Xu Ning
Abstract
Idle listening is a major source of energy waste in wireless sensor networks. It can be reduced through Low-Power Listening (LPL) techniques in which a node is allowed to sleep for a significant amount of time. In contrast to conventional fixed sleep time policies, we introduce a novel dynamic sleep time control approach that further reduces control packet energy waste by utilizing known data traffic statistics. We propose two distinct approaches to dynamically compute the sleep time, depending on the objectives and constraints of the network. The first approach provides a dynamic sleep time policy that guarantees a specified average delay at the sender node resulting from packets waiting for the end of a sleep interval at the receiver. The second approach determines the optimal policy that minimizes total energy consumed. In the case where data traffic statistics are unknown, we propose an adaptive learning algorithm to estimate them online and develop corresponding sleep time computation algorithms. Simulation results are included to illustrate the use of dynamic sleep time control and to demonstrate how it dominates fixed sleep time methods. An implementation of our approach on a commercial sensor node supports the computational feasibility of the proposed approach.
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Index Terms
- Dynamic sleep time control in wireless sensor networks
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