Highly-resilient, energy-efficient multipath routing in wireless sensor networks
ACM SIGMOBILE Mobile Computing and Communications Review
A coverage-preserving node scheduling scheme for large wireless sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
PEAS: A Robust Energy Conserving Protocol for Long-lived Sensor Networks
ICDCS '03 Proceedings of the 23rd International Conference on Distributed Computing Systems
Differentiated surveillance for sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
An adaptive energy-efficient MAC protocol for wireless sensor networks
Proceedings of the 1st international conference on Embedded networked sensor systems
Network coverage using low duty-cycled sensors: random & coordinated sleep algorithms
Proceedings of the 3rd international symposium on Information processing in sensor networks
Towards optimal sleep scheduling in sensor networks for rare-event detection
IPSN '05 Proceedings of the 4th international symposium on Information processing in sensor networks
Energy efficient adaptive sensing for dynamic coverage in wireless sensor networks
WCNC'09 Proceedings of the 2009 IEEE conference on Wireless Communications & Networking Conference
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One of the most important issues in a wireless sensor network is energy efficiency, in order to extend the lifetime of the network. An effective strategy is to turn off the redundant sensor nodes in the network to spare energy. In this paper, we propose and analyze an adaptive regression algorithm for dynamic environments that can continuously monitor two arbitrary sensors in a sensor field and decide on whether they can be mutually described by non isotonic linear relation, within a user specified error bound, or not. This is done without the need of offline pre-computations, dedicated phases, or base station assistance; thus, it can be utilized in fully distributed manner. The algorithm can dynamically eliminate the redundancy and estimate the deficient data based on the learned relations in a way to ensure that the sensors' energy consumption is near minimal and balanced. We compare our technique with deterministic clustering methods, provide a parameter sensitivity analysis and discuss the simulation results.