Improved genetic algorithm-based sensor nodes deployment for barrier coverage

摘要

Barrier coverage is widely used for an intruder detection. However, sensor nodes (SNs) are prone to failure. Hence it is very challenging to construct a barrier with an efficient coverage and connectivity with minimum number of SNs. From a given set of potential positions (PPs), finding minimum number of PPs for the placement of SNs to form a barrier is an NP-complete problem. In this paper, we propose an improved genetic algorithm (GA)-based approach to solve the aforesaid problem. For the better performance and fast convergence of the algorithm, a novel mutation operation is introduced. In our proposed approach chromosomes are efficiently represented along with an efficient fitness function to evaluate the quality. An extensive simulation is conducted on the various scenarios of the network. The efficiency of the proposed algorithm is shown by comparing the simulated results with traditional genetic algorithm (GA), differential evolution (DE) and GreedyCSC algorithms.