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Task Allocation for Wireless Sensor Network Using Modified Binary Particle Swarm Optimization

Many applications of wireless sensor network (WSN) require the execution of several computationally intense in-network processing tasks. Collaborative in-network processing among multiple nodes is essential when executing such a task due to the strictly constrained energy and resources in single node. Task allocation is essential to allocate the workload of each task to proper nodes in an efficient manner. In this paper, a modified version of binary particle swarm optimization (MBPSO), which adopts a different transfer function and a new position updating procedure with mutation, is proposed for the task allocation problem to obtain the best solution. Each particle in MBPSO is encoded to represent a complete potential solution for task allocation. The task workload and connectivity are ensured by taking them as constraints for the problem. Multiple metrics, including task execution time, energy consumption, and network lifetime, are considered a whole by designing a hybrid fitness function to achieve the best overall performance. Simulation results show the feasibility of the proposed MBPSO-based approach for task allocation problem in WSN. The proposed MBPSO-based approach also outperforms the approaches based on genetic algorithm and BPSO in the comparative analysis.