Abstract: Energy conservation is a major issue in the management of sensor networks. In this paper, we consider the problem of optimising the performance of energy-constrained sensor networks in the context of event-detection applications, with the performance metric being a weighted average of the numbers of missed events and false alarms. We propose two adaptive event-driven techniques, both simple to implement at the node level, that change the nodes' duty cycles based on the patterns of detected events. Simulation results show that this approach is effective in both cutting down wasteful energy consumption as well as in improving the detection performance in applications characterised by long periods of quiescence followed by bursts of activity. Keywords: sensor networks; environment monitoring; adaptive duty-cycling; MDP; Markov decision process; energy conservation; event clustering. Reference to this paper should be made as follows: Sundaresan, S., Koren, I., Koren, Z. and Krishna, C.M. (2009) C. Mani Krishna is a Professor of Electrical and Computer Engineering at the University of Massachusetts. He received his PhD in Electrical Engineering at the University of Michigan and has been at UMass ever since. His principal interests are in real-time, fault-tolerant and distributed systems, as well as sensor networks. He has co-authored two texts on real-time systems and fault-tolerant computing. His recent research includes projects in secure sensor networks, resource provisioning in modern deeply-pipelined processors, power-aware issues in real-time systems, parameter variation problems in nanotechnology, and the efficient design of cyber-physical systems. He is a Fellow of the IEEE.
S Sundaresan et al. REF presented an event-driven scheme based on Markov decision process (MDP) theory to adjust the wake-sleep pattern of sensors.
Event-driven adaptive duty-cycling in sensor networks
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Computer Science