Adaptive signal processing
Wireless sensor networks for habitat monitoring
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Distributed detection and fusion in a large wireless sensor network of random size
EURASIP Journal on Wireless Communications and Networking
SenSlide: a sensor network based landslide prediction system
Proceedings of the 3rd international conference on Embedded networked sensor systems
Deploying a Wireless Sensor Network on an Active Volcano
IEEE Internet Computing
Proceedings of the 4th international conference on Mobile systems, applications and services
Learning in decentralized systems: a nonparametric approach
Learning in decentralized systems: a nonparametric approach
An efficient multibit aggregation scheme for multihop wireless sensor networks
EURASIP Journal on Wireless Communications and Networking
Channel aware decision fusion in wireless sensor networks
IEEE Transactions on Signal Processing
Fusion of decisions transmitted over Rayleigh fading channels in wireless sensor networks
IEEE Transactions on Signal Processing
An application-specific protocol architecture for wireless microsensor networks
IEEE Transactions on Wireless Communications
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The problem of decision fusion for event detection in Wireless Sensor Networks is the prime focus of this paper. Our proposed algorithm focuses on single hop (star) and multihop (tree) topologies, which are commonly deployed wireless sensor network topologies. In order to minimize the overall energy consumption in the network, a transmission constraint of one-bit is imposed on each sensor node. This poses a challenging problem of designing a one-bit decision fusion rule at every fusion center, which improves the overall detection accuracy at the sink node. The absence of apriori knowledge of each sensor's local performance indices, makes the existing optimum fusion rule infeasible. Moreover, in the absence of a training sequence of true event occurrences, existing Adaptive distributed detection techniques also become inapplicable. In this setup, the key contribution of this paper is a Least Mean Squares based Blind Adaptive Weighted Aggregation Scheme (Blind-AdWAS) for Wireless Sensor Networks with tree topology. We extend our earlier work (Jagyasi et al. in Proceedings of 11th international symposium on wireless personal multimedia communication, 2008) to include an analysis of the effect of Rayleigh flat fading channel on Blind-AdWAS in comparison with existing channel-aware optimum and sub- optimum aggregation schemes. Even in the absence of any channel knowledge or knowledge of performance indices, Blind-AdWAS demonstrates robustness in event detection performance.