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
Energy efficiency of cooperative dense wireless sensor networks
Proceedings of the 2006 international conference on Wireless communications and mobile computing
Multisensor collaboration in wireless sensor networks for detection of spatially correlated signals
International Journal of Mobile Network Design and Innovation
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
A framework for QoI-inspired analysis for sensor network deployment planning
WICON '07 Proceedings of the 3rd international conference on Wireless internet
Multi-target cell tracking based on classic kinetics
Proceedings of the 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly
Data fusion improves the coverage of wireless sensor networks
Proceedings of the 15th annual international conference on Mobile computing and networking
An efficient multibit aggregation scheme for multihop wireless sensor networks
EURASIP Journal on Wireless Communications and Networking
Image change detection using wireless sensor networks
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
Estimation of target location via likelihood approximation in sensor networks
IEEE Transactions on Signal Processing
Distributed detection and localization of events in large ad hoc wireless sensor networks
Allerton'09 Proceedings of the 47th annual Allerton conference on Communication, control, and computing
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Bayesian data fusion for distributed target detection in sensor networks
IEEE Transactions on Signal Processing
Blind Adaptive Weighted Aggregation Scheme for Event Detection in Multihop Wireless Sensor Networks
Wireless Personal Communications: An International Journal
Delay optimal event detection on ad hoc wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Exploiting data fusion to improve the coverage of wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Adaptive calibration for fusion-based cyber-physical systems
ACM Transactions on Embedded Computing Systems (TECS)
Fusion-based volcanic earthquake detection and timing in wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
Description and composition of bio-inspired design patterns: a complete overview
Natural Computing: an international journal
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For a wireless sensor network (WSN) with a random number of sensors, we propose a decision fusion rule that uses the total number of detections reported by local sensors as a statistic for hypothesis testing. We assume that the signal power attenuates as a function of the distance from the target, the number of sensors follows a Poisson distribution, and the locations of sensors follow a uniform distribution within the region of interest (ROI). Both analytical and simulation results for system-level detection performance are provided. This fusion rule can achieve a very good system-level detection performance even at very low signal-to-noise ratio (SNR), as long as the average number of sensors is sufficiently large. For all the different system parameters we have explored, the proposed fusion rule is equivalent to the optimal fusion rule, which requires much more prior information. The problem of designing an optimum local sensor-level threshold is investigated. For various system parameters, the optimal thresholds are found numerically by maximizing the deflection coefficient. Guidelines on selecting the optimal local sensor-level threshold are also provided.