Biologically-inspired self-deployable heterogeneous mobile sensor networks

This paper studies the problem of self-deployment of heterogeneous mobile sensors using biologically-inspired principles and methodologies. The initial sensor deployment is assumed to be random, based on which two interrelated issues are investigated: the design of an optimal placement pattern of heterogeneous sensor platforms and the self configuration from the initial random state to the optimal state through intelligent sensor movement. We first develop an optimal placement algorithm based on the mosaic technique inspired by the retina mosaic pattern widely observed in both human and many animal visual systems. Different types of mobile sensors are organized into a mosaic pattern to maximize sensing coverage and to reduce network cost. Secondly, in order to converge to the optimal state, we investigate the swarm intelligence (SI)-based sensor movement strategy with the assistance of local communications, through which the randomly deployed sensors can self-organize themselves to reach the optimal placement state. The proposed algorithm is compared with the random movement and the SI-based method without direct communication using performance metrics such as sensing coverage, redundancy, convergence time, and energy consumption. Simulation results are presented to demonstrate the effectiveness of the mosaic placement and the SI-based movement with local communication.