Incremental quantile estimation for massive tracking
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Versatile low power media access for wireless sensor networks
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
Simulating the power consumption of large-scale sensor network applications
SenSys '04 Proceedings of the 2nd international conference on Embedded networked sensor systems
WiseMAC: an ultra low power MAC protocol for the downlink of infrastructure wireless sensor networks
ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
X-MAC: a short preamble MAC protocol for duty-cycled wireless sensor networks
Proceedings of the 4th international conference on Embedded networked sensor systems
Adaptive Low Power Listening for Wireless Sensor Networks
IEEE Transactions on Mobile Computing
Low energy operation in WSNs: A survey of preamble sampling MAC protocols
Computer Networks: The International Journal of Computer and Telecommunications Networking
Duty-cycle optimization for IEEE 802.15.4 wireless sensor networks
ACM Transactions on Sensor Networks (TOSN)
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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.