Mobility

SILENCE: Distributed Adaptive Sampling for Sensor-Based Autonomic Systems

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Executive Summary

Adaptive sampling and sleep scheduling can help realize the much needed resource efficiency in densely deployed autonomic sensor-based systems that monitor and reconstruct physical or environmental phenomena. This paper presents a data-centric approach to distributed adaptive sampling aimed at minimizing the communication and processing overhead in autonomic networked sensor-based systems. The proposed solution exploits the spatio-temporal correlation in sensed data and eliminates redundancy in transmitted data through selective representation without compromising on accuracy of reconstruction of the monitored phenomenon at a remote monitor node. In addition, the solution also exploits the same correlations for adaptive sleep scheduling aimed at saving energy in Wireless Sensor Networks (WSNs) while also providing a mechanism for ensuring connectivity to the monitor node.

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