Ad hoc and Sensor Networks Routing protocols (Part II)

Ad hoc and Sensor NetworksRouting protocols

(Part II)

Outline

4.1 Routing Challenges and Design Issues in WSNs 4.2 Flat Routing 4.3 Hierarchical Routing 4.4 Location Based Routing 4.5 QoS Based Routing 4.6 Data Aggregation and Convergecast 4.7 Data Centric Networking 4.8 ZigBee 4.9 Conclusions

2

Chapter 4.6 Data Aggregation and

Convergecast

Aggregation in Sensor Networks

Traditional Address-Centric routing IP address routing Not suitable in large scale sensor networks

Data-Centric Routing Content-based routing Enhance the data aggregation opportunity

4

Source 1 Source 2

A B

Sink

Source 1 Source 2

A B

Sink

a) Address-Centric (AC) Routing b) Data-Centric (DC) Routing

1

1

2

2

21

1+2

DataAggregation

Theoretical Results on Aggregation

Let there be k sources located within a diameter X, each a distance di from the sink. Let NA and ND be the number of transmissions required with AC and optimal DC protocols, respectively.

1. The following are bounds on ND:

2. Asymptotically, for fixed k, X, as d = min(di) is increased,

3. Although the problem is NP-hard in general, the optimal data aggregation tree can be formed in polynomial time when the sources induce a connected sub-graph on the communication graph.

( 1) min( )

min( ) ( 1)D i

D i

N k X d

N d k

1lim D

dA

N

N k

5

Aggregation Techniques

In general the formation of the optimal aggregation tree is NP-hard. Some suboptimal DC routing heuristics as follows: Center at Nearest Source (CNSDC)

All sources send the information first to the source nearest to the sink, which acts as the aggregator.

Shortest Path Tree (SPTDC) Opportunistically merge the shortest paths from each source

wherever they overlap.

Greedy Incremental Tree (GITDC) Start with path from sink to nearest source. Successively add next

nearest source to the existing tree.

Address Centric (AC) No aggregation, distinct shortest paths from each source to sink.

6

Performance Study

Event-Radius model Random Sources model

7

Performance Study (cont.)

Energy Costs

Event-Radius model

8

Performance Study (cont.)

Energy Costs

Random Sources model9

Conclusions

Data aggregation can result in significant energy savings for a wide range of operational scenarios.

The gains from aggregation are paid for with potentially higher delay. It should be possible to design routing algorithms for sensor networks in which this tradeoff is made explicitly.

10

Reference

B. Krishnamachari, D. Estrin, and S. B. Wicker, "The impact of data aggregation in wireless sensor networks," In Proceedings of the 22nd International Conference on Distributed Computing Systems Workshops (ICDCSW'02), pp. 575-578, Vienna, Austria, July 02-05 2002.

11

Chapter 4.7 Data centric networking

4.7.2 Data-centric Storage

Data centric storage Data is stored inside the network. All data with the same name (or data range) will be

stored at the same sensor network location Why data centric storage?

Energy efficiency Robustness against mobility and node failures Scalability

13

One-dimensional Data Storage

Data-Centric Storage in Sensornets with GHT, a Geographic Hash Table (GHT [Ratnasamy et al. 2003]) Data Storage and Retrieval Perimeter Refresh Protocol Structured Replication

14

One-dimensional Data Storage GHT

Put(k, v)- stores v (observed data) according to the key k

Get(k)- retrieve whatever value is associated with key k

Hash function Hash the key into the

geographic coordinates Put() and Get()

operations on the same key “k” hash k to the same location

(12,24)

user

queryresponse

data

Hash (‘elephant’)=(12,24)

Put (“elephant”, data)Get (“elephant”)

Hash (‘elephant’)=(12,24)

Elephant Data

An example for GHT

15

Perimeter Refresh Protocol

Assume key k hashes at location L

A is closest to L so it becomes the home node

E

F

B

D

A

C

L

home

Replica

Replica

16

Structured Replication

Augment event name with hierarchy depth

Given root r and given hierarchy depth d Compute 4d – 1 mirror

images of r

(0, 0) (100, 0)

(100, 100)(0, 100)

root point

level 1 mirror points

level 2 mirror points

Example of structured replication with a 2-level decomposition

17

Conclusions Data centric storage entails naming of data and

storing data at nodes within the sensor network GHT uses Perimeter Refresh Protocol and

structured replication to enhance robustness and scalability

DCS is useful in large sensor networks and there are many detected events but not all event types are Queried

18

Multi-dimensional Data Storage

Multi-Dimensional Range Queries in Sensor Networks (DIM [Li et al. 2003]) Building Zones Data Insertion Query Propagation

19

Building Zones

Divide network into zones.

Each node mapped to one zone.

Encode zones based on division.

Each zone has a unique code.

Map m-d space to zones.

Zones organized into a virtual binary tree.

6

5

1110

1111

43

21

78

10

9

1100111

0110

010

0001

0000 001 10

L[1/2, 1)L[0, 1/2)

T[

1/2,

1)

T[

0, 1

/2)

L[0, 1/4) L[1/4, 1/2) L[1/2, 3/4) L[3/4, 1)

T[3/4, 1)T[1/2, 3/4)

T[1/4, 1/2)T[0, 1/4)

L: Light, T: Temperature

20

Data Insertion

Encode events Compute

geographic destination

Hand to GPSR

Intermediate nodes can refine the destination estimation

1110

1111

L[1/2, 1)

L[0, 1/4)

1

110

L[0, 1/2)

0111

0110

010

T[

1/2,

1)

0001

0000 001 10

T[

0, 1

/2)

L[1/4, 1/2) L[1/2, 3/4) L[3/4, 1)

T[3/4, 1)T[1/2, 3/4)

T[1/4, 1/2)T[0, 1/4)

2

34

5

6

987

10

E1= <0.8, 0.7>

Store E1


21

Query Propagation

Split a large query into smaller sub-queries.

Encode each sub-query.

Process sub-queries separately, resolving locally or forwarding to other nodes based on their codes.

1110

1111

L[1/2, 1)

L[0, 1/4)

1

110

L[0, 1/2)

0111

0110

010

T[

1/2,

1)

0001

0000 001 10

T[

0, 1

/2)

L[1/4, 1/2) L[1/2, 3/4) L[3/4, 1)

T[3/4, 1)T[1/2, 3/4)

T[1/4, 1/2)T[0, 1/4)

2

34

5

6

987

10

Q10= <.75-1, .5-.75>

Q1= <0.5-1, 0.5-1>

Q12= <.75-1, .75-1>Q11= <.5-.75, . 5-1>


22

Conclusions DIM resolves multi-dimensional range queries

efficiently. Work that still needs to be done

Skewed data distribution These can cause storage and transmission hotspots.

Existential queries Whether there exists an event matching a multi-

dimensional range. Node heterogeneity

Nodes with larger storage space assert larger-sized zones for themselves.

23

Chapter 4.8ZigBee

24

The ZigBee Standard

ZigBee is a low cost, low power, low complexity, and low data rate wireless communication technology at short range. Based on IEEE 802.15.4, it is mainly used as a low data rate monitoring and controlling sensor network

802.15.4

ZigbeeSpecification

Applications

Application Framework

Network & Security

Medium Access Control (MAC) Layer

Physical (PHY) Layer

Application

Zigbee stack

Hardware

25

The Network Layer

ZigBee identifies three device types The ZigBee coordinator (one in the network) is an FFD

managing the whole network A ZigBee router is an FFD with routing capabilities A ZigBee end-device corresponds to a RFD or FFD acting

as a simple device

The ZigBee network layer supports three types of network configurations: Star topology Tree topology Mesh topology

26

The Network Layer (cont.)

ZigBee coordinator ZigBee router ZigBee end device

(a) Star network (b) Tree network (c) Mesh network

27

Network Formation and Address Assignment (Tree Network)

Before forming a network, the coordinator determines Maximum number of children of a router (Cm)

Maximum number of child routers of a router (Rm)

Depth of the network (Lm)

Note that a child of a router can be a router or an end

device, so Cm ≥ Rm

The coordinator and routers can each have at most Rm

child routers and at most Cm − Rm child end devices

28

Network Formation and Address Assignment (cont.)

For the coordinator, the whole address space is logically partitioned into Rm + 1 blocks

The first Rm blocks are to be assigned to the coordinator’s child routers and the last block is reserved for the coordinator’s own child end devices

From Cm, Rm, and Lm, each router computes a parameter called Cskip to derive the starting addresses of its children’s address pools

1

1 1 ,if 1

1 Otherwise,1

Lm d

Cm Lm dRm

Cskip d Cm Rm Cm Rm

Rm

29


The coordinator is said to be at depth d = 0, and d is increased by one after each level

Address assignment begins from the ZigBee coordinator by assigning address 0 to itself

If a parent node at depth d has an address Aparent :

the n-th child router is assigned to address:

Aparent + (n − 1) × Cskip(d) + 1

the n-th child end device is assigned to address:

Aparent + Rm × Cskip(d) + n

30



Addr = 12

Addr = 8

Addr = 9

Addr = 10

Addr = 25

Addr = 0Cskip = 6

A4

Addr = 19Cskip = 1

A3

Addr = 13Cskip = 1

Addr = 24

A1

Addr = 1Cskip = 1

Addr = 6

Addr = 3

Addr = 2

Cm = 5: Maximum number of children of a router Rm = 4: Maximum number of child routers of a router Lm = 2: Depth of the network A2

Addr = 7 Cskip = 1 the n-th child router:

Aparent + (n − 1) × Cskip(d) + 1

the n-th child end device: Aparent + Rm × Cskip(d) + n

31

ZigBee Routing Protocol

In a ZigBee network, the coordinator and routers can directly transmit packets along the tree

When a device receives a packet, it first checks if it is the destination or one of its child end devices is the destination

If so, this device will accept the packet or forward this packet to the designated child. Otherwise, it forwards the packet to its parent

32

ZigBee Routing Protocol (cont.)

If a device n receives a packet with destination Adest . Assume that the depth of the device n is d and its address is A. This packet is for one of its descendants if the destination address Adest satisfies A < Adest < A+ Cskip(d − 1), and this packet will be relayed to the child router with address

If the destination is not a descendant of this device, this packet will be forwarded to its parent

1

1dest

rA A

A A Cskip dCskip d

33

ZigBee Routing Protocol (cont.)


Cm = 6Rm = 4Lm = 3

1

1dest

rA A

A A Cskip dCskip d

A < Adest < A + Cskip(d − 1)

Addr = 125

Addr = 30

Addr = 31

Addr = 38

Addr = 126

Addr = 92

Addr = 63Cskip = 7

Addr = 64Cskip = 1

Addr = 1Cskip = 7

Addr = 32Cskip = 7

Addr = 33Cskip = 1

Addr = 40Cskip = 1

Z

? ?

BC

A

Addr = 0Cskip = 31

34

Route Discovery (Mesh Network)

Field Name Description

Destination Address

16-bit network address of the destination

Next-hop Address

16-bit network address of next hop towards destination

Entry Status One of Active, Discovery or Inactive

Routing Table in ZigBee

35

Route Discovery (cont.)

Field Name Description

RREQ ID(route request)

Unique ID (sequence number) given to every RREQ message being broadcasted

Source Address

Network address of the initiator of the route request

Sender Address

Network address of the device that sent the most recent lowest cost RREQ

Forward Cost The accumulated path cost from the RREQ originator to the current device

Residual Cost The accumulated path cost from the current device to the RREQ destination

Route Discovery Table

36

Route Discovery (cont.)RREQ message

Create RDT entry and record fwd path

cost

Update RDT entry with better fwd path

cost

DoesRREQ report

A better fwd path

cost ?

Send RREPDrop RREQ

No

Yes

No

Yes

No

Yes

The RREQ processing

37

RDT entry exists for this RREQ ?

IsRREQ for local node or one of end-device children ?

Create RT entry(Discovery under way)And rebroadcast RREQ

Route Discovery (cont.)

S

D

A

C

T

B

Discard route request

route

req.

route req.

route req.

route req.

route req.

route reply

UnicastBroadcastWithout routing capacity

38

References

P. Baronti, P. Pillai, V. Chook, S. Chessa, and F. Gotta, A. andFun Hu. Wireless sensor networks: a survey on the state of the art and the 802.15.4 and zigbee standards. Communication Research Centre, UK, May 2006.

J. Bruck, J. Gao and A. A. Jiang, “MAP: Medial Axis Based Geometric Routing in Sensor Network,” in Proceedings of ACM MobiCom, 2005.

Q. Fang, J. Gao, L. Guibas, V. de Silva, and L. Zhang. GLIDER: Gradient landmark-based distributed routing for sensor networks. In Proc. of the 24th Conference of the IEEE Communication Society (INFOCOM’05), March 2005.

B. Chen, K. Jamieson, H. Balakrishnan, and R. Morris. Span: An energy-efficient coordination algorithm for topology maintenance in ad hoc wireless networks. In International Conference on Mobile Computing and Networking (MobiCom 2001), pages 85–96, Rome, Italy, July 2001.

Y. Xu, J. Heidemann, and D. Estrin. Geography-informed energy conservation for ad hoc routing. In Proceedings of the ACM/IEEE International Conference on Mobile Computing and Networking, pages 70–84, Rome, Italy, July 2001.

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40

Conclusions

Routing in sensor networks is a new area of research, with a limited but rapidly growing set of research results

We highlight the design trade-offs between energy and communication overhead savings in some of the routing paradigm, as well as the advantages and disadvantages of each routing technique

Overall, the routing techniques are classified based on the network structure into four categories: flat, hierarchical, and location-based routing, and QoS based routing protocols.

Although many of these routing techniques look promising, there are still many challenges that need to be solved in sensor networks

Documents

Ad hoc and Sensor Networks Routing protocols (Part II)