Probabilistic Distributed Algorithms for Energy Efficient Routing and Tracking in Wireless Sensor Networks

In this work we focus on the energy efficiency challenge in wireless sensor networks, from both an on-line perspective (related to routing), as well as a network design perspective (related to tracking). We investigate a few representative, important aspects of energy efficiency: a) the robust and fast data propagation b) the problem of balancing the energy dissipation among all sensors in the network and c) the problem of efficiently tracking moving entities in sensor networks. Our work here is a methodological survey of selected results that have already appeared in the related literature. In particular, we investigate important issues of energy optimization, like minimizing the total energy dissipation, minimizing the number of transmissions as well as balancing the energy load to prolong the system's lifetime. We review characteristic protocols and techniques in the recent literature, including probabilistic forwarding and local optimization methods. We study the problem of localizing and tracking multiple moving targets from a network design perspective i.e. towards estimating the least possible number of sensors, their positions and operation characteristics needed to efficiently perform the tracking task. To avoid an expensive massive deployment, we try to take advantage of possible coverage overlaps over space and time, by introducing a novel combinatorial model that captures such overlaps. Under this model, we abstract the tracking network design problem by a covering combinatorial problem and then design and analyze an efficient approximate method for sensor placement and operation.