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WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
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HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
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ACM Transactions on Embedded Computing Systems (TECS)
Medium access control with coordinated adaptive sleeping for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
Computer
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PERCOMW '06 Proceedings of the 4th annual IEEE international conference on Pervasive Computing and Communications Workshops
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Z-MAC: a hybrid MAC for wireless sensor networks
IEEE/ACM Transactions on Networking (TON)
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APWeb'06 Proceedings of the 2006 international conference on Advanced Web and Network Technologies, and Applications
IEEE Communications Magazine
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ACM Transactions on Autonomous and Adaptive Systems (TAAS)
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International Journal of Sensor Networks
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International Journal of Sensor Networks
Improved minimum latency aggregation scheduling in wireless sensor networks under the SINR model
International Journal of Sensor Networks
Secure and energy-efficient data aggregation for wireless sensor networks
International Journal of Mobile Network Design and Innovation
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In this paper, we introduce and evaluate a data collection and transmission paradigm in Wireless Sensor Networks (WSNs), where the primary objective is energy efficiency, while at the same time it is flexible and adaptive to support various Quality of Service (QoS) application constraints. The proposed framework combines two different energy-saving methods, one at the application layer, namely data aggregation, and one at the Medium Access Control (MAC) layer by appropriately adopting sleeping mechanisms. This is achieved by following a cross-layer approach where local information about the status of each sensor, including its sleep/awake schedules, feeds a subsequent data aggregation phase, which gathers correlated data and aggregates it to a single packet towards its way to the sink by applying a fully localised, distributed, probabilistic method. Simulation results demonstrate the significant energy savings achieved by our proposed framework, especially when compared to conventional MAC layer approaches and/or data aggregation approaches.