Energy Efficient Distributed Algorithms for Sensor Target Coverage Based on Properties of an Optimal Schedule
A major challenge in Wireless Sensor Networks is that of maximizing the lifetime while maintaining coverage of a set of targets, a known NP-complete problem. In this paper, we present theoretically-grounded, energy-efficient, distributed algorithms that enable sensors to schedule themselves into sleep-sense cycles. We had earlier introduced a lifetime dependency (LD) graph model that captures the interdependencies between these cover sets by modeling each cover as a node and having the edges represent shared sensors. The key motivation behind our approach in this paper has been to start with the question of what an optimal schedule would do with the lifetime dependency graph. We prove some basic properties of the optimal schedule that relate to the LD graph. Based on these properties, we have designed algorithms which choose the covers that exhibit these optimal schedule like properties. We present three new sophisticated algorithms to prioritize covers in the dependency graph and simulate their performance against state-of-art algorithms. The net effect of the 1-hop version of these three algorithms is a lifetime improvement of more than 25-30% over the competing algorithms of other groups, and 10-15% over our own; the 2-hop versions have additional improvements, 30-35% and 20-25%, respectively.
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