Models of massive parallelism: analysis of cellular automata and neural networks
Models of massive parallelism: analysis of cellular automata and neural networks
Mobility improves coverage of sensor networks
Proceedings of the 6th ACM international symposium on Mobile ad hoc networking and computing
Mobile Wireless Sensor Network: Architecture and Enabling Technologies for Ubiquitous Computing
AINAW '07 Proceedings of the 21st International Conference on Advanced Information Networking and Applications Workshops - Volume 02
Distributed Deployment Schemes for Mobile Wireless Sensor Networks to Ensure Multilevel Coverage
IEEE Transactions on Parallel and Distributed Systems
On the Universal Computing Power of Amorphous Computing Systems
Theory of Computing Systems - Special Issue: Computation and Logic in the Real World; Guest Editors: S. Barry Cooper, Elvira Mayordomo and Andrea Sorbi
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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.