Next century challenges: scalable coordination in sensor networks
MobiCom '99 Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking
TEEN: ARouting Protocol for Enhanced Efficiency in Wireless Sensor Networks
IPDPS '01 Proceedings of the 15th International Parallel & Distributed Processing Symposium
Energy-Efficient Communication Protocol for Wireless Microsensor Networks
HICSS '00 Proceedings of the 33rd Hawaii International Conference on System Sciences-Volume 8 - Volume 8
Sensor node selection for execution of continuous probabilistic queries in wireless sensor networks
Proceedings of the ACM 2nd international workshop on Video surveillance & sensor networks
HEED: A Hybrid, Energy-Efficient, Distributed Clustering Approach for Ad Hoc Sensor Networks
IEEE Transactions on Mobile Computing
ExScal: Elements of an Extreme Scale Wireless Sensor Network
RTCSA '05 Proceedings of the 11th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications
Shuffle: an enhanced QoS control by balancing energy consumption in wireless sensor networks
GPC'10 Proceedings of the 5th international conference on Advances in Grid and Pervasive Computing
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In very large-scale dense sensor network applications, more sensor nodes may be deployed than are required to provide the initial desired spatial resolution. Such over-deployment can extend network life, improve robustness, and accommodate network dynamic. To enable large deployments, tiered and clustered network structures may be adopted for scalability and manageability. This article presents a highly scalable, distributed control method to manage the activity of sensors in each cluster so that dynamic application-specific spatial resolution requirement can be achieved with minimum energy cost and with uniform participation of sensors. The method, Look-Ahead Resolution Control (LARC), utilizes a look-ahead prediction of activities of sensors in the cluster and provides feedback to nodes as part of acknowledgments to transmissions. LARC is shown to be highly responsive to system dynamics such as changes in the resolution requested, node failures or replenishment. Existing control methods not only fall short in network life and scalability, but also do not provide such responsiveness and uniform participation to ensure a diverse representation of sensed data. LARC is extended to tradeoff between energy and delay in recovery from failures of large number of nodes, by multiplexing sleeping, listening, and transmissions probabilistically in such a way that the control overhead is minimized while the delay is bounded. The article presents the control strategy for LARC along with performance studies showing near-uniform participation and near-optimal cluster lifetime.