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GBG Approach for Connectivity and Coverage Control in Wireless Sensor Network

sensor network are used to monitor various physical environment. These environments are mostly highly asymmetric terrains. The deployment of the sensor node for coverage and connectivity cost should be optimized. Therefore, in sensor network, providing high connectivity and coverage becomes the critical issue. In the proposed work, coverage and connectivity is a minimization of cost function as square of the distance between the nodes. This has been solved for cost optimization which is based on gradient methods. This modified protocol provides a genetic framework. We assume that the backbone nodes are connected to each and they provide coverage to the sensors on the basis of fitness function. The protocol performing better on the basis of cost which is also optimizing both network coverage and backbone connectivity. Our formalization allows the design of self-organized network system, which achieves minimum cost configurations. We have presented simulation results that show the effectiveness of genetic algorithm to provide network configuration that optimize both network coverage and backbone connectivity in different scenarios. We have simulated the Genetic Based Gradient (GBG) method and the proposed work in done on MATLAB.