Cellular Automaton Based Motion Planning Algorithms for Mobile Sensor Networks

We develop a set of probabilistic and deterministic cellular automaton based algorithms for an optimization problem of mobile wireless sensor networks (MWSN). We consider a scenario where the sensors are initially randomly distributed and the mobile sensors need to disperse autonomously to both maximize coverage of the network and to maintain connectivity. We perform extensive simulations of both deterministic and randomized variants of the algorithm and argue that randomized algorithms have better overall performance. Cellular automaton algorithms rely only on local information about the network and, hence, they can be used in practice for MWSN problems. On the other hand, locality of the algorithm implies that maintaining connectivity becomes a non-trivial problem.