Sensor Selection in Ad-Hoc Wireless Sensor Networks

Olawoye Oyeyele

10/10/2003

Outline

Sensor Selection Problem Randomization Spatial Selection Discussions

Sensor Selection Problem

Densely deployed wireless sensor networks consume energy through communications

Not all measured data necessarily required for detection

Subset of data may provide acceptable detection

Objectives

Robust selection Acceptable performance Minimum complexity

Randomization[3]

Algorithm divides data from sensors into time slots.

In every time slot each sensor in the cluster randomly determines whether or not to transmit its measurement to the base station

The probability of selection is a small value

Possible Demerits

May result in a biased selection of data – data may be taken from one corner of the network

Data may not be representative of the entire network

Puts a lot of burden on the manager node (cluster head/base station).

Differences in number of selected sensors per trial leads to variations in detector performance.

ROC for Randomization

ROC for K=5,10,25

Spatial Selection

Attempts to remove biased data selection May be an attractive choice if detection

accuracy and robustness are critical requirements.

Ensures that data set is representative of the ‘view’ of the network.

Uses principles of spatial statistics. Target can be modeled as an isotropically

radiating source with a power – law decay:source

received

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d

Spatial Statistics

Applying statistical methods to data that are spatially distributed

Techniques used extensively in geostatistics and GIS

Major component is spatial dependence Spatial dependence based on the fact that data in a small

section of space can be similar hence redundant i.e. little additional information provided)

The Variogram is the major tool used in estimating spatial dependence.

The Semi-variogram

2

| |

1( ) ( )

2 ( )i j

i js s

h y yN h

( )h

h

yi

yj

N(h)

Is the semi-variance at lag h,

The lag i.e distance between locations si and sj

Value of variable y at location si

Value of variable y at location sj

Number of pairs of observed data points separated by lag h

where

The Semi-variogram

Shows the variance plotted vs. lag for different lags. At a certain lag called the Range, the data

measurements cease to be correlated. A practical variogram may show slight deviations

thus a theoretical variogram may be fitted (Kriging) in order to determine parameters of interest

Many theoretical models in use; Power-law, exponential, gaussian etc.

Practical/Theoretical semi-variograms

Simulated Semi-variogram

Power-law variogram

Power-law Variogram

Power – law variogram depicts infinite variance

May imply that measurements taken are all spatially dependent. Infinite Range?

If infinite ‘Range’, select arbitrary value for ‘Range’.

Rationale

The idea is to represent the network by decorrelated measurements(independent)

Simple classical statistical methods can then be applied since the data to be analysed are independent

The reduced data set should offer simple structure for analyses and detection.

Optimal detection typically requires knowledge of data/noise correlation which may be intensive to determine or at best estimated.

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 a radius of influence(coverage)

Selection Algorithm

Range

Range represents “decorrelation distance”

Packet

TS SC NP SI PI

TS – Time Stamp (24 bits)

SC – Sender Coordinates (16 bits)

NP - Node Power (3 bits)

SI – Selection Indicator (1 bit)

PI – Participation Indicator (1 bit)

Procedure

Algorithm triggered by a query for data / event Sensor closest to event selects itself and begins to

talk to those within its ‘range’. All sensors within a cluster know the value of

predetermined ‘Range’ They communicate with sensors within Range to

determine last standing sensor The transmit power used is set to one appropriate

for the chosen ‘Range’

Two sensor - scenario

A selects self and broadcasts message to B B receives message and checks if A’s power

is higher, if it is, B changes status from selected to unselected, otherwise it stays unselected.

If B is selected already, then B has most likely sent a request. If A receives a message, it compares the timestamp to that of the sent message

Two sensor – scenario(contd.)

If it sent a packet before the received packet was sent it checks the fields and selects/unselects itself accordingly.

One gets selected the other goes to sleep mode.

Simulation Directions

Detector Performance (Receiver Operating Characteristic)

Energy consumption Theoretical characteristics of the detector.

Randomization leads to complex detector properties that are only implementable through approximations

Demonstrate that chosen subset achieves coverage[4]

Discussions

Spatial Selection promises to be a robust selection technique Consistent, stable performance

Complexity may be further reduced if combined with a reactive clustering technique such as DeReClus

Increased network lifetime

References

1. Clark Isobel, ‘Practical Geostatistics’ (on the web)2. Xu Yingyue, Hairong Qi,’Decentralized Reactive Clustering

in Collaborative Processing Using Different Computing Paradigms’

3. 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

4. Dalenius T., Jaroslav Hajek, Stefan Zubrzycki,’On plane sampling and Related Geometrical Problems’,Fourth Berkeley Symposium, 1961.

5. Ripley Brian D.,’Spatial Statistics’,John Wiley and Sons, 1981.

Advances in Energy Research

Two new advances in energy Fuel cell ( Motor cars, laptops etc) – still large

form factor , research is active Power paper - Thin and likely to revolutionize

miniaturization