Physarum optimization: A biology-inspired algorithm for minimal exposure path problem in wireless sensor networks

Using insights from biological processes could help to design new optimization techniques for long-standing computational problems. This paper exploits a cellular computing model in the slime mold physarum polycephalum to solve the minimal exposure path problem which is a fundamental problem corresponding to the worst-case coverage in wireless sensor networks. We first formulate the minimal exposure path problem, and then convert it into the shortest path problem by discretizing the monitoring field to a large-scale weighted grid. Inspired by the path-finding capability of physarum, we develop a new optimization algorithm, named as the physarum optimization, for solving the shortest path problem. Our proposed algorithm is with low-complexity and high-parallelism. Moreover, the core mechanism of our physarum optimization is also helpful for designing new graph algorithms and improving routing protocols and topology control in self-organized networks.