Hybrid gravitational search algorithm based model for optimizing coverage and connectivity in wireless sensor networks

Abstract

Recently, the wireless sensor networks (WSNs) found its extensive application in surveillance and target tracking. For these two WSN applications, connectivity and coverage play a major role most particularly for target tracking. A large number of available sensor nodes track targets, during which a massive redundant data gets generated, which may minimize the system performance. Most particularly during the sensor node failure, the major intention of coverage and connectivity optimization model is to select less number of sensor nodes with maximum direct sensor node connectivity. But existing algorithms fail to achieve minimal node selection, therefore to mitigate the barriers of the traditional coverage algorithms, this paper proposed the hybrid Gravitational Search algorithm with social ski-driver (GSA-SSD) based model. This hybrid approach in target based WSN optimizes the coverage and connectivity requirement. By adapting the dynamic behaviour of SSD algorithm, the performance of GSA gets improved. Finally, the relative performance of the proposed hybrid GSA-SSD based optimization model is validated and compared with other optimization algorithms. On the basis of uncovered area rate and a number of sensor nodes the performance is evaluated. The results are implemented in the MATLAB simulation tool. Further, the performance enhancement in terms of uncovered area rate, number of selected active sensors, energy consumption, connectivity and network lifetime is achieved with randomly deployed nodes.