Maximum lifetime data sensing and extraction in energy constrained networked sensor systems

We focus on data gathering problems in energy constrained networked sensor systems. The system operates in rounds where a subset of the sensors generate a certain number of data packets during each round. All the data packets need to be transferred to the base station. The goal is to maximize the system lifetime in terms of the number of rounds the system can operate. We show that the above problem reduces to a restricted flow problem with quota constraint, flow conservation requirement, and edge capacity constraint. We further develop a strongly polynomial time algorithm for this problem, which is guaranteed to find an optimal solution. We then study the performance of a distributed shortest path heuristic for the problem. This heuristic is based on self-stabilizing spanning tree construction and shortest path routing methods. In this heuristic, every node determines its sensing activities and data transfers based on locally available information. No global synchronization is needed. Although the heuristic cannot guarantee optimality, simulations show that the heuristic has good average case performance over randomly generated deployment of sensors. We also derive bounds for the worst case performance of the heuristic.