Distributed algorithms for lifetime maximization in sensor networks via Min–Max spanning subgraphs

We consider the problem of static transmission-power assignment for lifetime maximization of a wireless sensor network with stationary nodes operating in a data-gathering scenario. Using a graph-theoretic approach, we propose two distributed algorithms, MLS and BSpan, that construct spanning trees with minimum maximum (minmax) edge cost. MLS is based on computation of minmax-cost paths from a reference node, while BSpan performs a binary search over the range of power levels and exploits the wireless broadcast advantage. We also present a simple distributed method for pruning a graph to its Relative Neighborhood Graph, which reduces the worst-case message complexity of MLS under natural assumptions on the path-loss. In our network simulations both MLS and BSpan significantly outperform the recently proposed Distributed Min–Max Tree algorithm in terms of number of messages required.