An energy-efficient data delivery scheme for delay-sensitive traffic in wireless sensor networks
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We propose a novel data-delivery method for delay-sensitive traffic that significantly reduces the energy consumption in wireless sensor networks without reducing the number of packets that meet end-to-end real-time deadlines. The proposed method, referred to as SensiQoS, leverages the spatial and temporal correlation between the data generated by events in a sensor network and realizes energy savings through application-specific in-network aggregation of the data. SensiQoS maximizes energy savings by adaptively waiting for packets from upstream nodes to perform in-network processing without missing the real-time deadline for the data packets. SensiQoS is a distributed packet scheduling scheme, where nodes make localized decisions on when to schedule a packet for transmission to meet its end-to-end real-time deadline and to which neighbor they should forward the packet to save energy. We also present a localized algorithm for nodes to adapt to network traffic to maximize energy savings in the network. Simulation results show that SensiQoS improves the energy savings in sensor networks where events are sensed by multiple nodes, and spatial and/or temporal correlation exists among the data packets. Energy savings due to SensiQoS increase with increase in the density of the sensor nodes and the size of the sensed events. © 2010 Harshavardhan Sabbineni and Krishnendu Chakrabarty.
Published Version (Please cite this version)10.1155/2010/792068
Publication InfoSabbineni, H; & Chakrabarty, K (2010). An energy-efficient data delivery scheme for delay-sensitive traffic in wireless sensor networks. International Journal of Distributed Sensor Networks, 2010. pp. 792068. 10.1155/2010/792068. Retrieved from https://hdl.handle.net/10161/4320.
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John Cocke Distinguished Professor of Electrical and Computer Engineering
Krishnendu Chakrabarty is the John Cocke Distinguished Professor of Electrical and Computer Engineering and Professor of Computer Science at Duke University. He is Chair of the Department of Electrical and Computer Engineering.