Framework of Meta-Heuristic Based Computational Load Balancing in Wireless Sensor Network
In the recent era, the emerging of wireless sensor networks has motivated researchers to develop Wireless Sensor Network (WSN) management algorithm for various applications. One of the applications is the computational load balancing which aims at optimizing the computational execution of tasks and processes on the network to meet various performance measures. Most importantly, the energy consumption, the execution time and the balancing of load among the nodes in order to prolong the lifetime. Hence, researchers have developed models and optimization approaches for this goal. Numerous meta heuristic approaches were used with various arrangement and problem encoding. Having a unified framework for that is needed to make the problem solving more systematic. In this article, we propose a framework for meta-heuristic based optimization of computational load balancing in WSN. Evaluation using both genetic algorithm and particle swarm optimization has provided the superiority of the latter. The framework accepts testing on other meta-heuristic approaches without any restriction.