Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
Wireless Communications: Principles and Practice
Wireless Communications: Principles and Practice
Directed diffusion for wireless sensor networking
IEEE/ACM Transactions on Networking (TON)
The good, bad and ugly: distributed detection of a known signal in dependent Gaussian noise
IEEE Transactions on Signal Processing
Decentralized detection in sensor networks
IEEE Transactions on Signal Processing
On the optimality of finite-level quantizations for distributed signal detection
IEEE Transactions on Information Theory
Distributed Detection in Wireless Sensor Networks Using Dynamic Sensor Thresholds
International Journal of Distributed Sensor Networks - Selected Papers in Innovations and Real-Time Applications of Distributed Sensor Networks
Optimal placement of distributed sensors against moving targets
ACM Transactions on Sensor Networks (TOSN)
Fusion of threshold rules for target detection in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Computer Networks: The International Journal of Computer and Telecommunications Networking
Optimal multiobjective placement of distributed sensors against moving targets
ACM Transactions on Sensor Networks (TOSN)
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A distributed detection and decision fusion scheme is proposed for a wireless sensor network (WSN) consisting of a large number of sensors. At the fusion center, the total number of detections reported by local sensors are employed for hypothesis testing. Based on the assumption that the received signal power decays as the distance from the target increases, system level detection performance measures, namely probabilities of detection and false alarm, are derived analytically through approximation by using the central limit theorem (CLT). If the number of sensors is sufficiently large, the proposed fusion rule can provide very good system level detection performance, in the absence of the knowledge of local sensors' performances and at low signal to noise ratio (SNR). It is shown that for all the different system parameters we have explored, this fusion rule is equivalent to the optimal fusion rule, which requires much more prior information. To achieve a better system level detection performance, the local sensor level decision threshold should be designed optimally. Numerical methods are employed to find the optimal local sensor level threshold for different sets of system parameters. Guidelines on selecting the optimal local sensor level decision threshold are also provided.