Taking Advantage of Data Correlation to Control the Topology of Wireless Sensor Networks

TRUST, Spring Conference, April 2-3, 2008

Taking Advantage of Data Correlation to Control the Topology of Wireless

Sensor Networks

Sergio Bermudez and Prof. Stephen WickerSchool of ECE, Cornell UniversityTo be presented at the 15th International Conference on Telecommunications


Agenda

Introduction– Motivation– Background

Model and Results– Assumptions– Analysis

Conclusions

"Topology Control," Sergio Bermudez 2


Introduction

Application of wireless sensor network (WSN)– Power Grid– Water supply pipes

Keep nodes in place for long periods Necessity to extend their lifetime Reducing their energy consumption



Motivation

Sleeping Schemes– reduce energy

consumption

Data aggregation– routing scheme with

compression of correlated data



Motivation

Random Clustering– Data aggregation at cluster heads



Conceptual idea

Combination of approaches to extend the network lifetime

Exploit data correlation to control the topology of a wireless network

Application to monitor physical infrastructure– Modal vibration in structures, e.g. a girder



Conceptual Idea

– autonomous creation of clusters– scheduling algorithm within these clusters



Proposed Scheme

Active nodes and backup nodes– Active nodes form the active network

Network Functional lifetime– network can carry-out useful operations

Focus on network connectivity



Proposed Scheme

Two-step procedure:– Initial setup: clusters

information obtained by the correlation of the sensed data

– Organization: Sleeping Scheduling energy-efficient scheduling mechanism controls the

underlying topology of the network

Random value at deployment– Cluster formation

Random scheduling– Energy saving



Proposed Scheme

Advantages– Localized protocol– Allows for continuous sensing

Disadvantages– Applicable in environments with strong data

correlation – Non-uniform energy depletion—secondary concern



Assumptions

Measurements are perfectly correlated within distance tc – spatial correlation

Nodes are able to determine data correlation information

Nodes distributed according to a Poisson spatial process with intensity

High density of node platforms Boolean communication model, radius rc

Uniform energy depletion when a node is active



Assumptions

Cluster Formation– Based on the spatial correlation– Define Active and Backup nodes, with

probability pa, pb

Sleeping Scheduling– Random energy-efficient scheduling mechanism

backup nodes belonging to a cluster go to sleep randomly and independently from each other

– For network robustness the backup nodes wake up periodically, with low-duty cycle



Analysis: network connectivity

We get a Marked Poisson process– Active and Backup nodes, with probability pa, pb– Choose appropriate µ such that there is

connectivity with high probability


~N ab pp 1

aa pN ~ bb pN ~

ANANAN ab


Results: network connectivity


Connectivity asfunction of active nodes

Connectivity asfunction ofnode density


Analysis: number of neighbors

Distribution of nearest-neighbor distance

Probability that exists a backup node as a neighbor for aj


2exp1

|1,

r

orobNPrD

c

cj

cj

jc

j

tD

tbaP

tbaP

aGPaGP

1

1

| 1|

2

1DE


Results: number of neighbors



Results: increase in lifetime


SNE

SNSNELT

a

ba


Conclusions

Approach to control the topology of wireless sensor networks by data correlation

Increase of functional lifetime, relevant for the monitoring of physical infrastructure

Future work:– Analysis with respect to other deployment

distribution– Couple this scheme with mobile sensor networks



Further Reading

“Networking Wireless Sensors”B. Krishnamachari

“A survey of Energy-Efficient scheduling mechanisms in sensor networks”L. Wang and Y. Xiao

“The impact of spatial correlation on routing with compression in wireless sensor networks”S. Pattem, B. Krishnamachari, andR. Govindan


Documents

Taking Advantage of Data Correlation to Control the Topology of Wireless Sensor Networks