Author(s): Zhilbert Tafa
Abstract: In many border surveillance applications (such as military, homeland security, etc.), the wireless sensor networks cannot be deployed manually and the barrier coverage breaks can appear along a given surveillance line. This paper introduces a cluster-based algorithm and new metrics to determining the number and the positions of the additional nodes needed to be deployed by drones, robots, or moved in a network; in order to fill the gaps in a randomly deployed network. Simulation results show that the proposed algorithm optimizes the number of additional nodes and outperforms the alternative in 52,15% of cases while it performs similarly in the rest of the cases. The machine learning classification algorithms are used to show that the decision on choosing one or another algorithm is highly classifiable. Precisely, the proposed algorithm is shown to be the approach of choice in implementations where the sensing range is relatively small.