in [28]. The pheromone models used by Sauter et

al. [42] are on the other hand prescriptive since they are used to control how

the nodes move to achieve a mission objective.

2.1.1 Synthetic Mobility Models

Since it is difficult, costly and not always possible to obtain real-world

mobility traces from which mobility models can be created researchers have

developed synthetic mobility models. These models range from the very

abstract random waypoint mobility model [22] to more realistic node

movement like the obstacle mobility model [21] and vehicular mobility [32].

Many of the used mobility models are entity mobility models which mean

that the nodes move independently. In reality the decision by a node of how

to move is often influenced by other nodes. This fact has made people design

mobility models like the reference point group mobility model [17] and

pheromone based models like the ones suggested by Sauter et al. [42].

A survey of different mobility models used in MANET research can be found

in [4]. In the following subsection some synthetic mobility models are

described that have been used in the evaluations or influenced the mobility

models used in this thesis.

2.1.1.1 Random waypoint

The most widely used mobility model is MANET research is the random

waypoint mobility model [22]. In random waypoint a node randomly selects

a destination and speed and then moves in a straight line to the selected

destination. When the destination is reached the node optionally pauses for

some random time until the process is restarted. The random values used are

normally drawn from a rectangular distribution.

The simplicity of the model and its widespread use means that it has

maintained its popularity, but there are problems with the model. Without

reflecting too much on the properties of the mobility model many researchers

initialize a simulation by distributing nodes randomly over the simulation

area using a rectangular distribution. The problem with this is that the

stationary distribution is not rectangular. In [33] Navidi and Camp showed

that the stationary distribution is actually according to equation (1) assuming

11

a rectangular simulation area of unit size, no pause times and rectangular

distributions in selecting speed and destination.

( ) ( )[ ]9179.1

2)(0

1 1

0

1

0

212112

2/1212

212

≈−

−+−= ∫ ∫ ∫ ∫k

dxdxdydyxx

yyxxkxg

x

x (1)

The node speeds are not uniform either, but instead distributed according to

equation (2) with the same assumptions. The parameters of the equations are

defined in Table 1.

<<=otherwise0

)/log(

1)( 10

01

vsvvvssf (2)

Table 1. Random waypoint parameters

Parameter Description

x1, x2 ,y1 ,y2 Path end points. k Constant to get a total density of 1. s Node speed. v0 Node minimum speed. v1 Node maximum speed.

These results mean that if a simulation is initialized by placing the nodes

using a rectangular distribution the statistical mobility properties will

continuously change until the steady state distribution is reached. This means

that simulation results obtained from the beginning of the simulation will be

different compared to results obtained late in a simulation. In [33] Navidi and

Camp also derived expressions for the speed and x- and y-coordinates with

pausing.

2.1.1.2 Gauss-Markov

Instead of determining the destination of a node you could regularly update

the direction in which the node is moving. The Gauss-Markov mobility model

[50][4] does this by updating the speed and direction of a node at fixed

intervals. Equations (3) and (4) describe the updates and the parameters are

defined in Table 2.

12

( ) ( )1

21 11

−−+−+= − nxnn ssss ααα (3)

( ) ( )1

21 11

−−+−+= − nxnn dddd ααα (4)

Table 2. Gauss-Markov parameters.

Parameter Description

ns Speed at step n.

s Average speed.

xs Random variable from a Gaussian distribution.

nd Direction at step n.

d Average direction.

xd Random variable from a Gaussian distribution.

α Randomness factor. 10 ≤≤ α

To ensure that a node does not move outside the simulation area the average

direction is changed when the node approaches the edge according to Figure

1.

Figure 1. Change of average direction near the edges.

270º

90º 45º

0º

315º 225º

135º

180º

13

2.1.1.3 Pheromone Based Mobility Models

Animals like ants use pheromones to guide them to the locations they need to

go to like food resources. The distributed nature of pheromone based systems

and the observation that complex behavior can emerge from the simple

control logic of the agents have made pheromone controlled mobility

interesting to study. The viability of the principle has been shown by

simulation and practical tests [12][37][42].

Sauter et al. [42] show by simulation that pheromone logic can be used for

several types of surveillance and target acquisition and tracking scenarios.

They have also shown by practical demonstration that the technique works in

practice. To guide the vehicles several types of pheromones are used, both

repulsive and attractive. Repulsive pheromones will make a vehicle avoid an

area where attractive pheromones will encourage vehicles to come to an area.

For the basic surveillance scenario two types of pheromones are used, one

repulsive and one attractive. In their scenario the area to be surveyed

generates attractive pheromones. When an area is visited the attractive

pheromones are removed and no new pheromones are generated for some set

time. To avoid that two vehicles try to survey the same area a vehicle places

repulsive pheromones in the next place it plans to move to. The pheromones

placed diffuse, that is slowly spread in the local environment. This creates

pheromone gradients that the vehicles use to guide their movement. There

are two main issues with their model. The first is that there seems to be a

global pheromone map that all agents can access. This might closely simulate

the real-life insect pheromone systems, but in a mechanical system where

pheromones need to be placed in a virtual map this means that there is a

central node managing the map. This design makes the system sensitive to

the failure of that node and all vehicles require good communication to this

node. Another issue is that they do not discuss how a vehicle determines

where to go. That it is based on the pheromone map is clear, but the areas

evaluated in order to select where to go are not described.

Parunak et al. [37] propose two approaches to perform target localization and

imaging. In the entity (individualistic) approach the UAVs use offline

determined paths to guide their movement. In the group (team) approach

visitation pheromones are used to deter UAVs from visiting areas recently

visited. To produce a distributed and robust solution each vehicle maintains

its own pheromone map. When a UAV passes through an area it updates its

internal map and broadcasts its position, which makes it possible for all

14

UAVs within communication range to update their maps. When a UAV shall

decide on its movement it randomly selects a location, where the probability

is inversely proportional to the distance to the location and the pheromone

concentration in the location. Unfortunately the paper does not provide any

evidence of the performance of the localization and imaging approaches,

which makes them difficult to evaluate.

Gaudiano et al. [12] test several control principles for an area-coverage

mission. From the tested approaches the pheromone one was the best. The

problem with their pheromone strategy is that is seems to rely on a global

pheromone map, giving the same problem as with the Sauter et al. solution.

Additionally, the pheromones do not fade with time (dissipate) in the simple

reconnaissance scenario, a property that they do use in a suppression mission

scenario also presented in the paper. In the suppression mission the UAVs

search for mobile targets and when found they try to destroy them.

2.1.2 Real-World Mobility Models

To better resemble real-world behavior, mobility models can be designed

based on observed real-world movement. Researchers have essentially used

three types of mobility information to create mobility models based on real-

world data.

• Actual node movement

• Node connections

• Node connections to a base station

To be able to create a mobility model based on observed behavior the system

to be described must actually exist. If that is not the case then synthetic

modeling is the only option.

Due to the cost and difficulty of collecting traces the trace data will generally

not be enough to run the amount of simulations needed to get enough

confidence in the performance of tested routing protocols. For this reason it is

generally good to build a synthetic model based on trace data. The model can

then generate an infinite number of traces with the same statistical mobility

properties as the real-world nodes have.

15

2.1.2.1 Position Based Traces

The best traces are those that collect the actual node mobility. Unfortunately

these are very difficult and expensive to collect since a large number of nodes

need to be equipped with position tracing equipment. For this reason large

scale position based traces have not been collected by the research

community. In [41] Rhee et al. collected traces from in total 44 individuals at

five different sites. While this is a relatively small population they found that

all walks had statistical features similar to those of Levy walks [43].

Instead of actually collecting node movement Hsu et al. [19] have performed

surveys on the USC campus and have constructed a model mobility where

nodes move between popular locations. They found that people tend to

aggregate at popular locations and that ad hoc network connectivity between

these locations is poor due to the low concentration of nodes between the

popular locations.

2.1.2.2 Node Connection Based Traces

Instead of collecting node positions it is generally easier to collect node inter

contact times (ICTs). As with collecting position based traces all nodes need

to be equipped with measuring devices, but instead of measuring where a

node is the times at which the nodes are in communication range are

measured. From these traces it is generally not possible to create a mobility

model in the Euclidian space, but instead an inter contact model is created.

The limit of such a model is that radio interference is generally difficult to

represent in simulations using inter contact models.

Examples of collected ICT traces are the studies by Su et al. [49] and Hui et al.

[20][6]. Both groups recorded the ICTs of humans carrying specially prepared

Bluetooth devices. The traces were collected from ten to fifty persons. A

consequence of the small populations in the traces is that the routing options

are much more limited than if a larger population would have been used. Su

et al. performed some routing experiments on the collected traces. They got a

median latency of just under three days with a delivery ratio of 86% with

epidemic routing. For most practical applications these results are not good

enough, but the authors expect that the results would be vastly improved

with a larger population, that is a denser network.

Hui et al. found that the inter contact times exhibited a power law

distribution with a coefficient of less than one. Analyzing what a power law

distribution meant for routing they found that with a coefficient of less than

16

one all naive routing algorithms (including epidemic routing) had an infinite

expected delay. Since normally used mobility models like random waypoint

do not exhibit this type of ICTs there is a need to investigate routing

performance under this type of mobility.

At the University of Massachusetts, Amherst, a testbed composed of 30

busses has been constructed [3]. Each bus is equipped with a computer with

two IEEE 802.11b interfaces and a GPS receiver. From this testbed they have

collected actual contacts and transfer possibilities. This means that the traces

contain the actual amount of data that can be transferred at each contact. The

GPS traces collected contained several gaps where contact with the satellites

had been lost due to placement constraint of the GPS receiver. To be able to

use the GPS data the missing parts had to be reconstructed [2].

2.1.2.3 Base Station Based Traces

In networks with base stations the connection and disconnections of nodes to

the base stations can be measured. The advantage compared to the two

previously presented methods is that the nodes do not need any

instrumentation and the connection information is normally collected anyway

by the base stations. The disadvantage is that only approximate node location

and node inter contact information is available. Two publicly available data

sets from Wi-Fi base stations are from UCSD [29] and Dartmouth College

[16].

In [44] Song et al. used the Dartmouth traces to create a connection trace with

the assumption that all nodes connected to a base station can communicate

with each other, but with no other nodes. This is a very rough model since it

does not take interference into account and it makes very simplifying

assumption regarding the nodes that can communicate with each other. Two

nodes connected to the same base station might not be able to communicate if

they are located at opposite ends and two nodes connected to different base

stations should be able to communicate if the nodes are located close to each

other.

If the locations of the base stations are known and if some node movement

properties are known then a mobility model from the base station traces can

be created. In [25] Kim et al. have used traces from Wi-Fi phone users at the

Dartmouth College to create a mobility model for these users. Combining

syslog data from the base stations and with the knowledge of the locations of

the base stations they created a mobility model for Wi-Fi phone users. The

17

model was validated against walks from users holding both a phone and a

GPS receiver.

2.22.22.22.2 RoutingRoutingRoutingRouting

Routing in connected mobile ad hoc networks has been studied extensively

and routing protocols like AODV [38], DSR [23] and GPSR [24] have been

suggested. All these protocols assume the existence of a contemporaneous

path between sender and receiver. In networks without contemporaneous

paths, but where node mobility can overcome partitions, a different type of

routing algorithm is required.

In RFC 4838 [5] Cerf et al. describe an architecture for delay-tolerant and

intermittently connected networks (DTNs). Their architecture is designed for

heterogeneous networks that are subject to long delays and/or discontinuous

end-to-end connectivity. The architecture is based on asynchronous

messaging and uses postal mail as a model of service classes and delivery

semantics. The architecture makes the following three assumptions:

• That storage is available and well distributed throughout the network.

• That storage is sufficiently persistent and robust to store data until

forwarding can occur.

• That the “store-and-forward” model is a better choice than attempting to

effect continuous connectivity or other alternatives.

Due to the long delays and disconnections in DTNs end-to-end reliability

methods like acknowledgements and timed out retransmissions are not

suitable for DTNs. To be able to offer reliable transfers in DTNs RFC 4838

provide custody transfer. In custody transfer a message is moved between

custodians that take responsibility for reliable delivery of the message. In

essence the network guarantees that a message is not lost.

The mobility of the nodes does mean that the network topology will

constantly change and that nodes constantly come in contact with new nodes

and leave the communication range of others. In RFC 4838 Cerf et al. classify

the contacts based on their predictability into scheduled, predicted and

opportunistic contacts. With scheduled contacts the nodes know when they

will be able to communicate with a specific peer. If nodes can estimate likely

meeting times or meeting frequencies you have a network with predicted

contacts. If no information is available on node contacts then the contacts are

18

opportunistic. In this thesis we will study routing in DTNs with opportunistic

contacts.

An overview of different routing strategies in delay-tolerant networks can be

found in Zhang’s survey [53].

2.2.1 DTN Routing in Opportunistic Networks

Routing in DTNs with opportunistic contacts is challenging since contact

times and durations are not known in advance. The challenge for the routing

protocol is to determine if a packet shall be handed over to a peer or not

when they meet. Factors that influence this decision are probability that the

peer can move the packet closer to the destination, available buffer spaces in

the two nodes and relative priority to forward this packet compared to other

packets the node holds. If nodes are location aware then the relative position

and direction of the nodes can be used to influence the forwarding decision.

Three examples of location unaware routing protocols for this environment

are Randomized Routing [46], Epidemic Routing [51] and Spray and Wait

[47].

In Randomized Routing only a single copy of a packet is present in the

network. When two nodes meet a packet is handed over to the other node at

some set probability. This means that a packet randomly walks around in the

network until it reaches the destination. This routing principle is better than

keeping a packet at the source node until it comes in contact with the

destination provided that the transmission speed is faster than the mode

movement or if node movements are local.

In Epidemic Routing (ER) all packets are distributed to all nodes in the

network (or at least a considerably large subset of nodes) giving a high cost in

both transfer and storage overhead. When two nodes meet they exchange

information on the messages stored in the nodes. Each node then decides on

the messages it wants to receive and request these from the other node. If a

node’s buffer space becomes full it drops the oldest messages first to make

place for new messages. A major problem with ER is that some messages

might not be transmitted in an overload situation since there is no

prioritization regarding transmission or drop order. Due to the epidemic

spread of messages in ER the network will be overloaded even at relatively

low transmission rates. To better handle transmission in an overload situation

Ramanathan et al. have proposed prioritized epidemic routing (PREP) [39].

The addition PREP does to ER is that it prioritizes packets when it comes to

19

transmission and deletion. By this it ensures that the packet that has the most

to gain on being transferred gets transmitted first and when a packet needs to

be dropped the packet expected to suffer the least from being removed is

dropped first. By these simple mechanisms PREP manages a good delivery

ratio even when the network is overloaded.

In Spray and Wait a packet is distributed to a limited number of nodes who

hold on to the packet until they meet the destination. The recommended

initial distribution method is to use binary spraying. When a node with more

than one copy of the packet meets a node that has not seen the packet then

half of the packets are handed over to the new node. With Spray and Wait a

destination close to the source will probably receive the packet during the

spraying phase. Destinations further away will have to wait until node

mobility moves a node that stores the packet within communication distance

to the destination. A strength of Spray and Wait is that the transmission

overhead of each generated packet is bounded. Spray and Wait can be an

efficient protocol if the nodes that carry the packet cover a large part of the

network with their mobility. To improve Spray and Wait Spyropoulos et al.

[48] have suggested to only spray to nodes that are more likely to encounter

the destination. Each node has then a utility value and only nodes with a

good enough utility value will be selected for spraying.

If the nodes are location aware and the (approximate) location of the

destination is known then the packets can be forwarded by geographic

routing. Li et al. [27] have modified GPSR [24] to better handle temporary

disruptions in relatively sparse networks (55 nodes/km² compared to our

even sparser scenario that has 10-30 nodes/km²). By using temporary storage

(up to 2 seconds) and having a set of possibly reachable neighbors they

substantially increased the delivery ratio compared to GPSR. Their approach

is geared towards handling short temporary disruptions due to obstructions,

node mobility or interference, and not intended to handle substantial

disconnections.

LeBrun et al. [26] have performed geographical routing in a very sparse (0.3-

4.4 nodes/km²) delay-tolerant network with a stationary destination. In their

motion vector (MoVe) routing algorithm a message is handed over to a peer

if, given their current directions, the peer is expected to come closer to the

destination than the holder of the packet. To limit the overhead MoVe uses a

request-response mechanism. This means that only nodes holding a message

transmit HELLO messages. When another node hears a HELLO message it

20

responds with a RESPONSE message. When a link is established using this

exchange the nodes start to exchange information to determine if the message

shall be handed over or not.

2.2.2 Beacons-less Routing

Most routing protocols require knowledge of a node’s neighbors to make

their routing decisions. This information is generally gathered by the use of

beacons, messages broadcasted regularly that will be heard by all nodes

within communication distance. Knowledge of your neighbors makes more

informed routing decisions possible, but beacons have their drawbacks. In

[15] Heissenbüttel et al. describe the problems with beacons and present

some remedies. The main problems with beacons are:

• Energy is consumed to transmit, receive and process the beacons.

• The beacons interfere with data transmissions.

• Neighbor information can be inaccurate due to node mobility.

The main problem with inaccurate neighbor information is that transmissions

are attempted to nodes that have moved out of range. These transmissions

will cost a lot of energy and bandwidth. Given these problems with beacons

alternatives to beaconing should be evaluated to see if better solutions can be

found.

There have been several suggestions for beacon-less routing protocols:

• Beacon-less routing (BLR) [14]

• Implicit geographical forwarding (IGF) [1]

• Geographic random forwarding (GeRaF) [57][58]

• Contention-based forwarding (CBF) [11]

• Priority-based stateless geographical routing (PSGR) [52]

• Guaranteed delivery beacon-less forwarding (GDBF) [7].

They all do geographical routing which means that the nodes have to be

location aware and to select the forwarding node they use broadcasts and

timers. Instead of selecting the forwarder from a list of neighbors and sending

the data packet to the selected node, as done by most geographical routing

protocols, they broadcast the data packet or a ready/request to send (RTS) to

all neighbor nodes. All nodes in a defined forwarding area are eligible

forwarders and set timers based on how good they are as forwarders where

the best forwarder gets the shortest time. When a timer expires a node either

21

broadcasts the data packet or a clear to send (CTS). The eligible forwarders

that overhear such a transmission generally abort their timer.

The protocols use one of two transfer principles. They either use a RTS/CTS

exchange followed by an acknowledged point-to-point data message transfer

(IGF, PSGR, GDBF) or the data message is broadcasted and successful

transfer is acknowledged upon previous holder overhearing the forwarder

rebroadcast the message (BLR, CBF). GeRaF does not model on the

transmission layer and can use both principles. The rationale for choosing

either method is not discussed in any of the papers. An argument for using

the RTS/CTS is that the RTS and CTS messages are relatively short and it

makes it possible to transmit the (long) data message using a point-to-point

transfer with reliability mechanisms such as acknowledgements and

resending. An argument for directly sending the data message is that the

RTS/CTS sequence consumes relatively much bandwidth compared to the

data packet due to message spacing times of the MAC protocol. We hope to

be able to investigate this tradeoff in the future under realistic transmission

models where interference and transmission errors are well modeled.

The forwarding area used is often of limited size so that all nodes in the area

can hear the transmissions of all other nodes in the area assuming a constant

radio range. Commonly used shapes of the forwarding area are a 60º circle

sector, a Reuleaux triangle or a circle (see Figure 2a-c). The longest distance

for all the shapes is normally the assumed communication distance. BLR, IGF

and CBF use these types of forwarding areas.

Holder

Sector(a)

Holder

Reuleaux(b)

Holder

Circle(c)

Holder

Progress(d)

Figure 2. Forwarding areas.

In GeRaF, PSGR and GDBF all nodes that provide progress are eligible

forwarders (see Figure 2d). The fact that overhearing between all nodes is not

possible is treated differently by the protocols. GeRaF has not dealt with the

issue since it is only simulated using a high level simulator. GDBF assumes

that overhearing the CTS sent by the forwarder and data packet sent by the

holder is enough. PSGR treats collisions between CTS packets in great detail

and tries to ensure that no collisions between CTS packets occur.

22

The different protocols use different criteria for the characteristics of a good

forwarding node. CBF, GeRaF, PSGR and GDBF all prioritize long steps, that

is the forwarder should be as close to the destination as possible. BLR on the

other hand prioritizes short steps. The reason for this is that BLR alternates

between finding a path (and transmitting the first packet) using a geographic

beacon-less strategy and sending packets through the found path using point-

to-point transfers. If the nodes can adjust their transmission power to the

minimum required to make a reliable transfer then short hops consume less

system bandwidth than long hops. IGF considers both the power available in

the nodes and the progress made. With equal energy nodes closer to the

destination are selected, but as energy is depleted the timer is increased

which means that nodes with low energy are less likely to be selected as

forwarders.

23

3333 Reconnaissance Reconnaissance Reconnaissance Reconnaissance

MobilityMobilityMobilityMobility

Since the performance of routing algorithms will change depending on the

scenario and scenario parameters [21][28][55] it is important to carefully select

a mobility model that represents the environment a routing protocol is

intended to work in. We have chosen to create a mobility model that could

represent how UAVs move when performing reconnaissance of an area.

3.13.13.13.1 ScenarioScenarioScenarioScenario

The main scenario used to evaluate the suggested routing protocols is a

military reconnaissance scan scenario. The objective is to scan an area

regularly using multiple UAVs. Since it is probable that the units on the

ground do not want to be detected by the UAVs there may not be any

apparent pattern to when a UAV will scan a particular area. The same type of

requirements can be found on the searching behavior in the FOPEN (Foliage

Penetration) scenario reported in the work by Parunak et al. [37].

To collect the reconnaissance data the UAVs have a camera directed

downwards covering a rectangular image centered at the UAV. The images

captured are then processed by the UAV. If something of interest is detected

then this information needs to be sent to a unit that can act on the

information. For the routing simulations in chapter 4 we have used four

stationary receivers, but other configurations are conceivable including

mobile receivers.

A fixed wing aircraft is limited in its movement in that it has a minimum and

maximum air speed and that an instantaneous change of direction is not

possible. As we are mainly interested in the behavior of the system of UAVs a

coarse description of the movements of the individual UAVs (as opposed to a

detailed kinematic model) has been used. The UAVs’ movements are

described using a 2D model with fixed speed, constant radius turns, and no

collisions. The reason to use a 2D model is that all UAVs are flying at about

24

the same altitude and there is no need to model start and landing. A fixed

speed is relatively realistic in a reconnaissance scenario. There should be a

speed drop during turns, but the benefit of modeling that is expected to be

minor. The reason to use constant radius turns is that it is much easier to

model, and a more realistic progressive turn model is not expected to add

any major value to the simulation. The reason that collisions do not have to be

modeled is that it is assumed that the UAVs can make altitude adjustments to

avoid collisions. For the scenario we envision the real-world parameters

according to Table 3. The parameters are based on reasonable assumptions

made by domain experts.

Table 3. Scenario parameters

Radio range 8000 m

UAV flight speed 150 km/h (81.0 knots)

UAV flight altitude 3500 m (11 000 feet)

UAV turn radius 500 m

Camera coverage area 2000x1000 m

3.23.23.23.2 Random Mobility ModelRandom Mobility ModelRandom Mobility ModelRandom Mobility Model

As a comparative baseline for the main mobility model described in the next

section we have created a simple random mobility model. The random model

is a Markov process [36] where the UAVs regularly decide whether to go

straight ahead, turn right or turn left. For our simulations this decision is

taken every other second with the probabilities given in Table 4. If a UAV

moves closer than the turn radius to an edge then it turns towards the centre

of the search area until it has reached a randomly chosen direction -45° to 45°

related to the normal of the edge of the search area. Compared to the Gauss-

Markov model in [50][4] this model has no mean direction and the directional

change is given as three discrete values, not a continuous distribution.

Table 4. Random action table.

Probability of action

Last action Turn left Straight ahead

Turn right

Straight ahead 10% 80% 10%

Turn left 70% 30% 0%

Turn right 0% 30% 70%

25

3.33.33.33.3 Distributed Pheromone Repel Mobility Distributed Pheromone Repel Mobility Distributed Pheromone Repel Mobility Distributed Pheromone Repel Mobility

ModelModelModelModel

To produce a mobility control algorithm that is robust and random we have

designed a distributed pheromone repel model. By using pheromones and

localized search the UAVs are guided to areas not recently visited by other

UAVs.

When a UAV moves around it places pheromones on the areas it has scanned

with its camera. Since it is not possible to place these pheromones in the

environment as would be done in a natural system the UAV places them in a

local pheromone map. The pheromone map is a grid where each element

contains a timestamp representing the last time the element was scanned.

Since timestamps are used pheromones placed will slowly fade away. To

share this pheromone information with the other UAVs each UAV regularly

broadcasts a local area pheromone map. All UAVs that receive the broadcast

merge this information into their pheromone map. The broadcast frequency

and size of the map broadcasted needs to be adjusted to limit the bandwidth

required for the transfer of the pheromone information. The actual

parameters used can be found in Table 5.

Table 5. Pheromone map parameters.

Grid element size 100 m

Transfer map size 5000x5000 m

Transfer interval 10 seconds

Figure 3 to Figure 6 illustrate the difference between the local pheromone

map held in a UAV and the total pheromone data present in the system. In

the figures black represents fresh pheromones and white represents no

pheromone information or pheromones older than the time out of one hour.

Also drawn on the maps is the path of the UAVs whose local pheromone map

is shown. The pheromone maps are taken from simulations where the

probability of a successful transfer of pheromone data was set to 50%.

As we will show in the evaluations in section 3.4 there is no real benefit for a

UAV to have access to the global pheromone map. The important thing is to

have a reasonably accurate pheromone map in the UAVs vicinity.

26

Figure 3. Local pheromone map after 3600 seconds of simulation.

Figure 4. Global pheromone view after 3600 seconds of simulation.

27

Figure 5. Local pheromone map after 7200 seconds of simulation.

Figure 6. Global pheromone view after 7200 seconds of simulation.

28

As with the random model a UAV decides to turn left or right or go straight

ahead every other second. But instead of making this decision with fixed

probabilities, the probabilities are based on the pheromone smell in three

evaluation areas. Each area is a circle and they are placed as shown in Figure

7. To get the pheromone smell from the time stamps stored in the pheromone

map the times are scaled so that the current time gives maximum intensity

and the smell is then linearly reduced to zero at the fade away time. The

computation is done according to equation (5) with parameters as defined in

Table 6. The smell zero at time zero is needed to handle elements that have

never been scanned since the elements in the pheromone map are initialized

to zero.

−1000 −500 0 500 1000

−500

0

500

1000

1500

2000

Scan area

CenterRightLeft

UAV

Figure 7. Pheromone search pattern.

Table 6. Pheromone parameter definition.

Parameter Description

sarea Total smell in an area (left, center, right)

stotal sleft + scenter + sright

selement Smell in a pheromone map element

telement Timestamp in a pheromone map element

t Current time

dtime_out Pheromone fade away time

+−−≤

==

=∑

otherwise

0

00

_

_

outtimeelement

outtimeelement

element

element

areaelementarea

dtt

dtt

t

s

ss

(5)

29

Since a UAV should go to places not recently visited it should prefer areas

with a low pheromone smell. For that reason the probability of action is

defined as specified in Table 7. If no pheromone smell is reported for any

direction then a random direction is chosen as in the random model. If the

center and either the left or right has no smell then a random direction is

chosen between these two. The area outside the search area is given a high

pheromone smell (higher than the ordinary full intensity) for the UAVs to

avoid it. A special rule has been added to handle the case when a UAV flies

directly into a corner of the search area. If only guided by the pheromones

then a UAV flying into a corner would get very high smells in the left and

right areas and a low smell in the center area. This would mean that the UAV

would be guided straight into the corner. To counter this problem a UAV

turns right if both the right and left areas have a smell intensity that indicate

that parts of the evaluation area is outside the search area.

Table 7. UAV pheromone action table.

Probability of action

Turn left Straight ahead Turn right

(stotal – sleft) / (2 * stotal)

(stotal – scenter) / (2 * stotal)

(stotal – sright) / (2 * stotal)

3.43.43.43.4 EvaluationEvaluationEvaluationEvaluation

As described in section 3.1 the main objective of the reconnaissance scenario

is to scan all parts of an area regularly. If the area is large and the requirement

of how often each part of the area shall be scanned is low the several UAVs

have to cooperate to perform the scanning. For the evaluations we have set

the requirement that each part of the reconnaissance area should be scanned

at least once every hour. To reflect this the pheromone fade time was set to

one hour for the pheromone mobility model.

The area over which reconnaissance should be performed was set to a 90x90

km square and 90 UAVs were used. Initially all UAVs started from the center

of the south edge moving north. This start will challenge the mobility model

to get the UAVs to spread out over the entire reconnaissance area.

In addition to the two mobility models presented in sections 3.2 and 3.3 the

scenario was also run with the random waypoint mobility model with no

wait times. The reason to include random waypoint is that it is the most

commonly used mobility model in ad-hoc networking research. The mobility

30

models were tested by performing 50 independent runs per model where

each run simulates 3 hours.

To evaluate the robustness of the pheromone logic the distributed pheromone

mobility model was tested with several data transfer probabilities and

compared to an ideal case where a global pheromone map was accessible to

all UAVs. The transfer probabilities used were 100%, 50%, 10% and 0%. This

simulates a range of cases from perfect transmission capability (100%) to the

absence of radio communication (0%). A transfer probability of 0% will mean

that a UAV is only guided by its own pheromones.

To evaluate the main scanning objective we have looked at the scan coverage,

that is the percentage of the area scanned the last hour. A secondary

requirement was to scan in an unpredictable pattern. To evaluate this

property we have studied the probability distribution of the time between

scans. Finally we also looked at the wireless connectivity to determine the

viability for ad hoc routing in the evaluated setting.

3.4.1 Scan Coverage

Initially the UAVs shall scan the area as fast as possible. When the initial scan

is completed the UAVs need to continuously monitor the area by rescanning

every part at least once per hour. The coverage data from the different runs

are presented in Figure 8 to Figure 14. The figures show the average coverage

from the 50 runs and the 95% confidence interval. In Figure 15 the averages

from some of the runs are collected for easier comparison.

The absolute maximum scan speed is 0.083 km²/second/UAV according to

equation (6) and the data from section 3.1. Given the area of 8100 km² and 90

UAVs the fastest time to cover the whole area (which is in practice

impossible) is 18 minutes (1056 seconds). Adding the overhead of turning and

additional requirements like randomness a coverage time of 40 minutes (2400

seconds) should be feasible. Extrapolating the steepest part of the coverage

curve of the pheromone and random waypoint mobility models the coverage

time is a little more than 40 minutes.

Scan speed = UAV speed * scan area width (6)

Comparing Figure 8 and Figure 9 we see that using a global pheromone map

does not give any better results than using a distributed pheromone map.

This means that the UAVs manage to distribute the local information needed

for the mobility decisions. Looking at Figure 10 we see that if only 50% of the

31

transmitted pheromone maps are received then the UAVs still have enough

information to efficiently scan the area. When the transfer probability drops

to 10%, Figure 11, then it takes substantially more time to reach a steady state

and a slightly lower steady state coverage is achieved. But considering the

large loss of information the result is still good.

In Figure 13 we see the problem with not having pheromones that can guide

the nodes to previously uncovered areas. With the random mobility model it

takes much time for the UAVs to become evenly spread out over the area. In

simulations with a smaller area, but with the same node density the random

mobility model achieved a steady state coverage of about 90±8% with a 95%

confidence interval. Adding only local pheromone information as reported in

Figure 12 the coverage improves, but not enough to provide acceptable

results.

In Figure 14 the properties of the random waypoint mobility model becomes

relatively clear. The initial coverage rate is good since the nodes move out

from the start position to different points in the area. The reason that the

coverage stabilizes around 84% is due to the steady state distribution of the

nodes. Since more nodes are present in the central parts of the reconnaissance

area than at the edges the areas close to the edges do not get scanned

regularly enough.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (seconds)

Cov

erag

e

Figure 8. Pheromone mobility coverage with global pheromone map.

32

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Time (seconds)

Cov

erag

e

Figure 9. Pheromone mobility coverage with 100% transfer probability.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.1

0.2

0.3

0.4

0.5

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Time (seconds)

Cov

erag

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Figure 10. Pheromone mobility coverage with 50% transfer probability.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.1

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Figure 11. Pheromone mobility coverage with 10% transfer probability.

33

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.1

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Figure 12. Pheromone mobility coverage with 0% transfer probability.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.1

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Figure 13. Random mobility coverage

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

0.1

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Figure 14. Random Waypoint mobility coverage

34

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

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1

Time (seconds)

Cov

erag

e

Pheromone 100%Pheromone 10%Pheromone 0%Random Waypoint

Figure 15. Comparison of average coverage.

3.4.2 Scan Characteristic

The random and pheromone mobility models have been designed to produce

unpredictable scanning patterns. The randomness of the scanning can be

investigated by looking at the probability distribution of the time between

scans. In Figure 16 to Figure 22 the average probability distributions and the

95% confidence interval of the three models are shown. The area under the

curve between two time points is the probability that the next scan will occur

during this time period after the current scan.

The question is then; what is the desired distribution? To this question there

is no definite answer, but a uniform distribution (the horizontal dashed line

from 0 to 2160 seconds) should be an attractive result. This would mean that

the probability of the next scan is evenly distributed over some time period.

The only firm requirement on the distribution function is that it shall be zero

after one hour to meet the one hour rescan requirement. What is very obvious

from these graphs is that as long as the nodes can exchange some pheromone

information the pheromone model manages quite well to avoid rescanning a

recently scanned area (does not peak for low time between scans). As we saw

in the scan coverage graph no model manages to achieve 100% coverage. This

is here seen by that the function is not zero after one hour.

The limitation of the probability distribution graphs is that they do not

include the areas never scanned or only scanned once. To see the capability of

the models to scan the complete area at least once during the three hours the

maximum, median and minimum uncovered area for the 50 runs are shown

in Table 8. These numbers clearly show the ability of the pheromone model to

35

cover the complete area. The concentration of the nodes to the central parts of

the area by the random waypoint mobility model is exhibited by the

relatively large values for the never scanned area.

Table 8. Never scanned area

Max Median Min

Pheromone global 0.04% 0.01% 0.00%

Pheromone 100% 0.07% 0.01% 0.00%

Pheromone 50% 0.09% 0.01% 0.00%

Pheromone 10% 3.66% 0.18% 0.02%

Pheromone 0% 13.52% 4.73% 1.93%

Random 29.76% 20.50% 13.21%

Random Waypoint 6.79% 5.20% 3.80%

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

5

10

15x 10

−4

Time until next scan (seconds)

Pro

babi

lity

dens

ity

Figure 16. Pheromone mobility with global pheromone map.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

5

10

15x 10

−4

Time until next scan (seconds)

Pro

babi

lity

dens

ity

Figure 17. Pheromone mobility with 100% transfer probability.

36

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

5

10

15x 10

−4

Time until next scan (seconds)

Pro

babi

lity

dens

ity

Figure 18. Pheromone mobility with 50% transfer probability.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

5

10

15x 10

−4

Time until next scan (seconds)

Pro

babi

lity

dens

ity

Figure 19. Pheromone mobility with 10% transfer probability.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

5

10

15x 10

−4

Time until next scan (seconds)

Pro

babi

lity

dens

ity

Figure 20. Pheromone mobility with 0% transfer probability.

37

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

5

10

15x 10

−4

Time until next scan (seconds)

Pro

babi

lity

dens

ity

Figure 21. Random mobility.

0 500 1000 1500 2000 2500 3000 3500 4000 4500 5000

0

5

10

15x 10

−4

Time until next scan (seconds)

Pro

babi

lity

dens

ity

Figure 22. Random Waypoint mobility.

3.4.3 Communication

Reconnaissance data has no value if it cannot be transmitted to where it is

needed and nodes can only transmit data between each other if they are

within radio range. With the used node density of 0.011 nodes/km²

communication would be impossible if the nodes were evenly distributed in a

regular grid since they then would have a distance of about 9.5 km to their

closest neighbor which is larger than the radio range of 8 km.

In Figure 23 to Figure 29 the average number of partitions and 95%

confidence interval for the three mobility models are illustrated. A partition

consists of the UAVs that can pair-wise establish a contemporaneous path

with each other, either directly or via peers.

38

In Figure 23 to Figure 26 we see that the pheromone mobility manages to

spread the nodes well since we have a high number of partitions. With the

low (10%) transfer probability it just takes some more time to reach the

spread. In Figure 28 we again observe that the UAVs moving under the

random mobility model have not yet reached a steady state. The same

conclusion is also valid for nodes moving under the pheromone mobility

model but where no pheromone data is exchanged between the nodes (see

Figure 27). For the random waypoint mobility model the increased density of

nodes in the central parts of the simulation area leads to a lower number of

partitions compared to the pheromone mobility model as shown in Figure 29.

With an average of about 30 partitions for configurations with good coverage

the existence of a contemporaneous path between two random nodes is not

likely. To be able to send the information to any selected peer delay-tolerant

routing principles need to be used. This is the topic of the next chapter where

delay-tolerant routing on nodes that move under the pheromone mobility

model is further studied.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

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Figure 23. Pheromone mobility with global pheromone map.

39

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

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Figure 24. Pheromone mobility with 100% transfer probability.

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Figure 25. Pheromone mobility with 50% transfer probability.

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

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Par

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Figure 26. Pheromone mobility with 10% transfer probability.

40

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000

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Figure 27. Pheromone mobility with 0% transfer probability.

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Figure 28. Random mobility.

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Figure 29. Random Waypoint mobility.

41

4444 RoutingRoutingRoutingRouting in DTNs in DTNs in DTNs in DTNs

Routing in opportunistic DTNs is challenging since it is generally not possible

to establish a contemporaneous path between source and destination. Using

the store-carry-forward principle it is possible to transfer messages between

nodes by exploiting node mobility. The main difficulty is then to decide when

and to whom to forward a message that a node is carrying.

In this chapter we describe and evaluate our proposed routing algorithm

LAROD (location aware routing for opportunistic delay-tolerant networks).

LAROD is evaluated against two other routing protocols described in

sections 4.2 and 4.3.

4.14.14.14.1 LARODLARODLARODLAROD

LAROD is a geographical routing protocol for DTNs that combines the

geographical beacon-less routing with the store-carry-forward principle. In its

essence LAROD uses greedy packet forwarding when possible. When greedy

forwarding is not possible the node holding the packet (the custodian) waits

until node mobility makes it possible to resume greedy forwarding.

To reduce system wide overhead LAROD uses the same basic beacon-less

routing strategy as BLR [14] and CBF [11] (see section 2.2.2). Instead of

forwarding a packet to a neighbor based on location data received from

beacons a custodian simply broadcasts a message when it wants to transmit

it. The “best” forwarding node receiving the message then forwards the

message in the same manner. The custodian that sent the message will

overhear this transmission and conclude that the forwarder has taken over

custody of the packet. If no such transmission is heard the node regularly

broadcasts the message until a forwarder becomes available due to node

mobility.

42

Custodian Destination

Forwarding areaCircle

(a)

Custodian Destination

Forwarding areaProgress

(b)

Figure 30. LAROD forwarding areas.

To select the “best” node to forward a message among all nodes receiving it a

forwarding area and timer delay function is used. The nodes that are eligible

to forward a packet are determined by a forwarding area related to the node

performing the broadcast. In [14] Heissenbüttel et al. recommended a circle as

the best forwarding area (see Figure 30a). Our simulations have shown that

this area is too small due to the low density of nodes we use. Instead all

nodes that provide some minimum progress (dprogress) towards the destination

are eligible as forwarders (see Figure 30b). An implication of using this larger

forwarding area is that multiple copies of a packet can be active since all

nodes in the forwarding area might not hear the broadcast made by the best

forwarder. This should not be seen as a problem, since it enables exploration

of multiple paths to the destination.

All nodes receiving the packet in the forwarding area are tentative

forwarders and to select the best forwarder they all set a delay timer when to

rebroadcast the packet. The node with the timer that expires first will

rebroadcast the packet. This broadcast will be heard by the old custodian and

most of the other tentative forwarders. Overhearing the transmission they

will relinquish custody of the packet. Since the forwarding area chosen is

larger than the transmission range it is possible that several nodes take over

custody of the packet.

The delay function that computes the actual delay (tdelay) is defined in equation

(7) with the equation parameters defined in 0. The vectors and distances used

in the equation are illustrated in Figure 31. Depending on the relative position

of the receiver, custodian and destination the delay is computed based on one

of three cases. The delay for all three cases has a linear component and a

random component. When the custodian is so close to the destination that the

destination should have received the packet all tentative forwarders calculate

the linear delay based on the distance between the tentative forwarder and

the destination (first row in equation (7)). In all other cases the delay is based

on the distance from the custodian along the line connecting the custodian

43

and destination. Nodes not on the line get their distance by perpendicularly

projecting their position on to the line. Nodes with a computed distance of

less than dzero have a linear component with a maximum delay (tlmax) at the

custodian and zero delay at dzero (last row in equation (7)). The dzero distance

should be set close to the expected radio range. For nodes with a distance of

more than dzero the linear component is zero (line two in equation (7)). The

reason to have this case is that due to radio propagation factors nodes further

away than the expected radio range can receive a packet. To prevent

simultaneous transmission from nodes whose linear delay is zero all cases

add a small random delay (0 to trmax) to the computed linear delay. trmax should

be set much smaller than tlmax to make the impact of the random factor small

on the linear factor. To be able to calculate the delays all LAROD data packets

contain the position of the source, destination and last forwarder (custodian).

For BLR Heissenbüttel et al. recommended the inverse function, that is short

steps. This was done to be able to transmit at low power in the unicast phase

to increase total system bandwidth. Since there is no unicast phase in LAROD

it exploits the benefits of long steps.

In the case that the custodian does not hear a rebroadcast it will perform a

new broadcast after some time (trebroadcast). To stop transmission when a packet

has reached its destination the destination broadcasts an acknowledgement

packet. This packet is rebroadcasted by all custodians and tentative

custodians hearing it will stop their retransmissions. All nodes hearing an

acknowledgement packet will store the packet until the packet times out (tTTL).

If a node receives a packet for which it previously has received an

acknowledgement then it broadcasts an acknowledgement to stop the

transmission of the packet.

To prevent that a packet indefinitely tries to find a path to its destination all

packets have a time to live (TTL) expressed as a duration (tTTL). When the TTL

expires a packet is deleted by its custodian. For a full description of the

routing protocol see Figure 32.

LAROD is based on the principle that the custody of a packet is moved to the

most recent node that accepted the packet. This means that the protocol is not

robust to node loss. If a node fails for some reason then all packets that the

node holds will be lost unless they happened to be duplicated due to

diverging paths.

44

Table 9. LAROD parameter definitions.

Parameter Description

tdelay Delay until node shall broadcast received message.

tlmax Maximum linear delay.

trmax Maximum random delay.

trebroadcast Delay until node shall rebroadcast a message for which it has not overheard a transmission by a forwarder.

tTTL Maximum time a packet is permitted to live.

dzero Distance from custodian to have zero linear delay.

dprogress Minimum progress required to be an eligible forwarder.

nr

Vector from sender to potential custodian.

dr

Vector from sender to destination.

rr

Vector from potential custodian to destination.

X Uniform random variable in range [0, 1].

⋅Χ+⋅−

≥⋅Χ

≤⋅Χ+⋅

=

otherwiseproj

proj

rmaxlmaxzero

zero

zerormax

zerormaxlmaxzero

delay

ttd

dnd

ddnt

ddttd

r

trr

rr

rr

(7)

Custodian

Receiver

Destination

dzero

dprogress

→d

→n →r

→ →n proj d

Figure 31. Illustration of vectors used for delay time computations.

45

Source node at data packet generation Broadcast data packet Set up timer for rebroadcasting packet to trebroadcast

Destination node at data packet reception The packet is received for the first time Deliver data packet to application Broadcast ack packet A duplicate data packet is received Broadcast ack packet

All intermediate (non-destination) nodes at data packet reception If an ack has been received for the packet Broadcast ack packet Else if the node is in the forwarding area If the node does not have an active 3 copy of the packet Set up timer for rebroadcast to tdelay If the node has an active copy of the packet Do nothing Else Remove active copy if it has one

At ack packet reception If the node has an active copy of the packet Remove active copy Broadcast ack packet Else Do nothing

When a data packet rebroadcasting timer expires If the packet’s TTL has expired ( tTTL) Remove packet Else Broadcast data packet Set up timer for rebroadcasting the packet to trebroadcast

Figure 32. LAROD pseudo code.

3 A node has an active copy of a packet if it has a copy of the packet waiting on a timer to be

rebroadcasted.

46

4.24.24.24.2 Broadcast DelayBroadcast DelayBroadcast DelayBroadcast Delay----tolerant Routing tolerant Routing tolerant Routing tolerant Routing

(BDTR)(BDTR)(BDTR)(BDTR)

To be able to estimate the delivery rate of a delay-tolerant routing protocol

which searches all possible paths to a destination we propose BDTR. BDTR is

a flooding based routing protocol that limits the flooding to an area centered

round the source and destination. In contrast to epidemic routing [51] it does

not use beacons to find its neighbors. Instead it uses the same principle as

LAROD does, when it has something to send is simply broadcasts the

message. All nodes within the flooding area that receive the message will

rebroadcast it after a short random delay. By this mechanism all nodes in the

partition of the source node should quickly receive a copy of the message and

if there is a contemporaneous path to the destination in the broadcast area the

packet will soon reach the destination. To overcome partitions all nodes in the

flooding area regularly rebroadcast the data packet. When the packet reaches

the destination an acknowledgment packet is sent and it is in turn

rebroadcasted by all nodes that have received the data packet. As for LAROD

a node will store the acknowledgement packet until the data packet times out

(tTTL) and if a node receives a data packet for which it has received an

acknowledgement packet it will broadcast an acknowledgement packet. For a

full description of the routing protocol and the parameters see Figure 33 and

Table 10.

Table 10. BDTR parameter definitions.

Parameter Description

tdelay Delay until node shall broadcast received message.

trebroadcast Delay until node shall rebroadcast a message for which it has not overheard a transmission by a forwarder.

tTTL Maximum time a packet is permitted to live.

For the simulations the flooding area used was a circle with a diameter

slightly larger than the distance between source and destination. This area

means that not all possible paths are searched, but it gives a good balance

between search space size and limiting the overhead. Tests with the complete

simulation area as broadcast area have shown that the overhead was

prohibitive and that it affected the ability to search all paths even at the low

loads used in our simulations.

47

BDTR is a simple protocol and robust in the sense that several nodes have an

individual packet and try to forward it to the destination. This means that

failure of a single node does not impact the routing to a great degree. The

rebroadcast and resend strategies used by BDTR means that it will quickly

saturate the wireless medium in dense networks or networks that transmit

large amounts of data. This means that BDTR by design in not suited for

these environments.

Source node at data packet generation Broadcast data packet Set up timer for rebroadcasting packet to trebroadcast

Destination node at data packet reception The packet is received for the first time Deliver data packet to application Broadcast ack packet A duplicate data packet is received Broadcast ack packet

All intermediate (non-destination) nodes at data packet reception If an ack has been received for the packet Broadcast ack packet Else if the node is in the flooding area If the node does not have an active 4 copy Set up timer for initial rebroadcast to tdelay If the node has an active copy Do nothing Else Do nothing

At ack packet reception If the node has an active copy of the packet Remove active copy Broadcast ack packet Else Do nothing

When a data packet rebroadcasting timer expires If the packet’s TTL has expired ( tTTL) Remove packet Else Broadcast data packet Set up timer for rebroadcasting the packet to trebroadcast

Figure 33. BDTR pseudo code.

4 A node has an active copy of a packet if it has a copy of the packet waiting on a timer to be

rebroadcasted.

48

4.34.34.34.3 Broadcast Routing (BR)Broadcast Routing (BR)Broadcast Routing (BR)Broadcast Routing (BR)

To evaluate how often a contemporaneous path exists between a source-

destination pair a simple broadcast routing scheme was used. BR is identical

to BDTR except that the nodes do not continuously resend packets, they are

only re-broadcasted once. For BR we have used the entire simulation area as

broadcast area.

4.44.44.44.4 EvaluationEvaluationEvaluationEvaluation

The above routing protocols have been implemented in a modified version of

ns2 2.30 [34]. The main changes to the simulator are:

(1) addition of the main algorithm LAROD, and the new baselines (BDTR

and its instance BR),

(2) addition of the pheromone repel mobility model,

(3) support for application level broadcasts,

(4) an application to generate and consume events.

As wireless communication technology we use IEEE 802.11 as implemented

in ns2 2.30 with default parameters using the two-ray ground radio

propagation model.

For the evaluation we use the scenario described in section 3.1 but we have

scaled the parameters to match the default radio range in ns-2. In Table 11 the

parameters are given both as the values used in the simulations and what

they correspond to in the scale used in section 3. The scale change makes

comparison with other studies easier since they generally use ns-2 with

default parameters. When scanning the area a UAV flies over it may detect

something that needs to be communicated to a ground unit that can act on it.

In the current simulations we have had four stationary ground units. The

generation of detect events and selection of receiver are both random

functions.

For the routing experiments we are interested in the performance when the

nodes are at the mobility model’s steady state. For this reason the UAVs are

initially randomly distributed over the reconnaissance area. The simulation is

then run for 1 hour without detect-event packets being generated to populate

the pheromone repel model with data. The detect events are then enabled for

1 hour and the simulation ends after all detect-event packets have timed out.

In the evaluations each data point is the average result of ten simulations.

49

Table 11. Scenario parameters

Parameter ns-2 Real-world

Reconnaissance area 2000x2000 meters 64x64 kilometers

Placement of ground units Center of each quadrant

Node density 10-30 (20) nodes/km2 0.010-0.029 (0.020) nodes/km2

UAV speed 0.8-2.0 (1.4) m/s 92-230 (161) km/h

Packet time to live (tTTL

) 200-600 (400) seconds

Detect data generation rate 12-120 (36) pkt/hour/UAV

Radio range 250 meters 8000 meters

The node speed used in our simulations might seem low compared to the

node speeds in other MANET papers [24][27], but it is actually close to the

average speed used in most simulations. The speed presented in most papers

is the top speed for the random waypoint mobility model. According to the

equations in [33] for a top speed of 20 m/s and minimum speed of 0.1 m/s

with no pause time 49.8% of the nodes would on average move slower than

1.4 m/s. Adding the fact that most simulations use pause times this means

that they have large number of relatively stable nodes that can be used to

create stable routes.

In Table 12 and Table 13 the parameter values used for LAROD and BDTR

are given. BR uses the same initial rebroadcast delay as BDTR. For values

where a range is given the actual value used is randomized in the range using

a rectangular distribution.

Table 12. LAROD parameter values.

Parameter Value

tlmax 1.0 seconds trmax 0.2 seconds trebroadcast 8.0-12.0 seconds dzero 240 meters dprogress 10 meters

Table 13. BDTR parameter values.

Parameter Value

tdelay 0.8-1.2 seconds trebroadcast 8.0-12.0 seconds Flooding area diameter Source-destination distance + 100 meters

50

The goal of the evaluations is to measure the ability of LAROD to efficiently

and reliably perform routing in a mobile and intermittently connected

network. We have measured the delivery ratio (proportion of packets

delivered to the destination) and overhead (average number of packet

transmissions per generated data packet) for the different protocols while

changing the node density, node speed, packet life time and network load. To

gain further insight in the delivery properties we also study the accumulated

delivery ratio against delivery delay for some selected data points.

4.4.1 Node density

The higher the node density the more connected the network will be and the

easier it is to find a path between two nodes. As can be seen from the delivery

ratio of BR in Figure 34 the node density has a dramatic impact on the

existence of contemporaneous paths between source and destination. By

using store-carry-forward both LAROD and BDTR can overcome

communication gaps and they significantly improve the delivery ratio

compared to BR.

Even if LAROD and BDTR give about the same delivery ratio the overhead of

BDTR is much higher as can be seen in Figure 35. At low node densities a

BDTR packet is only receive by a few nodes since fewer nodes are in the

broadcast area. Additionally the clusters of connected nodes (partitions) will

contain fewer nodes. Both these factors result in fewer transmissions

generated at low node densities. As the node density increases more nodes

will receive a BDTR packet and the number of transmissions increases. The

reason the transmissions fall at higher node densities is that packets will be

delivered faster to the destination due to increased connectivity. This means

that the packet will live a shorter time in the network, reducing the number of

transmissions. For LAROD increased density means that fewer rebroadcasts

are required to overcome partitions and thus the overhead is lower for higher

node densities.

Studying the delay curves in Figure 36 we see that even with similar eventual

delivery ratio LAROD and BDTR have different delay properties. For higher

node densities the wider search of BDTR results in lower average delivery

delay since BDTR finds paths that LAROD does not. At low node densities,

on the other hand, there are so few paths available that both BDTR and

LAROD seem to find similar paths.

51

10 12 14 16 18 20 22 24 26 28 300

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Node density (nodes/km2)

Del

iver

y ra

tio

LAROD progressBDTR circleBR all

Figure 34. Delivery ratio for different node densities.

10 12 14 16 18 20 22 24 26 28 300

50

100

150

200

250

300

Node density (nodes/km2)

Tra

nsm

issi

ons

per

data

pac

ket

LAROD progressBDTR circleBR all

Figure 35. Overhead for different node densities.

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Delay (s)

Del

iver

y ra

tio

LAROD 30 nodes/km2

LAROD 20 nodes/km2

LAROD 10 nodes/km2

BDTR 30 nodes/km2

BDTR 20 nodes/km2

BDTR 10 nodes/km2

Figure 36. Delay for different node densities.

52

4.4.2 Node speed

A higher node speed should give a higher delivery ratio since the increased

mobility will change the network topology at a higher pace enabling

messages to bridge communication gaps faster. In Figure 37 we see that this is

the case for both LAROD and BDTR. It is interesting to note that the delivery

ratio for BR increases somewhat with higher mobility. This probably comes

from the fact that BR has a short random delay between message reception

and the resending of a packet. During this time the nodes will move making

it possible to reach nodes that were not reachable at packet reception.

In Figure 38 we see that increased node speed decreases the overhead for

BDTR and LAROD. This is a natural consequence of the improved delivery

ratio at higher node speeds. Since most packets will be delivered before their

expiry time fewer packets will be active in the network at higher delivery

ratios and thereby reducing the number of rebroadcasts.

Looking at the delay curves for LAROD and BDTR in Figure 39 we see that

LAROD essentially has the same delivery ratio independent of speed after 10

seconds. This is essentially the delivery of packets to nodes with a

contemporaneous path from the source. After that node mobility is required

to close partition gaps which is done faster at higher speeds. For BDTR the

initial delivery ratio (first 10 seconds) is significantly affected by the node

speed. The reason is probably that with higher node speeds nodes receiving a

packet do scatter a bit more before a packet is rebroadcasted than with lower

speeds resulting in less interference. As for the node density DBTR has a

slightly better delivery ratio due to that more paths are searched.

0.8 1 1.2 1.4 1.6 1.8 20

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Node speed (m/s)

Del

iver

y ra

tio

LAROD progressBDTR circleBR all

Figure 37. Delivery ratio for different node speeds.

53

0.8 1 1.2 1.4 1.6 1.8 20

50

100

150

200

250

300

Node speed (m/s)

Tra

nsm

issi

ons

per

data

pac

ket

LAROD progressBDTR circleBR all

Figure 38. Overhead for different node speeds.

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Delay (s)

Del

iver

y ra

tio

LAROD 2.0 m/sLAROD 1.4 m/sLAROD 0.8 m/sBDTR 2.0 m/sBDTR 1.4 m/sBDTR 0.8 m/s

Figure 39. Delay for different node speeds.

4.4.3 Time to live

Increasing packet life time should increase the delivery ratio since more time

is allowed for node mobility to bridge network voids. A longer life time will

on the other hand also mean that the overhead will increase since more

rebroadcasts will occur due to that packets will try to reach the destination

for a longer time.

In Figure 40 we see the expected delivery rate improvement for both LAROD

and BDTR. Since BR is not affected by packet life time the delivery rate is

basically constant. In Figure 41 we see a significant increase in the overhead

for BDTR as the packet life time is increased. The overhead using LAROD

also increases, but at a much lower rate.

54

Studying the accumulated delivery ratio related to delivery delay LAROD

have essentially identical curved independent of time to live (TTL) value. For

BDTR the impact of the higher network load associated with a high TTL

value is obvious in Figure 42. This means that even if the eventual delivery

ratio is higher for a TTL of 600 seconds than 200 seconds the time to deliver a

packet will generally be longer.

As TTL is a tunable protocol parameter the question is which value to choose.

For data with a clear freshness requirement the TTL should match the

requirement if it does not result in an unacceptably high network load. For

other types of data the main limitation on a high TTL is acceptable network

load and resource consumption to deliver a packet.

200 250 300 350 400 450 500 550 6000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Packet life time (s)

Del

iver

y ra

tio

LAROD progressBDTR circleBR all

Figure 40. Delivery ratio for different packet life times.

200 250 300 350 400 450 500 550 6000

50

100

150

200

250

300

350

Packet life time (s)

Tra

nsm

issi

ons

per

data

pac

ket

LAROD progressBDTR circleBR all

Figure 41. Overhead for different packet life times.

55

0 100 200 300 400 500 6000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Delay (s)

Del

iver

y ra

tio

BDTR 200 sBDTR 400 sBDTR 600 s

Figure 42. Delay for different packet life times.

4.4.4 Network load

To investigate the scalability of the routing protocols when it comes to

network load different event generation rates were tested. Ideally a fully

scalable protocol will give the same result independent of network load. In

Figure 43 we see that the delivery ratio of BDTR drops as the load increases

while it stays constant for LAROD. The problem for BDTR is that the network

becomes congested and that several transmissions fail due to collisions. This

clearly shows that the broadcast strategy of BDTR has scalability issues. For

LAROD the critical load level is not yet reached and as a result the delivery

ratio is stable. In Figure 44 we see the consequence of the lower delivery ratio

on the load for BDTR. As the delivery ratio decreases and delivery times

increases the packets live longer in the network and as a result the per packet

overhead increases.

As LAROD has not reach a point where congestion becomes a problem all

LAROD delay curves are essentially identical to the 20 nodes/km² curve in

Figure 36. For BDRT on the other hand the effect of congestion is very

obvious in the delay curves in Figure 45. As the load increases more collisions

will occur and delivery takes more time.

56

20 40 60 80 100 1200

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Load (pkt/hour/UAV)

Del

iver

y ra

tio

LAROD progressBDTR circleBR all

Figure 43. Delivery ratio for different network loads.

20 40 60 80 100 1200

50

100

150

200

250

300

350

Load (pkt/hour/UAV)

Tra

nsm

issi

ons

per

data

pac

ket

LAROD progressBDTR circleBR all

Figure 44. Overhead for different network loads.

0 50 100 150 200 250 300 350 4000

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1

Packet delivery latency (s)

Del

iver

y ra

tio

BDTR 12 packets/hour/UAVBDTR 72 packets/hour/UAVBDTR 120 packets/hour/UAV

Figure 45. Delay for different network loads.

57

5555 ConclusiConclusiConclusiConclusionsonsonsons and Future and Future and Future and Future

WorkWorkWorkWork

5.15.15.15.1 ConclusionsConclusionsConclusionsConclusions

Routing in mobile ad hoc networks is challenging since the network layout is

constantly changing and constant distribution of the current network layout

to all nodes in the network is generally too costly. For this reason

geographical routing protocols are generally well suited for MANETS since

they are essentially stateless. The ability to use geographical routing does rely

on the nodes being location aware and that the (approximate) location of a

destination is known.

Depending on node density and node movement groups of nodes may be

separated from each other so that communication between nodes in different

partitions using contemporaneous paths is not possible. Due to node mobility

communication is still feasible by utilizing the store-carry-forward principle.

When message progress is not possible by wireless transfer the message is

stored in a suitable node until node mobility provides opportunities for

further wireless progress.

In this thesis we have studied communication in a reconnaissance scenario in

which a group of UAVs cooperatively shall detect units on the ground. To

guide them in a distributed and robust way we have designed a distributed

pheromone model where the UAVs are guided by virtual pheromones. This

model has been shown to provide good search coverage even when

communication between UAVs is poor. What the model also showed was that

with the assumed system parameters the connectivity between the UAVs was

low. This means that regular MANET routing protocols do not work since

they assume the existence of contemporaneous paths.

Since the UAVs are location aware we have explored the feasibility of

combining greedy geographical routing with the store-carry-forward

principle. To avoid position inaccuracies introduced by beacon based

58

geographical protocols we used the ideas from beacon-less geographical

routing protocols from MANET research and designed a routing protocol for

intermittently connected networks, LAROD. Our simulations have shown

that the delivery rate of LAROD is close to the delivery rate of an epidemic

routing protocol under the overload limit, that is the best possible result

achievable. Moreover, we have shown that the overheads measured in terms

of transmissions are considerably lower even under high load scenarios.

5.25.25.25.2 Future WorkFuture WorkFuture WorkFuture Work

A main constraint in our work is that we have assumed that the destinations

are static and that their positions are known to all sources. We wish to

remove this limitation by introducing a distributed location service in the

network. By this service the nodes can acquire a position or position estimate

of a node. Due to propagation delays, especially noticeable in intermittently

connected networks, the geographical routing needs to be able to handle

incorrect position information. One option could be to dynamically update

the positions in the packets as more accurate information of node location is

available at forwarders closer to the destination.

Another aspect we want to explore is how a UAV determines the nodes that

are interested in the intelligence it gathers. By some kind of publish subscribe

system the destinations should be able to inform UAVs of the data they are

interested in.

On the radio level we wish to explore the difference between broadcast

transmissions as used by LAROD and CTS/RTS with reliable data transfer.

What we are interested in exploring is how the reliable data transfer in the

CTS/RTS scheme affects the transfer probability. To properly explore the

tradeoffs the currently used two ray-ground radio model probably needs to

be replaced by a more realistic model like Nakagami [31] or log-normal

shadowing [40].

To become widely accepted a routing protocol should work well in a wide

range of environments. For this reason we intend to evaluate our suggested

routing algorithms under several different mobility models. At the same time

we will also continue to develop our scenario to become as realistic as

reasonably possible.

59

6666 AcronymsAcronymsAcronymsAcronyms

Acronym Description

AODV Ad hoc on-demand distance vector routing BDTR Broadcast delay-tolerant routing BLR Beacon-less routing BR Broadcast routing CAR Context-aware routing CBF Contention-based forwarding CTS Clear to send DSR Dynamic source routing DTGR Disruption-tolerant geographic routing for wireless ad hoc

networks DTN Delay-tolerant network, Disruption-tolerant network ER Epidemic routing FOPEN Foliage penetration GDBF Guaranteed delivery beacon-less forwarding GeRaF Geographic random forwarding GPS Global positioning system GPSR Greedy perimeter stateless routing GSM Global system for mobile communications ICT Inter contact time IEEE Institute of electrical and electronics engineers IETF Internet Engineering Task Force IGF Implicit geographical forwarding LAROD Location aware routing for delay-tolerant networks OLSR Optimized link state routing MANET Mobile ad-hoc network MoVe Motion vector PREP Prioritized epidemic routing PSGR Priority-based geographical forwarding RFC Request for comment RTS Ready/request to send TBRPF Topology dissemination based on reverse-path forwarding TTL Time to live UAV Unmanned aerial vehicle

60

61

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LINKÖPINGS UNIVERSITETLINKÖPINGS UNIVERSITETLINKÖPINGS UNIVERSITETLINKÖPINGS UNIVERSITET

Rapporttyp Report category

Licentiatavhandling

Examensarbete

C-uppsats

D-uppsats

Övrig rapport

Språk Language

Svenska/Swedish

Engelska/English

Titel Title

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Linköping Studies in Science and Technology

Thesis No. 1356

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2008-04-14

Avdelning, institution Division, department

Institutionen för datavetenskap

Department of Computer and Information Science

Mobility and Routing in a Delay-tolerant Network of Unmanned Aerial Vehicles

Erik Kuiper

Technology has reached a point where it has become feasible to develop unmanned aerial vehicles (UAVs), that is aircraft without a human pilot on board. Given that future UAVs can be autonomous and cheap, applications of swarming UAVs are possible. In this thesis we have studied a reconnaissance application using swarming UAVs and how these UAVs can communicate the reconnaissance data. To guide the UAVs in their reconnaissance mission we have proposed a pheromone based mobility model that in a distributed manner guides the UAVs to areas not recently visited. Each UAV has a local pheromone map that it updates based on its reconnaissance scans. The information in the local map is regularly shared with a UAV’s neighbors. Evaluations have shown that the pheromone logic is very good at guiding the UAVs in their cooperative reconnaissance mission in a distributed manner. Analyzing the connectivity of the UAVs we found that they were heavily partitioned which meant that contemporaneous communication paths generally were not possible to establish. This means that traditional mobile ad hoc network (MANET) routing protocols like AODV, DSR and GPSR will generally fail. By using node mobility and the store-carry-forward principle of delay-tolerant routing the transfer of messages between nodes is still possible. In this thesis we propose location aware routing for delay-tolerant networks (LAROD). LAROD is a beacon-less geographical routing protocol for intermittently connected mobile ad hoc networks. Using static destinations we have shown by a comparative study that LAROD has almost as good delivery rate as an epidemic routing scheme, but at a substantially lower overhead.

mobility models, routing, ad hoc networks, delay-tolerant networks

978-91-7393-937-9

0280-7971

LiU-Tek-Lic- 2008:14

Department of Computer and Information ScienceLinköpings universitet

Linköping Studies in Science and TechnologyFaculty of Arts and Sciences - Licentiate Theses

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Bases, 1991.No 298 Rolf G Larsson: Aktivitetsbaserad kalkylering i ett nytt ekonomisystem, 1991.No 318 Lena Srömbäck: Studies in Extended Unification-Based Formalism for Linguistic Description: An Algorithm

for Feature Structures with Disjunction and a Proposal for Flexible Systems, 1992.No 319 Mikael Pettersson: DML-A Language and System for the Generation of Efficient Compilers from Denotatio-

nal Specification, 1992.No 326 Andreas Kågedal: Logic Programming with External Procedures: an Implementation, 1992.No 328 Patrick Lambrix: Aspects of Version Management of Composite Objects, 1992.No 333 Xinli Gu: Testability Analysis and Improvement in High-Level Synthesis Systems, 1992.No 335 Torbjörn Näslund: On the Role of Evaluations in Iterative Development of Managerial Support Sytems,

1992.No 348 Ulf Cederling: Industrial Software Development - a Case Study, 1992.No 352 Magnus Morin: Predictable Cyclic Computations in Autonomous Systems: A Computational Model and Im-

plementation, 1992.No 371 Mehran Noghabai: Evaluation of Strategic Investments in Information Technology, 1993.No 378 Mats Larsson: A Transformational Approach to Formal Digital System Design, 1993.No 380 Johan Ringström: Compiler Generation for Parallel Languages from Denotational Specifications, 1993.No 381 Michael Jansson: Propagation of Change in an Intelligent Information System, 1993.No 383 Jonni Harrius: An Architecture and a Knowledge Representation Model for Expert Critiquing Systems, 1993.No 386 Per Österling: Symbolic Modelling of the Dynamic Environments of Autonomous Agents, 1993.No 398 Johan Boye: Dependency-based Groudness Analysis of Functional Logic Programs, 1993.

No 402 Lars Degerstedt: Tabulated Resolution for Well Founded Semantics, 1993.No 406 Anna Moberg: Satellitkontor - en studie av kommunikationsmönster vid arbete på distans, 1993.No 414 Peter Carlsson: Separation av företagsledning och finansiering - fallstudier av företagsledarutköp ur ett agent-

teoretiskt perspektiv, 1994.No 417 Camilla Sjöström: Revision och lagreglering - ett historiskt perspektiv, 1994.No 436 Cecilia Sjöberg: Voices in Design: Argumentation in Participatory Development, 1994.No 437 Lars Viklund: Contributions to a High-level Programming Environment for a Scientific Computing, 1994.No 440 Peter Loborg: Error Recovery Support in Manufacturing Control Systems, 1994.FHS 3/94 Owen Eriksson: Informationssystem med verksamhetskvalitet - utvärdering baserat på ett verksamhetsinrik-

tat och samskapande perspektiv, 1994.FHS 4/94 Karin Pettersson: Informationssystemstrukturering, ansvarsfördelning och användarinflytande - En kompa-

rativ studie med utgångspunkt i två informationssystemstrategier, 1994.No 441 Lars Poignant: Informationsteknologi och företagsetablering - Effekter på produktivitet och region, 1994.No 446 Gustav Fahl: Object Views of Relational Data in Multidatabase Systems, 1994.No 450 Henrik Nilsson: A Declarative Approach to Debugging for Lazy Functional Languages, 1994.No 451 Jonas Lind: Creditor - Firm Relations: an Interdisciplinary Analysis, 1994.No 452 Martin Sköld: Active Rules based on Object Relational Queries - Efficient Change Monitoring Techniques,

1994.No 455 Pär Carlshamre: A Collaborative Approach to Usability Engineering: Technical Communicators and System

Developers in Usability-Oriented Systems Development, 1994.FHS 5/94 Stefan Cronholm: Varför CASE-verktyg i systemutveckling? - En motiv- och konsekvensstudie avseende ar-

betssätt och arbetsformer, 1994.No 462 Mikael Lindvall: A Study of Traceability in Object-Oriented Systems Development, 1994.No 463 Fredrik Nilsson: Strategi och ekonomisk styrning - En studie av Sandviks förvärv av Bahco Verktyg, 1994.No 464 Hans Olsén: Collage Induction: Proving Properties of Logic Programs by Program Synthesis, 1994.No 469 Lars Karlsson: Specification and Synthesis of Plans Using the Features and Fluents Framework, 1995.No 473 Ulf Söderman: On Conceptual Modelling of Mode Switching Systems, 1995.No 475 Choong-ho Yi: Reasoning about Concurrent Actions in the Trajectory Semantics, 1995.No 476 Bo Lagerström: Successiv resultatavräkning av pågående arbeten. - Fallstudier i tre byggföretag, 1995.No 478 Peter Jonsson: Complexity of State-Variable Planning under Structural Restrictions, 1995.FHS 7/95 Anders Avdic: Arbetsintegrerad systemutveckling med kalkylkprogram, 1995.No 482 Eva L Ragnemalm: Towards Student Modelling through Collaborative Dialogue with a Learning Compani-

on, 1995.No 488 Eva Toller: Contributions to Parallel Multiparadigm Languages: Combining Object-Oriented and Rule-Based

Programming, 1995.No 489 Erik Stoy: A Petri Net Based Unified Representation for Hardware/Software Co-Design, 1995.No 497 Johan Herber: Environment Support for Building Structured Mathematical Models, 1995.No 498 Stefan Svenberg: Structure-Driven Derivation of Inter-Lingual Functor-Argument Trees for Multi-Lingual

Generation, 1995.No 503 Hee-Cheol Kim: Prediction and Postdiction under Uncertainty, 1995.FHS 8/95 Dan Fristedt: Metoder i användning - mot förbättring av systemutveckling genom situationell metodkunskap

och metodanalys, 1995.FHS 9/95 Malin Bergvall: Systemförvaltning i praktiken - en kvalitativ studie avseende centrala begrepp, aktiviteter och

ansvarsroller, 1995.No 513 Joachim Karlsson: Towards a Strategy for Software Requirements Selection, 1995.No 517 Jakob Axelsson: Schedulability-Driven Partitioning of Heterogeneous Real-Time Systems, 1995.No 518 Göran Forslund: Toward Cooperative Advice-Giving Systems: The Expert Systems Experience, 1995.No 522 Jörgen Andersson: Bilder av småföretagares ekonomistyrning, 1995.No 538 Staffan Flodin: Efficient Management of Object-Oriented Queries with Late Binding, 1996.No 545 Vadim Engelson: An Approach to Automatic Construction of Graphical User Interfaces for Applications in

Scientific Computing, 1996.No 546 Magnus Werner : Multidatabase Integration using Polymorphic Queries and Views, 1996.FiF-a 1/96 Mikael Lind: Affärsprocessinriktad förändringsanalys - utveckling och tillämpning av synsätt och metod,

1996.No 549 Jonas Hallberg: High-Level Synthesis under Local Timing Constraints, 1996.No 550 Kristina Larsen: Förutsättningar och begränsningar för arbete på distans - erfarenheter från fyra svenska fö-

retag. 1996.No 557 Mikael Johansson: Quality Functions for Requirements Engineering Methods, 1996.No 558 Patrik Nordling: The Simulation of Rolling Bearing Dynamics on Parallel Computers, 1996.No 561 Anders Ekman: Exploration of Polygonal Environments, 1996.No 563 Niclas Andersson: Compilation of Mathematical Models to Parallel Code, 1996.No 567 Johan Jenvald: Simulation and Data Collection in Battle Training, 1996.No 575 Niclas Ohlsson: Software Quality Engineering by Early Identification of Fault-Prone Modules, 1996.No 576 Mikael Ericsson: Commenting Systems as Design Support—A Wizard-of-Oz Study, 1996.No 587 Jörgen Lindström: Chefers användning av kommunikationsteknik, 1996.No 589 Esa Falkenroth: Data Management in Control Applications - A Proposal Based on Active Database Systems,

1996.No 591 Niclas Wahllöf: A Default Extension to Description Logics and its Applications, 1996.No 595 Annika Larsson: Ekonomisk Styrning och Organisatorisk Passion - ett interaktivt perspektiv, 1997.No 597 Ling Lin: A Value-based Indexing Technique for Time Sequences, 1997.

No 598 Rego Granlund: C3Fire - A Microworld Supporting Emergency Management Training, 1997.No 599 Peter Ingels: A Robust Text Processing Technique Applied to Lexical Error Recovery, 1997.No 607 Per-Arne Persson: Toward a Grounded Theory for Support of Command and Control in Military Coalitions,

1997.No 609 Jonas S Karlsson: A Scalable Data Structure for a Parallel Data Server, 1997.FiF-a 4 Carita Åbom: Videomötesteknik i olika affärssituationer - möjligheter och hinder, 1997.FiF-a 6 Tommy Wedlund: Att skapa en företagsanpassad systemutvecklingsmodell - genom rekonstruktion, värde-

ring och vidareutveckling i T50-bolag inom ABB, 1997.No 615 Silvia Coradeschi: A Decision-Mechanism for Reactive and Coordinated Agents, 1997.No 623 Jan Ollinen: Det flexibla kontorets utveckling på Digital - Ett stöd för multiflex? 1997.No 626 David Byers: Towards Estimating Software Testability Using Static Analysis, 1997.No 627 Fredrik Eklund: Declarative Error Diagnosis of GAPLog Programs, 1997.No 629 Gunilla Ivefors: Krigsspel coh Informationsteknik inför en oförutsägbar framtid, 1997.No 631 Jens-Olof Lindh: Analysing Traffic Safety from a Case-Based Reasoning Perspective, 1997No 639 Jukka Mäki-Turja:. Smalltalk - a suitable Real-Time Language, 1997.No 640 Juha Takkinen: CAFE: Towards a Conceptual Model for Information Management in Electronic Mail, 1997.No 643 Man Lin: Formal Analysis of Reactive Rule-based Programs, 1997.No 653 Mats Gustafsson: Bringing Role-Based Access Control to Distributed Systems, 1997.FiF-a 13 Boris Karlsson: Metodanalys för förståelse och utveckling av systemutvecklingsverksamhet. Analys och vär-

dering av systemutvecklingsmodeller och dess användning, 1997.No 674 Marcus Bjäreland: Two Aspects of Automating Logics of Action and Change - Regression and Tractability,

1998.No 676 Jan Håkegård: Hiera rchical Test Architecture and Board-Level Test Controller Synthesis, 1998.No 668 Per-Ove Zetterlund: Normering av svensk redovisning - En studie av tillkomsten av Redovisningsrådets re-

kommendation om koncernredovisning (RR01:91), 1998.No 675 Jimmy Tjäder: Projektledaren & planen - en studie av projektledning i tre installations- och systemutveck-

lingsprojekt, 1998.FiF-a 14 Ulf Melin: Informationssystem vid ökad affärs- och processorientering - egenskaper, strategier och utveck-

ling, 1998.No 695 Tim Heyer: COMPASS: Introduction of Formal Methods in Code Development and Inspection, 1998.No 700 Patrik Hägglund: Programming Languages for Computer Algebra, 1998.FiF-a 16 Marie-Therese Christiansson: Inter-organistorisk verksamhetsutveckling - metoder som stöd vid utveckling

av partnerskap och informationssystem, 1998.No 712 Christina Wennestam: Information om immateriella resurser. Investeringar i forskning och utveckling samt

i personal inom skogsindustrin, 1998.No 719 Joakim Gustafsson: Extending Temporal Action Logic for Ramification and Concurrency, 1998.No 723 Henrik André-Jönsson: Indexing time-series data using text indexing methods, 1999.No 725 Erik Larsson: High-Level Testability Analysis and Enhancement Techniques, 1998.No 730 Carl-Johan Westin: Informationsförsörjning: en fråga om ansvar - aktiviteter och uppdrag i fem stora svenska

organisationers operativa informationsförsörjning, 1998.No 731 Åse Jansson: Miljöhänsyn - en del i företags styrning, 1998.No 733 Thomas Padron-McCarthy: Performance-Polymorphic Declarative Queries, 1998.No 734 Anders Bäckström: Värdeskapande kreditgivning - Kreditriskhantering ur ett agentteoretiskt perspektiv,

1998.FiF-a 21 Ulf Seigerroth: Integration av förändringsmetoder - en modell för välgrundad metodintegration, 1999.FiF-a 22 Fredrik Öberg: Object-Oriented Frameworks - A New Strategy for Case Tool Development, 1998.No 737 Jonas Mellin: Predictable Event Monitoring, 1998.No 738 Joakim Eriksson: Specifying and Managing Rules in an Active Real-Time Database System, 1998.FiF-a 25 Bengt E W Andersson: Samverkande informationssystem mellan aktörer i offentliga åtaganden - En teori om

aktörsarenor i samverkan om utbyte av information, 1998.No 742 Pawel Pietrzak: Static Incorrectness Diagnosis of CLP (FD), 1999.No 748 Tobias Ritzau: Real-Time Reference Counting in RT-Java, 1999.No 751 Anders Ferntoft: Elektronisk affärskommunikation - kontaktkostnader och kontaktprocesser mellan kunder

och leverantörer på producentmarknader,1999.No 752 Jo Skåmedal: Arbete på distans och arbetsformens påverkan på resor och resmönster, 1999.No 753 Johan Alvehus: Mötets metaforer. En studie av berättelser om möten, 1999.No 754 Magnus Lindahl: Bankens villkor i låneavtal vid kreditgivning till högt belånade företagsförvärv: En studie

ur ett agentteoretiskt perspektiv, 2000.No 766 Martin V. Howard: Designing dynamic visualizations of temporal data, 1999.No 769 Jesper Andersson: Towards Reactive Software Architectures, 1999.No 775 Anders Henriksson: Unique kernel diagnosis, 1999.FiF-a 30 Pär J. Ågerfalk: Pragmatization of Information Systems - A Theoretical and Methodological Outline, 1999. No 787 Charlotte Björkegren: Learning for the next project - Bearers and barriers in knowledge transfer within an

organisation, 1999.No 788 Håkan Nilsson: Informationsteknik som drivkraft i granskningsprocessen - En studie av fyra revisionsbyråer,

2000.No 790 Erik Berglund: Use-Oriented Documentation in Software Development, 1999.No 791 Klas Gäre: Verksamhetsförändringar i samband med IS-införande, 1999.No 800 Anders Subotic: Software Quality Inspection, 1999.No 807 Svein Bergum: Managerial communication in telework, 2000.

No 809 Flavius Gruian: Energy-Aware Design of Digital Systems, 2000.FiF-a 32 Karin Hedström: Kunskapsanvändning och kunskapsutveckling hos verksamhetskonsulter - Erfarenheter

från ett FOU-samarbete, 2000.No 808 Linda Askenäs: Affärssystemet - En studie om teknikens aktiva och passiva roll i en organisation, 2000.No 820 Jean Paul Meynard: Control of industrial robots through high-level task programming, 2000.No 823 Lars Hult: Publika Gränsytor - ett designexempel, 2000.No 832 Paul Pop: Scheduling and Communication Synthesis for Distributed Real-Time Systems, 2000.FiF-a 34 Göran Hultgren: Nätverksinriktad Förändringsanalys - perspektiv och metoder som stöd för förståelse och

utveckling av affärsrelationer och informationssystem, 2000.No 842 Magnus Kald: The role of management control systems in strategic business units, 2000.No 844 Mikael Cäker: Vad kostar kunden? Modeller för intern redovisning, 2000.FiF-a 37 Ewa Braf: Organisationers kunskapsverksamheter - en kritisk studie av ”knowledge management”, 2000.FiF-a 40 Henrik Lindberg: Webbaserade affärsprocesser - Möjligheter och begränsningar, 2000.FiF-a 41 Benneth Christiansson: Att komponentbasera informationssystem - Vad säger teori och praktik?, 2000.No. 854 Ola Pettersson: Deliberation in a Mobile Robot, 2000.No 863 Dan Lawesson: Towards Behavioral Model Fault Isolation for Object Oriented Control Systems, 2000.No 881 Johan Moe: Execution Tracing of Large Distributed Systems, 2001.No 882 Yuxiao Zhao: XML-based Frameworks for Internet Commerce and an Implementation of B2B

e-procurement, 2001.No 890 Annika Flycht-Eriksson: Domain Knowledge Management inInformation-providing Dialogue systems,

2001.FiF-a 47 Per-Arne Segerkvist: Webbaserade imaginära organisationers samverkansformer: Informationssystemarki-

tektur och aktörssamverkan som förutsättningar för affärsprocesser, 2001.No 894 Stefan Svarén: Styrning av investeringar i divisionaliserade företag - Ett koncernperspektiv, 2001.No 906 Lin Han: Secure and Scalable E-Service Software Delivery, 2001.No 917 Emma Hansson: Optionsprogram för anställda - en studie av svenska börsföretag, 2001.No 916 Susanne Odar: IT som stöd för strategiska beslut, en studie av datorimplementerade modeller av verksamhet

som stöd för beslut om anskaffning av JAS 1982, 2002.FiF-a-49 Stefan Holgersson: IT-system och filtrering av verksamhetskunskap - kvalitetsproblem vid analyser och be-

slutsfattande som bygger på uppgifter hämtade från polisens IT-system, 2001.FiF-a-51 Per Oscarsson:Informationssäkerhet i verksamheter - begrepp och modeller som stöd för förståelse av infor-

mationssäkerhet och dess hantering, 2001.No 919 Luis Alejandro Cortes: A Petri Net Based Modeling and Verification Technique for Real-Time Embedded

Systems, 2001.No 915 Niklas Sandell: Redovisning i skuggan av en bankkris - Värdering av fastigheter. 2001.No 931 Fredrik Elg: Ett dynamiskt perspektiv på individuella skillnader av heuristisk kompetens, intelligens, mentala

modeller, mål och konfidens i kontroll av mikrovärlden Moro, 2002.No 933 Peter Aronsson: Automatic Parallelization of Simulation Code from Equation Based Simulation Languages,

2002.No 938 Bourhane Kadmiry: Fuzzy Control of Unmanned Helicopter, 2002.No 942 Patrik Haslum: Prediction as a Knowledge Representation Problem: A Case Study in Model Design, 2002.No 956 Robert Sevenius: On the instruments of governance - A law & economics study of capital instruments in li-

mited liability companies, 2002.FiF-a 58 Johan Petersson: Lokala elektroniska marknadsplatser - informationssystem för platsbundna affärer, 2002.No 964 Peter Bunus: Debugging and Structural Analysis of Declarative Equation-Based Languages, 2002.No 973 Gert Jervan: High-Level Test Generation and Built-In Self-Test Techniques for Digital Systems, 2002.No 958 Fredrika Berglund: Management Control and Strategy - a Case Study of Pharmaceutical Drug Development,

2002.FiF-a 61 Fredrik Karlsson: Meta-Method for Method Configuration - A Rational Unified Process Case, 2002.No 985 Sorin Manolache: Schedulability Analysis of Real-Time Systems with Stochastic Task Execution Times,

2002.No 982 Diana Szentiványi: Performance and Availability Trade-offs in Fault-Tolerant Middleware, 2002.No 989 Iakov Nakhimovski: Modeling and Simulation of Contacting Flexible Bodies in Multibody Systems, 2002.No 990 Levon Saldamli: PDEModelica - Towards a High-Level Language for Modeling with Partial Differential

Equations, 2002.No 991 Almut Herzog: Secure Execution Environment for Java Electronic Services, 2002.No 999 Jon Edvardsson: Contributions to Program- and Specification-based Test Data Generation, 2002No 1000 Anders Arpteg: Adaptive Semi-structured Information Extraction, 2002.No 1001 Andrzej Bednarski: A Dynamic Programming Approach to Optimal Retargetable Code Generation for

Irregular Architectures, 2002.No 988 Mattias Arvola: Good to use! : Use quality of multi-user applications in the home, 2003.FiF-a 62 Lennart Ljung: Utveckling av en projektivitetsmodell - om organisationers förmåga att tillämpa

projektarbetsformen, 2003.No 1003 Pernilla Qvarfordt: User experience of spoken feedback in multimodal interaction, 2003.No 1005 Alexander Siemers: Visualization of Dynamic Multibody Simulation With Special Reference to Contacts,

2003.No 1008 Jens Gustavsson: Towards Unanticipated Runtime Software Evolution, 2003.No 1010 Calin Curescu: Adaptive QoS-aware Resource Allocation for Wireless Networks, 2003.No 1015 Anna Andersson: Management Information Systems in Process-oriented Healthcare Organisations, 2003.No 1018 Björn Johansson: Feedforward Control in Dynamic Situations, 2003.No 1022 Traian Pop: Scheduling and Optimisation of Heterogeneous Time/Event-Triggered Distributed Embedded

Systems, 2003.FiF-a 65 Britt-Marie Johansson: Kundkommunikation på distans - en studie om kommunikationsmediets betydelse i

affärstransaktioner, 2003.

No 1024 Aleksandra Tešanovic: Towards Aspectual Component-Based Real-Time System Development, 2003.No 1034 Arja Vainio-Larsson: Designing for Use in a Future Context - Five Case Studies in Retrospect, 2003.No 1033 Peter Nilsson: Svenska bankers redovisningsval vid reservering för befarade kreditförluster - En studie vid

införandet av nya redovisningsregler, 2003.FiF-a 69 Fredrik Ericsson: Information Technology for Learning and Acquiring of Work Knowledge, 2003.No 1049 Marcus Comstedt: Towards Fine-Grained Binary Composition through Link Time Weaving, 2003.No 1052 Åsa Hedenskog: Increasing the Automation of Radio Network Control, 2003.No 1054 Claudiu Duma: Security and Efficiency Tradeoffs in Multicast Group Key Management, 2003.FiF-a 71 Emma Eliason: Effektanalys av IT-systems handlingsutrymme, 2003.No 1055 Carl Cederberg: Experiments in Indirect Fault Injection with Open Source and Industrial Software, 2003.No 1058 Daniel Karlsson: Towards Formal Verification in a Component-based Reuse Methodology, 2003.FiF-a 73 Anders Hjalmarsson: Att etablera och vidmakthålla förbättringsverksamhet - behovet av koordination och

interaktion vid förändring av systemutvecklingsverksamheter, 2004.No 1079 Pontus Johansson: Design and Development of Recommender Dialogue Systems, 2004.No 1084 Charlotte Stoltz: Calling for Call Centres - A Study of Call Centre Locations in a Swedish Rural Region,

2004.FiF-a 74 Björn Johansson: Deciding on Using Application Service Provision in SMEs, 2004.No 1094 Genevieve Gorrell: Language Modelling and Error Handling in Spoken Dialogue Systems, 2004.No 1095 Ulf Johansson: Rule Extraction - the Key to Accurate and Comprehensible Data Mining Models, 2004.No 1099 Sonia Sangari: Computational Models of Some Communicative Head Movements, 2004.No 1110 Hans Nässla: Intra-Family Information Flow and Prospects for Communication Systems, 2004.No 1116 Henrik Sällberg: On the value of customer loyalty programs - A study of point programs and switching costs,

2004.FiF-a 77 Ulf Larsson: Designarbete i dialog - karaktärisering av interaktionen mellan användare och utvecklare i en

systemutvecklingsprocess, 2004.No 1126 Andreas Borg: Contribution to Management and Validation of Non-Functional Requirements, 2004.No 1127 Per-Ola Kristensson: Large Vocabulary Shorthand Writing on Stylus Keyboard, 2004.No 1132 Pär-Anders Albinsson: Interacting with Command and Control Systems: Tools for Operators and Designers,

2004.No 1130 Ioan Chisalita: Safety-Oriented Communication in Mobile Networks for Vehicles, 2004.No 1138 Thomas Gustafsson: Maintaining Data Consistency im Embedded Databases for Vehicular Systems, 2004.No 1149 Vaida Jakoniené: A Study in Integrating Multiple Biological Data Sources, 2005.No 1156 Abdil Rashid Mohamed: High-Level Techniques for Built-In Self-Test Resources Optimization, 2005.No 1162 Adrian Pop: Contributions to Meta-Modeling Tools and Methods, 2005.No 1165 Fidel Vascós Palacios: On the information exchange between physicians and social insurance officers in the

sick leave process: an Activity Theoretical perspective, 2005.FiF-a 84 Jenny Lagsten: Verksamhetsutvecklande utvärdering i informationssystemprojekt, 2005.No 1166 Emma Larsdotter Nilsson: Modeling, Simulation, and Visualization of Metabolic Pathways Using Modelica,

2005.No 1167 Christina Keller: Virtual Learning Environments in higher education. A study of students’ acceptance of edu-

cational technology, 2005.No 1168 Cécile Åberg: Integration of organizational workflows and the Semantic Web, 2005.FiF-a 85 Anders Forsman: Standardisering som grund för informationssamverkan och IT-tjänster - En fallstudie

baserad på trafikinformationstjänsten RDS-TMC, 2005.No 1171 Yu-Hsing Huang: A systemic traffic accident model, 2005.FiF-a 86 Jan Olausson: Att modellera uppdrag - grunder för förståelse av processinriktade informationssystem i trans-

aktionsintensiva verksamheter, 2005.No 1172 Petter Ahlström: Affärsstrategier för seniorbostadsmarknaden, 2005.No 1183 Mathias Cöster: Beyond IT and Productivity - How Digitization Transformed the Graphic Industry, 2005.No 1184 Åsa Horzella: Beyond IT and Productivity - Effects of Digitized Information Flows in Grocery Distribution,

2005.No 1185 Maria Kollberg: Beyond IT and Productivity - Effects of Digitized Information Flows in the Logging

Industry, 2005.No 1190 David Dinka: Role and Identity - Experience of technology in professional settings, 2005.No 1191 Andreas Hansson: Increasing the Storage Capacity of Recursive Auto-associative Memory by Segmenting

Data, 2005.No 1192 Nicklas Bergfeldt: Towards Detached Communication for Robot Cooperation, 2005.No 1194 Dennis Maciuszek: Towards Dependable Virtual Companions for Later Life, 2005.No 1204 Beatrice Alenljung: Decision-making in the Requirements Engineering Process: A Human-centered

Approach, 2005No 1206 Anders Larsson: System-on-Chip Test Scheduling and Test Infrastructure Design, 2005.No 1207 John Wilander: Policy and Implementation Assurance for Software Security, 2005.No 1209 Andreas Käll: Översättningar av en managementmodell - En studie av införandet av Balanced Scorecard i ett

landsting, 2005.No 1225 He Tan: Aligning and Merging Biomedical Ontologies, 2006.No 1228 Artur Wilk: Descriptive Types for XML Query Language Xcerpt, 2006.No 1229 Per Olof Pettersson: Sampling-based Path Planning for an Autonomous Helicopter, 2006.No 1231 Kalle Burbeck: Adaptive Real-time Anomaly Detection for Safeguarding Critical Networks, 2006.No 1233 Daniela Mihailescu: Implementation Methodology in Action: A Study of an Enterprise Systems Implemen-

tation Methodology, 2006.No 1244 Jörgen Skågeby: Public and Non-public gifting on the Internet, 2006.No 1248 Karolina Eliasson: The Use of Case-Based Reasoning in a Human-Robot Dialog System, 2006.No 1263 Misook Park-Westman: Managing Competence Development Programs in a Cross-Cultural Organisation-

What are the Barriers and Enablers, 2006. FiF-a 90 Amra Halilovic: Ett praktikperspektiv på hantering av mjukvarukomponenter, 2006.No 1272 Raquel Flodström: A Framework for the Strategic Management of Information Technology, 2006.

No 1277 Viacheslav Izosimov: Scheduling and Optimization of Fault-Tolerant Embedded Systems, 2006.No 1283 Håkan Hasewinkel: A Blueprint for Using Commercial Games off the Shelf in Defence Training, Education

and Research Simulations, 2006.FiF-a 91 Hanna Broberg: Verksamhetsanpassade IT-stöd - Designteori och metod, 2006.No 1286 Robert Kaminski: Towards an XML Document Restructuring Framework, 2006No 1293 Jiri Trnka: Prerequisites for data sharing in emergency management, 2007.No 1302 Björn Hägglund: A Framework for Designing Constraint Stores, 2007.No 1303 Daniel Andreasson: Slack-Time Aware Dynamic Routing Schemes for On-Chip Networks, 2007.No 1305 Magnus Ingmarsson: Modelling User Tasks and Intentions for Service Discovery in Ubiquitous Computing,

2007.No 1306 Gustaf Svedjemo: Ontology as Conceptual Schema when Modelling Historical Maps for Database Storage,

2007.No 1307 Gianpaolo Conte: Navigation Functionalities for an Autonomous UAV Helicopter, 2007.No 1309 Ola Leifler: User-Centric Critiquing in Command and Control: The DKExpert and ComPlan Approaches,

2007.No 1312 Henrik Svensson: Embodied simulation as off-line representation, 2007.No 1313 Zhiyuan He: System-on-Chip Test Scheduling with Defect-Probability and Temperature Considerations,

2007.No 1317 Jonas Elmqvist: Components, Safety Interfaces and Compositional Analysis, 2007.No 1320 Håkan Sundblad: Question Classification in Question Answering Systems, 2007.No 1323 Magnus Lundqvist: Information Demand and Use: Improving Information Flow within Small-scale Business

Contexts, 2007.No 1329 Martin Magnusson: Deductive Planning and Composite Actions in Temporal Action Logic, 2007.No 1331 Mikael Asplund: Restoring Consistency after Network Partitions, 2007.No 1332 Martin Fransson: Towards Individualized Drug Dosage - General Methods and Case Studies, 2007.No 1333 Karin Camara: A Visual Query Language Served by a Multi-sensor Environment, 2007.No 1337 David Broman: Safety, Security, and Semantic Aspects of Equation-Based Object-Oriented Languages and

Environments, 2007.No 1339 Mikhail Chalabine: Invasive Interactive Parallelization, 2007.No 1351 Susanna Nilsson: A Holistic Approach to Usability Evaluations of Mixed Reality Systems, 2008.No 1353 Shanai Ardi: A Model and Implementation of a Security Plug-in for the Software Life Cycle, 2008.No 1356 Erik Kuiper: Mobility and Routing in a Delay-tolerant Network of Unmanned Aerial Vehicles, 2008.