Efficient Clustering for Improving Network

Performance in Wireless Sensor Networks

Bracha HodJoint work with:

Tal Anker, Danny Bickson and Danny Dolev

The Hebrew University of Jerusalem

Outline

Introduction Related work Motivation and main contribution Belief Propagation (BP) Clustering using BP Simulation results Summary

Introduction Cluster-based network is divided

into subsets Each group of nodes contains a

single leader (cluster head) and several ordinary nodes

Introduction Clustering main objectives

Minimize the total transmission power aggregated over all nodes in the selected path

Balance the load to prolong the network lifetime

Clustering advantages Increase network scalability Support data aggregation Reduce energy consumption

Clustering challenges Optimal cluster selection is a hard problem Cluster maintenance is essential

Related Work Many research efforts

LEACH - Low Energy Adaptive Clustering Hierarchy (Heinzelman et al, 2002)

HEED - Hybrid, Energy-Efficient, Distributed clustering (Younis et al, 2004)

VCA - Voting-based Clustering Algorithm (Qin et al, 2005)

EEUC - Energy-Efficient Unequal Clustering (Li et al, 2005)

Motivation

Network performance is important Retransmission and dropped

packets may waste energy Since the network is usually dense

and several nodes are redundant, network lifetime should be measured by the time that the system is available for providing services

Main Contribution We propose a novel approach based on

Belief Propagation (BP) Considers both local properties of a node

and joint characteristics of a group of nodes Utilizes better the available information Incurs small constant overhead

Resulting in Better network performance Balanced power consumption among the

nodes Our scalable and practical implementation

of BP can be used for other inference goals

Belief Propagation (BP) BP is an iterative algorithm for

computing maximum or marginal posterior probabilities by a local message passing

BP is associated with rapid convergence, accurate results and good performance in asynchronous environment

When performed on trees, BP converges to the correct values in a finite number of iterations

The Min-Sum Algorithm (MS) The goal is to minimize the overall cost

in the network, based on the local cost functions and the constraints between the nodes

Each node transmits to its neighbors a message with its local and joint costs. Each neighbor updates its own belief accordingly and transmits the new belief

Gradually the information is propagated through the network until the nodes converge to a common belief

Efficient Implementation The Min-Sum variation of BP

requires simple operations and works well with integer values Saves floating-point calculation

Broadcast messages instead of the traditional unicast messages in BP Preserves communication resources

The routing tree is used as the message-passing tree No special maintenance and overhead

Example Network Routing tree

which used also as the message-passing treeA

B

E

C D5

76

4

4

8

100

80

85 83

151

A

B

E

C D

Example Round 1: Messages transmitted by all the

nodes A B C D E

Processing by node A: A: A->A+B->A+C->A+D->A = 80+7+6+8 =

101 B: A->B+B->B+C->B+D->D = 7+85+5+83 =

180 If B is selected to be the cluster head, D

selects itself …

A

B

E

C D5

7 6

4

4

8100

80

85 83

151

E 151C 4B 65535D 65535

D 83A 8C 4

C 100A 6B 5D 4E 4

B 85A 7C 5E 65535

A 80B 7C 6D 8

Example

E 155C 104B 65535D 65535

D 91A 88C 10

C 110A 237B 163D 163E 235

B 92A 87C 11E 65535

A 101B 180C 115D 180

Round 2: Messages transmitted by all the nodes

A B C D E

Processing by node A: A->A: 80 B->A: 87 – 80 = 7 C->A: 237 – 80 = 157

The message from E is propagated to A D->A: 88 – 80 = 8 Total cost: 80 + 7 + 157 + 8 = 252

A

B

E

C D5

7 6

4

4

8100

80

85 83

151

Example

E 235C 110B 65535D 65535

D 180A 101C 115

C 119A 252B 331D 331E 252

B 180A 101C 115E 65535

A 252B 331C 119D 331

E 252C 119B 65535D 65535

D 331A 252C 119

C 119A 252B 331D 331E 252

B 331A 252C 119E 65535

A 252B 331C 119D 331

Round 3: Messages transmitted by all the nodes

A B C D E

After round 3, all the nodes converge to a common belief – node C should be the cluster head A B C D E

A

B

E

C D5

7 6

4

4

8100

80

85 83

151

Clustering using MS Two events trigger a clustering process

A regular node does not have a cluster head

Periodically by each cluster head to balance the power

Message passing properties 1-hop vicinity for localized and distributed

process Complete asynchronous operation Number of rounds is bounded and

determines a-priory to avoid impact of the environment

Clustering using MS Every message contains

Self cost of being a cluster head Cost of connecting to other cluster

heads Final state decision (on last round)

Cost metric Self cost is based on the expected

energy consumption in a period and the residual battery power The expected energy consumption

considers degree and distance from the base station

Joint cost is based on link quality and the residual battery power

Simulation Model Simulation in TOSSIM, TinyOS simulator 250 nodes including a single base station Link Estimation and Parent Selection

routing protocol Shortest path metric combined with link quality

Surge application for data aggregation Power information of Berkeley Mica2 mote Variable power levels for cluster heads and

regular nodes

HEED Cluster heads are selected with a

probability based on their residual energy

When there are no cluster heads’ announcements, a node selects itself with the probability it has or alternatively doubles its probability for the next round

Local and efficient method which achieves very good results on simulations

Performance Evaluation Data collection time

Clustering using BP achieves more than 40% higher throughput than HEED

Performance Evaluation Data collection rate during the

network lifetime

Clustering with BP achieves better routing, deployment and stability

Performance Evaluation BP has better deployment and

network stability than HEED

Average hop count Re-clustering processes

Performance Evaluation Clustering

Overhead

BP suffers from more overhead because larger size of messages

Network Lifetime

HEED has very small advantage because using BP more packets are transmitted

Summary We present a new framework for

clustering based on BP This approach is fully distributed,

localized, asynchronous, robust and scalable

Utilization of all available information and not only subset of parameters yields better results and better network performance

Future work Comparing the BP algorithm with the

theoretical optimal clustering algorithm

Thank You!

Appendix – Notations of BP - set of possible states of node i - local distribution function of

node i - joint function of two

connected nodes i and j - unicast message from

node i to node j at round t about the state that node j should be

- broadcast message from node i to its direct neighbors N(i)

MS Formulation Message passing

Message update rule

Belief calculation

Efficient implementation in sensors by broadcast messages and integer calculations only