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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
EE
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