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Spray and Wait:An Efficient Routing Scheme for

Intermittently Connected Mobile Networks

Thrasyvoulos Spyropoulos, Konstantinos Psounis, Cauligi S. Raghavendra

(All from the University of Southern California)

SIGCOMM-2005, Philadelphia

Presented by: Harshal Pandya

On 10/31/2006 for CS 577 - Advanced Computer

Networks


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Abstract

Intermit tent ly Connected Mobi le Netwo rks (ICMN)aresparse wireless networks where most of the time there does

not exist a complete path from the source to the destination. Itcan be viewed as a set of disconnected, time-varying clustersof nodes

These fall into the general category of Delay TolerantNetworks, where incurred delays can be very large and

unpredictable.

Some networks that follow this paradigm are: Wildlife tracking sensor networks

Military networks

Inter-planetary networks

In such networks conventional routing schemes such as DSR& AODV would fail

ABSTRACT

Introduction

Related Work

Spray & Wait

Optimization

Simulation

Conclusion


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An example of Intermittently ConnectedMobile Networks (ICMN)

S is the source & D is the Destination

There is no direct path from S to D

In this case all the conventional protocols would fail Thus, the authors introduce a new routing scheme, called Spray

and Wait, that sprays a number of copies into the network, andthen waits till one of these nodes meets the destination.

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Basic Idea behind Spray & Wait

In such networks, the traditional protocols will fail to discover a complete pathor will fail to converge, resulting in a deluge of topology update messages

However, this does not mean that packets can never be delivered in suchnetworks

Over time, different links come up and down due to node mobility. If thesequence of connectivity graphs over a time interval are overlapped, then anend-to-end path might exist

This implies that a message could be sent over an existing link, get bufferedat the next hop until the next link in the path comes up, and so on, until itreaches its destination

This approach imposes a new model for routing. Routing consists of asequence of independent, local forwarding decisions, based on currentconnectivity information and predictions of future connectivity information

In other words, nod e mo bi l i ty needs to be explo i ted in order to overcomethe lack of end-to-end connect iv i ty and del iver a message to i tsdest inat ion

Abstract

INTRODUCTION

Related Work

Spray & Wait

Optimization

Simulation

Conclusion


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A possible solution

Abstract

INTRODUCTION

Related Work

Spray & Wait

Optimization

Simulation

Conclusion

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Advantages of Spray & Wait

Under lowload, Spray and Wait results in much fewertransmissions and comparable or smaller delays than flooding-

based schemes

Under highload, it yields significantlybetterdelays and fewertransmissions than flooding-based schemes

It is highly scalable, exhibiting good and predictable performancefor a large range of network sizes, node densities and connectivity

levels. As the size of the network and the number of nodes increase,the number of transmissionsper node that Spray and Wait requiresin order to achieve the same performance, decreases

It can be easily tuned onlineto achieve given QoS requirements,even in unknown networks

Using only a handful of copies per message, it can achievecomparable delays to an oracle-based optimal scheme thatminimizes delay while using the lowest possible number oftransmissions

Abstract

INTRODUCTION

Related Work

Spray & Wait

Optimization

Simulation

Conclusion


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Related Work

A large number of routing protocols for wireless ad-hoc networkshave been proposed in the past. The performance of such protocols

would be poor even if the network was only slightly disconnected

When the network is not dense enough (as in the ICMN case), evenmoderate node mobility would lead to frequent disconnections

In most cases trepairis expected to be larger than the path duration,

this way reducing the expected throughput to almost zero accordingto the formula:

PD = average Path Duration

Another approach to deal with disconnections is to reinforceconnectivity on demand, by bringing additional communicationresources into the network when necessary(e.g. satellites, UAVs,etc.)

Abstract

Introduction

RELATED WORK

Spray & Wait

Optimization

Simulation

Conclusion


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Related Work

Similarly, one could force a number of specialized nodes(e.g. robots) tofollow a given trajectorybetween disconnected parts of the network in orderto bridge the gap

There, a number of algorithms with increasing knowledge about networkcharacteristics like upcoming contacts, queue sizes, etc.is compared with anoptimal centralized solution of the problem, formulated as a linear program

A number of mobile nodes performing independent random walks serve asDataMules that collect data from static sensors and deliver them to basestations

In a number of other works, all nodes are assumed to be mobileandalgorithms to transfer messages from any node to any other node, aresought for

Epidemic Rout ing : Here, each node maintains a list of all messages it carries, whose delivery is

pending. Whenever it encounters another node, the two nodes exchange all

messages that they dont have in common. This way, all messages are eventuallyspread to all nodes, including their destination But it creates a lot of contention for the limited buffer spaceand network capacity

of typical wireless networks, resulting in many message drops andretransmissions

Abstract

Introduction

RELATED WORK

Spray & Wait

Optimization

Simulation

Conclusion


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Related Work

Randomized Flood ing: One simple approach to reduce the overhead of flooding and improve

its performance is to only forward a copy with some probability p < 1

History-based orUti l i ty-based Rout ing : Here, each node maintains a utility value for every other nodein the

network, based on a timer indicating the time elapsed since the twonodes last encountered each other. These utility values essentiallycarry indirect information about relative node locations, which getdiffused through nodes mobility

Nodes forward message copies only to nodes with a higher utility bysome pre-specified threshold value Uthfor the messages destination.Such a scheme results in superior performance than flooding

But these schemes face a dilemma when choosing the utility threshold

Oracle-based algor i thm : This algorithm is aware of all future movements, and computes the

optimal set of forwarding decisions (i.e. time and next hop), which

delivers a message to its destination in the minimum amount of time. This algorithm cannot be implemented practically, but is quite useful to

compare against proposed practical schemes

Abstract

Introduction

RELATED WORK

Spray & Wait

Optimization

Simulation

Conclusion

Wh t h ld t f S &


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What should we expect from Spray &Wait ?

Perform significantly fewer transmissionsthan epidemic and other flooding-based routing schemes, under all conditions

Generate low contention, especially under high traffic loads

Achieve a delivery delaythat is better than existing single and multi-copyschemes, and close to the optimal

Be highly scalable, that is, maintain the above performance behavior despitechanges in network size or node density

Be simpleand require as little knowledge about the network as possible, inorder to facilitate implementation

Decouplethe number of copies generated per message, and therefore thenumber of transmissions performed, from the network size

Thus it should be a tradeoffbetween single and multi-copy schemes.

Abstract

Introduction

Related Work

SPRAY & WAIT

Optimization

Simulation

Conclusion


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Definition

Definition 3.1 : Spray and Wait routing consists of thefollowing two phases:

Spray phase:For every message originating at a source node,L message copies are initially spreadforwarded by thesource and possibly other nodes receiving a copyto L distinctrelays

Wait phase:If the destination is not found in the sprayingphase, each of the L nodes carrying a message copy performsdirect transmission (i.e. will forward the message only to itsdestination)

This does not tell us how the Lcopies of a message are to bespread initially. So an improvement over Spray & Wait isBinary Spray & Wait

Abstract

Introduction

Related Work

SPRAY & WAIT

Optimization

Simulation

Conclusion


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Binary Spray & Wait

Binary Spray & Wait: The source of a message initially starts with L copies; any node A that

has n > 1 message copies, and encounters another node B with nocopies, hands over to B, n/2 and keeps n/2 for itself; when it is left withonly one copy, it switches to direct transmission

To prove that Binary Spray and Wait is optimal, when node movementis IID, the authors state and prove a theorem

Theorem 3.1: When all nodes move in an IID manner, Binary Sprayand Wait routing is optimal, that is, has the minimum expected delayamong all spray and wait routing algorithms

Proof: Let us call a node activewhen it has more than one copies of amessage. Let us further define a spraying algorithm in terms of afunction f : N N as follows

When an active node with n copies encounters another node, it handsover to it f(n) copies, and keeps the remaining 1 f(n)

Abstract

Introduction

Related Work

SPRAY & WAIT

Optimization

Simulation

Conclusion


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Binary Spray & Wait

Any spraying algorithm (i.e. any f) can be represented by the following binarytree with the source as its root: Assign the root a value of L; if the currentnode has a value n > 1 create a right child with a value of 1f(n) and a left

one with a value of f(n); continue until all leaf nodes have a value of 1

A particular spraying corresponds then to a sequence of visiting all nodes ofthe tree. This sequence is random. On the average, all tree nodes at thesame level are visited in parallel

Further, since only active nodes may hand over additional copies, the higherthe number of active nodes when icopies are spread, the smaller the

residual expected delay

Since the total number of tree nodes is fixed (21+log L 1) for any sprayingfunction f, it is easy to see that the tree structure that has the maximumnumber of nodes at every level, also has the maximum number of activenodes at every step.

This tree is the balanced tree, and corresponds to the Binary Spray and Waitrouting scheme. As Lgrows larger, the sophistication of the sprayingheuristic has an increasing impact on the delivery delay of the spray and waitscheme.

Abstract

Introduction

Related Work

SPRAY & WAIT

Optimization

Simulation

Conclusion


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As the number of copies ofthe message increase, theSpray & Wait Mechanismslowly moves towardsoptimality. Binary Spray &Wait is better than SourceSpray & Wait

Figure compares the expected delay of Binary Spray & Waitand Source Spray & Wait as a function of the number ofcopies Lused, in a 100100 network with 100 nodes

This figure also shows the delay of the Optimal scheme.

Binary Spray & Wait

Abstract

Introduction

Related Work

SPRAY & WAIT

Optimization

Simulation

Conclusion


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Optimizing Spray & Wait

In many situations the network designer or the application itselfmight impose certain performance requirements on the protocols

(e.g. maximum delay, maximum energy consumption, minimumthroughput, etc.).

Spray and Wait can be tuned to achieve the desired performance.

To do so the authors summarize a few results regarding theexpected delay of the Direct Transmission and Optimal schemes

Lemma 4.1:Let M nodes with transmission range K performindependent random walks on a torus.

The expected delay of Direct Transmission is exponentially distributedwith average

Abstract

Introduction

Related Work

Spray & Wait

OPTIMIZATION

Simulation

Conclusion


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Optimizing Spray & Wait

The expected delay of the Optimal algorithm is:

Lemma 4.2:The expected delay of Spray and Wait, when Lmessage copies are used, is upper-bounded by:

This bound is tight when L


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Choosing L to Achieve a RequiredExpected Delay

Assume that there is a specific delivery delay constraint to be met.This delay constraint is expressed as a factor a times the optimal

delay EDopt(a > 1)

Lemma 4.3:The minimum number of copies Lminneeded for Sprayand Wait to achieve an expected delay at most a*EDopt, isindependent ofthe size of the network Nand transmission range K,and only depends ona and the number of nodes M

Thus we get the following equation from the previous upperbounded equation of EDsw

Note : EDsw = a*EDopt and approximate the harmonic numberHMLwith its Taylor Series terms up to second order, and solvethe resulting third degree polynomial

Abstract

Introduction

Related Work

Spray & Wait

OPTIMIZATION

Simulation

Conclusion


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Comparing Various L

L found through the approximation is quite accurate when the

delay constraint is not too stringent.

Abstract

Introduction

Related Work

Spray & Wait

OPTIMIZATION

Simulation

Conclusion

Estimating L when Network Parameters


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Estimating Lwhen Network Parametersare Unknown

In many casesboth Mand N, might be unknown

But for determining L, at least M is required

Hence we have to somehow estimate M to find out L

A straightforward way to estimate Mwould be to count unique IDs of nodesencountered already. This method requires a large database of node IDs tobe maintained in large networks, and a lookup operation to be performed

every time any node is encountered

A better method: We define T1as the time until a node encounters anyothernode. T1is exponentially distributed with average T1= EDdt/(M 1)

We similarly define T2as the time until two different nodes are encountered,then the expected value of T2equals EDdt (1/(M-1) + 1/(M-2))

Canceling EDdtfrom these two equations we get the following estimate for M:

Abstract

Introduction

Related Work

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OPTIMIZATION

Simulation

Conclusion


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A better estimate of T1& T2

When a random walk i meets another random walkj, i andj become coupled

In other words, the next inter meeting time of i andj is not anymore exponentiallydistributed with average EDdt

In order to overcome this problem, each node keeps a record of all nodes with which it iscoupled

Every time a new node is encountered, it is stamped as coupled for an amount of timeequal to the mixing or relaxation time for that graph

Then, when node Imeasures the next sample inter meeting time, it ignores all nodesthat its coupled with at the moment, denoted as ck, and scales the collected sample T1,kby (Mck)/(M1)

A similar procedure is followed for T2. Putting it altogether, after n samples have beencollected:

Abstract

Introduction

Related Work

Spray & Wait

OPTIMIZATION

Simulation

Conclusion


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Comparing the online estimators of M

This method is more than two times faster than ID counting

Abstract

Introduction

Related Work

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OPTIMIZATION

Simulation

Conclusion

Estimatedvalue of M

quickly

converges

with the

actual value


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Scalability of Spray and Wait

As the number of nodes in the network increases, the percentage ofnodes (Lmin/M) that need to become relays in Spray and Wait to

achieve the same performance relative to the optimal, actuallydecreases

Also, the performance of Spray & Wait improves faster than optimalscheme !!! This can be proved using Lemma 4.4

Abstract

Introduction

Related Work

Spray & Wait

OPTIMIZATION

Simulation

Conclusion

Spray and Wait actually decreases the transmissions per


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Spray and Wait actually decreases the transmissions per

node as the number of nodes Mincreases

Lemma 4.4: Let L/M be constant and let L


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Scenario A : Effect of Traffic Load

Assumptions: 100 nodes move according to the random waypoint model in a 500 500 grid

with reflective barriers. The transmission range Kof each node is equal to 10

Each node is generating a new message for a randomly selected destination withan inter-arrival time distribution uniform in [1, Tmax] until time 10000

Tmaxis variedfrom 10000 to 2000 creating average traffic loads from 200 (lowtraffic) to 1000 (high traffic).

Abstract

Introduction

Related Work

Spray & Wait

Optimization

SIMULATION

Conclusion

Spray & Wait

is

significantly

better than

other

schemes


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Scenario B : Effect of Connectivity

The size of the network is 200200 and Tmaxis fixed to 4000(medium traffic load). The number of nodes Mand transmission

range K, are variedto evaluate the performance of all protocols innetworks with a large range of connectivity characteristics, rangingfrom very sparse, highly disconnected networks, to almostconnected networks.

As the

transmissionrange increases

more & more

nodes fall within

the range of each

other & the

percentage of

nodes in the max

cluster increases

Abstract

Introduction

Related Work

Spray & Wait

Optimization

SIMULATION

Conclusion

Scenario B: Number of transmissions for various


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Scenario B: Number of transmissions for various

transmission ranges for 100 & 200 nodes

For both networks, the number of

transmissionsfor spray and wait arevery lessin number as compared to

other schemes & are also more or

less independent from the

transmission rangeK

Abstract

Introduction

Related Work

Spray & Wait

Optimization

SIMULATION

Conclusion

Scenario B: Delivery delay for various


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Scenario B: Delivery delay for various

transmission ranges for 100 & 200 nodes

The delivery delay of Spray and Wait

is significantly better than that of

other schemes & depends on thetransmission range. As the

transmission range increases the

delay decreases.


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Conclusion

Spray and Wait effectively manages to overcome theshortcomings of epidemic routing and other flooding-based

schemes, and avoids the performance dilemma inherent inutility-based schemes

Spray and Wait, despite its simplicity, outperforms all existingschemes with respect to number of transmission sand

delivery delays, achieves comparable delays to an optimalscheme, and is very sc alableas the size of the network orconnectivity level increase

Abstract

Introduction

Related Work

Spray & Wait

Optimization

Simulation

CONCLUSION


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Some issues not discussed

Power consumed

Security

Constrained Mobility of Nodes


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Acknowledgements

The slide design has been adopted from the presentationby Mike Putnam (Because I liked it a lot)

Some figures have been adopted from the presentationby the authors

All other figures have been taken from the actual paper


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Thank you

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