  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 targets 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 targets
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
(e.g., formed by node failures or non-uniform node 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 comparatively evaluated against existing approaches through Monte
Carlo simulations which demonstrate its superiority in terms of tracking
performance, network-resilience and network-lifetime.
