Abstract:
Mobile Wireless Sensor Networks (MWSN) is an ad-hoc network that comprises
of a large number of sensors. The sensors usually have limited sensing
and communication capabilities. Recent times has seen a rapid development
in MWSN technology which has resulted in its increasing application
and transformed this sector into an important field of research. Mobility is a
key factor in the implementation of MWSN. After being deployed in the environment
the sensors need to move around to increase the coverage of the
network while maintaining connectivity. This movement is done by the individual
sensors based on local information. The use of local information means
that Cellular Automaton (CA) is suitable for developing motion planning algorithms
for MWSN. CA is a biologically inspired discrete model. Although
there are works that have used CA for developing motion planning algorithm
for MWSN they mostly used square grids which is not a good representation
of the actual MWSN model. In this paper we develop a set of probabilistic and
deterministic cellular automaton (CA)-based algorithms for motion planning
problems in MWSNs for hexagonal grid. We consider scenarios where sensors
are either explicitly or randomly placed in the environment and they need to
disperse to maximize the covered area and also maintain connectivity. We conduct
simulations for both the deterministic and probabilistic models and find
that the probabilistic model yield better results.. . .
Description:
Supervised by
Prof. Dr. Muhammad Mahbub Alam,
Department of Computer Science and Engineering,
Islamic University of Technology
