An Energy-Efficient Data Delivery Scheme for Delay-Sensitive Traffic in Wireless Sensor Networks

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

Scalable Coverage Maintenance for Dense Wireless Sensor Networks

Owing to numerous potential applications, wireless sensor networks have been attracting significant research effort recently. The critical challenge that wireless sensor networks often face is to sustain long-term operation on limited battery energy. Coverage maintenance schemes can effectively prolong network lifetime by selecting and employing a subset of sensors in the network to provide sufficient sensing coverage over a target region. We envision future wireless sensor networks composed of a vast number of miniaturized sensors in exceedingly high density. Therefore, the key issue of coverage maintenance for future sensor networks is the scalability to sensor deployment density. In this paper, we propose a novel coverage maintenance scheme, scalable coverage maintenance (SCOM), which is scalable to sensor deployment density in terms of communication overhead (i.e., number of transmitted and received beacons) and computational complexity (i.e., time and space complexity). In addition, SCOM achieves high energy efficiency and load balancing over different sensors. We have validated our claims through both analysis and simulations