Node Selection in Distributed Sensor Networks Olawoye Oyeyele 03/04/2004

Outline Motivation Aims and Objectives Modeling Assumptions Selection Algorithm Simulation Conclusions

Motivation Densely deployed wireless sensor networks consume energy through communications Bandwidth may not be sufficient for data transmission in such networks Subset of data may provide acceptable detection Computational burden of processing all available data may be prohibitive. Network may be short-lived

Aims & Objectives Select a subset of sensors (hence data) for detection Consistently achieve coverage of entire region Do not compromise detection performance Minimize complexity of selection algorithm Low Energy consumption Small Latency Achieve fault-tolerance through redundancy

Modeling Assumptions Target can be modeled as an isotropic radiating source with power law decay Data captured by nearby sensors are similar, therefore redundant Spatial dependence can be estimated Sensor network is a sampling problem in which the spatial process is sampled spatio-temporally Nodes are capable of power control

Typical Environment Nodes fail unpredictably High Probability of attack from weather and other agents Nodes operate autonomously

Sensor Network Properties Consists of battery operated sensor nodes Deployed randomly on the field in large numbers Resource constrained sensor nodes power, computation, communication No networking infrastructure

Systematic Sampling Sample the sensor field as systematically as possible within discs of radius r Exploit spatial dependence to select sensors

Estimating Spatial Dependence The Semi-variogram is the major tool used in estimating spatial dependence Is the semi-variance at lag h, h The lag i.e distance between locations si and sj yi Value of variable y at location si yj Value of variable y at location sj N(h) Number of pairs of observed data points separated by lag h

The Semi-variogram Shows the semi-variance plotted vs. lag for different lags. At a certain lag (distance) called the Range, the data measurements cease to be correlated. Many theoretical models in use; Power-law, exponential, Gaussian, spherical etc.

The Semi-variogram

Power-law Variogram A power law variogram implies infinite variance Solution: select arbitrary value for ‘Range’ r. Only requirement is r < rs and r < rt where rs and rt are the sensing and maximum transmission range of sensor node

Selection Algorithm Estimate “Range” or “Correlation Length” and use as a basis for selecting sensors Simple Packet containing the source node’s remnant energy is sent RM RM – Remaining Energy 32 bit Number representing the remnant energy on the Node

Properties of spatial selection Ensures that data is collected from all over the area covered by the network Reduces communication cost Chosen ‘Range’ used to select sensors Range may be seen as limit of spatial dependence Range can be chosen to ensure coverage and connectivity Achieves high fault-tolerance and may work well in harsh environments

State Machine SLEEP NEGOTIATE ACTIVE

Pseudo-Code createNodes(number); deploy nodes(AREA); //transmit up to Range using appropriate energy level nodes[i].transmit(corRange) Do { SpatialSelect() if nodes[i].status = ACTIVE if nodes[j] == nodes[i].neighbor && nodes[j].status == ACTIVE nodes[j].receive(); if nodes[j].energy <= nodes[i].energy nodes[j].status = SLEEP for a predetermined time; else if nodes[j].energy > nodes[i].energy nodes[j].status = ACTIVE for predetermined time; end end SpatialSelect(); } while (predetermined time) //executed after predetermined time

Applicable Scenarios Data Selection Sensor Selection Refers to the determination of sites where data will be collected Sensor Selection The problem of choosing the sensors to be used in detection Algorithm may be applied at various points in the lifetime of a network Post-deployment Change of cluster – head etc.

Simulation Parameters Operation Energy Dissipated Transmitter/Receiver Electronics 50nJ Transmit Amplifier 100pJ/bit/m2 Parameter Value Transmission Rate 100kbps Number of Nodes 200 Area 50m by 50m Topology Uniformly Random

Simulation Results Energy consumption Detector Performance (Receiver Operating Characteristic) Demonstrate that chosen subset achieves coverage and connectivity

Sensor Network

Node Configuration : Range = 1

Node Configuration: Range 3

Node Configuration : Range = 5

Selected Sensors vs. Range Values

Energy Depletion with Time

Energy Consumption

ROC performance

Discussion Spatial Selection promises to be a robust selection technique Consistent coverage and connectivity, fairly stable performance (ROC) Simplified technique Increased network lifetime Flexible – offers two design parameters Range Value Sleep Time Performs better than Randomization or pure Random Selection

References Clark Isobel, ‘Practical Geostatistics’ (on the web) Xu Yingyue, Hairong Qi, ’Decentralized Reactive Clustering in Collaborative Processing Using Different Computing Paradigms’ Sestok C.K., Maya R. Said, Alan V. Oppenheim, ’Randomized Data Selection in Detection with Applications to Distributed Signal Processing’,Proceedings of the IEEE, September, 2003 Dalenius T., Jaroslav Hajek, Stefan Zubrzycki,’On plane sampling and Related Geometrical Problems’,Fourth Berkeley Symposium, 1961. Ripley Brian D.,’Spatial Statistics’,John Wiley and Sons, 1981.