Simulated annealing approach to optimizing the lifetime of sparse time-driven sensor networks

Time-driven sensor networks are devoted to the continuous reporting of ambient data to the base station. In many cases, these data are provided by nodes that have been deployed in a structured manner, either by selecting strategic locations or by adopting some regular sampling pattern. In either case, the resulting inter-node distances may not be small, and thus additional supporting nodes may be necessary. This suggests that the problem could be better addressed from a network planning perspective. In this sense, a particular approach is proposed and, as part of it, the paper focuses on optimizing the lifetime that can be predicted from the network topology, which is assumed to be a static data gathering tree. It is shown that this problem requires the exploration of all possible spanning trees, since the energy consumed by a node depends on its workload, which in turn depends on how this node is connected to its neighborhood. Because this is an NP-hard problem, the use of a heuristic approach is required. Then, an algorithm based on simulated annealing is proposed, which converges asymptotically to the global optimum. This algorithm is tested on different scenarios and its computational complexity is proved to be linearly dependent on the number of nodes.