Set k-cover algorithms for energy efficient monitoring in wireless sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
Error Correction Coding: Mathematical Methods and Algorithms
Error Correction Coding: Mathematical Methods and Algorithms
Near-optimal sensor placements: maximizing information while minimizing communication cost
Proceedings of the 5th international conference on Information processing in sensor networks
SmartGossip: an improved randomized broadcast protocol for sensor networks
Proceedings of the 5th international conference on Information processing in sensor networks
Networking Wireless Sensors
DESYNC: self-organizing desynchronization and TDMA on wireless sensor networks
Proceedings of the 6th international conference on Information processing in sensor networks
Sensor Selection for Minimizing Worst-Case Prediction Error
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Leveraging redundancy in sampling-interpolation applications for sensor networks
DCOSS'07 Proceedings of the 3rd IEEE international conference on Distributed computing in sensor systems
Early overhearing avoidance in wireless sensor networks
NETWORKING'08 Proceedings of the 7th international IFIP-TC6 networking conference on AdHoc and sensor networks, wireless networks, next generation internet
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We focus on dense networks of wireless sensors used for distributed sampling and interpolation. Sensors periodically sample a physical quantity of interest, e.g. temperature, and report their measurements to a data center. A continuous spatial estimate of the quantity can then be constructed through interpolation. In these cases, density in deployment can be exploited to achieve longer operational time for the network. The challenge is how to select multiple disjoint subsets of sensors such that each of them is individually capable of meeting the quality demands of the monitoring application. In this paper we present an efficient sensor selection scheme for the min-max monitoring application, i.e., minimizing the maximum distortion incurred over space through interpolation. Evaluation with synthetic sensor network data shows that significant reductions in the number of active sensors and energy consumption are possible compared to simpler selection methods.