Reliability vs. efficiency in distributed source coding for field-gathering sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
All of Nonparametric Statistics (Springer Texts in Statistics)
All of Nonparametric Statistics (Springer Texts in Statistics)
Stability of Kalman filtering with Markovian packet losses
Automatica (Journal of IFAC)
Maximum Likelihood Localization of a Diffusive Point Source Using Binary Observations
IEEE Transactions on Signal Processing
IEEE Communications Magazine
The critical-square-grid coverage problem in wireless sensor networks is NP-Complete
Computer Networks: The International Journal of Computer and Telecommunications Networking
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This paper studies the effect of wireless channel imperfections on the transport and estimation of spatially distributed events using wireless sensor networks (WSNs). It is observed that the quality of event estimation at the sink (fusion center) degrades considerably with correlated packet losses during transmission from the sensors. A novel diversity technique based on field estimation is proposed to mitigate the effects of packet losses on the quality of estimation at the sink. Dense deployment of sensor nodes and the spatial nature of the observed physical phenomenon result in the sensor observations being noisy spatial samples of an unknown underlying function. The proposed algorithm exploits this feature, using supervised learning to achieve diversity. A new information fusion methodology based on approximate likelihood is proposed to integrate the information obtained from the learning algorithm into the classical estimation framework. Simulation results are provided to demonstrate the performance of the proposed approach.