Connectivity preserving obstacle avoidance localized motion planning algorithms for mobile wireless sensor networks

Abstract

Mobile wireless sensor networks (MWSN) are better in terms of coverage and it plays an important role in ubiquitous wireless networks. We design Cellular Automaton (CA) based localized motion planning algorithms for mobile wireless sensors. We propose cellular automaton based algorithms for both dispersion and gathering problems. The dispersion algorithm is intended for self-deployment purpose with the goal of increasing the sensing coverage of the network. We apply a probabilistic approach that maximizes the network coverage as well as maintains the connectivity of the network. In addition, after finishing the dispersion, a gathering algorithm guides the sensors to round up to a single place for collection. It is noteworthy that both algorithms are synchronous which means that all sensors run algorithms in parallel at the same time. Moreover, our algorithms allow the sensors to avert obstacles in their path of movement. As cellular automaton functions depend on the local information about the network strictly, they are suitable for MWSN in practice. We evaluate the performance of our algorithm based on some defined metrics i.e., coverage, strongly connected coverage. We find that our dispersion algorithm maintains better coverage than state-of-the-art algorithm. Furthermore, in case of synchronous gathering, sensors get disconnected for some cases to form multiple clusters while using state-of-the-art algorithm, but our proposed gathering algorithm is always able to provide the connectivity.