Fault-Tolerant Target Detection in Sensor Networks Min Ding +, Dechang Chen *, Andrew Thaeler +, and Xiuzhen Cheng + + Department of Computer Science, The George Washington University * Uniformed Service University of the Health Science IEEE Wireless Communications and Networking Conference (WCNC), 2005 Wang, Sheng-Shih Apr. 1, 2005

Outline Introduction Fault-Tolerant Target Detection Algorithm Simulation Conclusion

Target Detection Applications Battlefield, disaster rescue, national park, etc Challenge Faulty or malfunctional sensors Concerns Fault tolerance Target position Low false alarm rate

Motivation and Objective Motivation Faulty sensor Waste network resource Mislead user to make wrong decisions Objectives Target region detection Target identification

Network Model Sensor Fixed radio range Location-aware Faulty sensor Report inconsistent and arbitrary values to the neighboring sensors

Network Model (cont’d) Signal propagation model Signal strength at s i Noise level (Gaussian distribution)

Target Region Detection Purpose Find all event sensors which can detect the presence of a target Basic idea Nodes closed to the target usually have higher measurements

Target Region Detection (cont’d) SiSi S5S5 S3S3 S4S4 S2S2 S6S6 S1S1 R1R1 R2R2 R3R3 R4R4 R5R5 R6R6 Estimate at S i Rule Faulty sensors may have extreme values but have little influence on the value of median

Target Localization Motivation Position computation at the base station Consume much energy Basic Idea One sensor locally computes the position of the target, and then transmits to the base station

Target Localization (cont’d) SiSi S5S5 S3S3 S4S4 S2S2 S6S6 S1S1 R1R1 R2R2 R3R3 R4R4 R5R5 R6R6 S i is the event sensor Root sensor determination Location estimation Depend on the subset of event sensors S7S7 R7R7 m=7 n =13 Geometric center

Temporal Dimension Consideration Purpose Identify false alarms Improve the target position accuracy

Temporal Dimension Consideration (cont’d) After collecting raw data from root sensors for T epoches, the base station applies a clustering algorithm to identify groups for targets Raw data (position): { L (1), L (2), …, L (t), … } Each group corresponds to one target For each group G The base station reports a false alarm if | G | < T/2

Temporal Dimension Consideration Example epochgroup id 1 st 1, 2, 3, 4, 5, 6, 7, 8, 9, 10 2 nd 1, 2, 3, 4, 5, 6, 7, 9 3 rd 1, 3, 4, 5, 6, 7, 8, 9, 10 4 th 1, 2, 3, 4, 6, 7, 9, 10 5 th 1, 3, 4, 6, 7, 10 6 th 1, 2, 3, 4, 6, 9, 10 7 th 1, 3, 4, 6, 7, 9, 10 8 th 2, 3, 4, 6, 7, 9, 10 Total epoches: 8 1 st group: 7 2 nd group: 4 3 rd group: 6 4 th group: 8 5 th group: 3 6 th group: 8 7 th group: 7 8 th group: 2 9 th group: 7 10 th group: 7 False alarm

Simulation Setup Simulation toolMATLAB Sensor region size b  b (b: variable) No. of sensors1024 Sensor deploymentUniformly random deployment 11 3  (3) 22 4  (4) No. of runs100

Simulation Result Higher network density leads to a higher correction accuracy correction accuracy a ( C ) C : set of event sensors O : set of faulty sensors

Simulation Result (cont’d) More sequential measurements results in higher correction accuracies

Simulation Result (cont’d) T=1

Simulation Result (cont’d) T=9

Simulation Result (cont’d) Position errors increase with higher sensor faulty probability Higher density decreases position errors

Conclusion Fault-tolerant algorithm for stationary target detection and localization Use median to locally aggregate neighboring readings to filter out faulty measurements Future work Multi-target identification