Path planning for semi-flocking-controlled mobile sensor networks on mobility maps

Abstract

Mobile sensor networks (MSNs) are useful in monitoring outdoor environments. Semi-flocking algorithms have been proven to be efficient in controlling MSNs in area sensing and target tracking applications. Even though outdoor environments may consist of irregular regions with different traverse costs, existing semi-flocking algorithms assume an area of interest (AoI) to be regular and with uniform costs. Such an assumption limits target tracking capabilities of a MSN. In this work, we model operating terrains using a mobility map that incorporates the speed limits of different sub-regions. To gather the required number of mobile sensor nodes for target tracking, an A∗ heuristic search algorithm is used to find time-efficient paths connecting nodes to targets. Nodes can acquire navigation information using the A∗-based path planning module and then traverse along the time-efficient paths until they reach the targets. Simulation results verify the effectiveness of the proposed method over an existing semi-flocking algorithm.