Practical Belief Propagation in Wireless Sensor Networks

Practical Belief Practical Belief Propagation Propagation

in Wireless Sensor in Wireless Sensor Networks Networks

Bracha HodBracha HodBased on a joint work with: Based on a joint work with:

Danny Dolev, Tal Anker and Danny Danny Dolev, Tal Anker and Danny BicksonBickson

The Hebrew University of The Hebrew University of JerusalemJerusalem

Outline

Introduction to Wireless Sensor Networks

Belief Propagation overview Efficient Belief Propagation

framework for Wireless Sensor Networks

Experimental evaluation Summary

Israeli Networking Seminar May 29, 2008

Wireless Sensor Networks Technology is pushed by

breakthroughs in MEMS, wireless communication, battery power, etc.

Wireless Sensor Networks (WSNs) Wireless network consisting of

spatially distributed autonomous devices using sensors

The sensors cooperatively monitor physical or environmental conditions


WSN Applications

WSN Characteristics Limited power sources and restricted

computational capacities Wireless medium which imposes

constraints, such as collisions and errors

Topology changes due to interference, poor link quality, sleep states, death, etc.

Special network dynamic resulting from the self-organization property

Scaling problems because the network has a large number of nodes


History 1994 - DARPA funded research on ‘Low

Power Wireless Integrated Microsensor’ 1998 - WSN technology has been nurtured

in its early stages at UC-Berkeley and UCLA It is estimated that in the US over $100 million

in government funding has been invested in university WSN research projects since then

2003 - Technology Review from MIT, listed WSN on the top, among 10 emerging technologies, that would impact our future

2008 – About ten years of academic work has been done in this area but still a long way to go


Current Status Research

Many protocols for routing, synchronization, fault tolerant, localization, collaborative information processing, data aggregation, etc.

Development Dedicated Operating System and Database

system, programming languages and test deployments

Standardization IEEE 802.15.4, Zigbee, 6lowpan

Still a lot to do Deployment, integration with other

networks, security and scalability


Inference in WSNs Data fusion and processing are the

core information gathering activities performed in the sensor nodes

Consequently, inference methods become an increasing research interest in the field of WSNs

In this model, an undirected graph G = (V,E) is a set of nodes V and arcs E, which represent dependencies among random variables

A complex system is viewed as a combination of many simpler pieces connected by probability theory

The idea: instead of calculating 8-sumswe can calculate4-sumsand 2-sums

Graphical Model


A

D

C

B

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 Variation 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


Practical Considerations

The unique constraints and requirements in WSN demand changes in traditional algorithms

Several issues to address Mapping WSN to graphical model Robustness against failures Scalability


Mapping WSN to Graphical Model

Loopy BP Operates on a cyclic network Usually works well because the cycles are

large Junction Tree

Creates a tree based on the cliques in the graph

Scales exponentially with the number of nodes

Involves high overhead

X1

X5

X4X3

X2

m12m21

m45m54

m24m32

m23

m42


Robustness against Failures Broadcast communication

The message update rule is not an atomic operation which may result in erroneous calculation

Synchronization problems Asynchronous messages may harm the

accuracy Topology changes

A link break in the middle of the message-passing may badly affect the convergence


Scalability

The original BP algorithm is based on

a local message passing, but it is not

scalable The process is performed in the entire

network

The convergence depends on the size

of the network, and as a result, time

and message complexity are not

constantIsraeli Networking Seminar May 29, 2008

Our Solution Adoption of two WSNs’ approaches

Localized Algorithms Data-centric

Resulting in

Approximation by a set of local optimums instead of a single global optimum

Energy-efficient, fully distributed, asynchronous, robust and scale

scheme


Efficient BP Framework Construction of multiple trees according to

the routing tree properties and the information that the nodes hold

Every tree is created on-the-fly using a special message, without any maintenance

A “round” field in each message helps in dealing with the asynchronous process

Part of the errors may be detected and overcome

Scalability is achieved by operating in a restricted region, with limited number of rounds


Empirical Evaluation Case study:

clustering The challenge is to

efficiently form a connected disjointed group of nodes in a local and distributed manner. Each group contains a single leader and several ordinary nodes


Simulation Framework Simulation in TOSSIM, TinyOS simulator 5 different time slots were used to

validate the behavior on different network topologies

The localized algorithm vicinity was set to 2 with constant number of rounds equal to 8

In the simulation, the average density is 14 which means that the optimal number of clusters for 50 nodes is about 4


Simulation Results


Network topology

Message-passing trees

Clustered network

Simulation Results

Number of clusters per 50 nodes

Percent of nodes who have a cluster head


Simulation Results

Percent of nodes who reach a full convergence

Average loss messages in a message-passing tree


Summary WSNs are envisioned to become an

integral part of our lives, in applications such as environmental monitoring, smart spaces, medical monitoring, etc.

Two leading approaches: localized algorithms and data-centric, are essential for the design of practical and robust algorithms in WSNs

BP is a promising approach to solve inference tasks in WSNs, when combined with these two approaches.


Thank You!Thank You!


Practical Belief Propagation in Wireless Sensor Networks

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