POSE.R: Prediction-based Opportunistic Sensing for Resilient and Efficient Sensor Networks
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The paper presents a distributed algorithm, called Prediction-based Opportunistic Sensing for Resilient and Efficient Sensor Networks (POSE.R), where the sensor nodes utilize predictions of the target's positions to probabilistically control their multi-modal operating states to track the target. There are two desired features of the algorithm: energy-efficiency and resilience. If the target is traveling through a high node density area, then an optimal sensor selection approach is employed that maximizes a joint cost function of remaining energy and geometric diversity around the target's position. This provides energy-efficiency and increases the network lifetime while preventing redundant nodes from tracking the target. On the other hand, if the target is traveling through a low node density area or in a coverage gap, formed by node failures or non-uniform deployment, then a potential game is played amongst the surrounding nodes to optimally expand their sensing ranges via minimizing energy consumption and maximizing target coverage. This provides resilience, that is the self-healing capability to track the target in the presence of low node densities and coverage gaps. The algorithm is validated through extensive Monte Carlo simulations which demonstrate its superior performance as compared to the existing approaches in terms of tracking performance, network-resilience and network-lifetime.
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