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The paper presents a distributed algorithm, called Prediction-basedOpportunistic Sensing for Resilient and Efficient Sensor Networks (POSE.R),where the sensor nodes utilize predictions of the targets positions toprobabilistically control their multi-modal operating states to track thetarget. There are two desired features of the algorithm: energy-efficiency andresilience. If the target is traveling through a high node density area, thenan optimal sensor selection approach is employed that maximizes a joint costfunction of remaining energy and geometric diversity around the targetsposition. This provides energy-efficiency and increases the network lifetimewhile 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(e.g., formed by node failures or non-uniform node deployment), then apotential game is played amongst the surrounding nodes to optimally expandtheir sensing ranges via minimizing energy consumption and maximizing targetcoverage. This provides resilience, that is the self-healing capability totrack the target in the presence of low node densities and coverage gaps. Thealgorithm is comparatively evaluated against existing approaches through MonteCarlo simulations which demonstrate its superiority in terms of trackingperformance, network-resilience and network-lifetime.

POSE.R: Prediction-based Opportunistic Sensing for Resilient and Efficient Sensor Networks