Description

Wireless sensor networks (WSNs) are networks of autonomous nodes used for monitoring an environment. Developers of WSNs face challenges that arise from communication link failures, memory and computational constraints, and limited energy. Wireless sensor networks (WSNs) have been successfully adopted in many strategic applications such as target tracking, surveillance and classification. Coverage and target detection probability are the two most significant factors for the performance of WSNs, which are determined by dynamic deployment algorithms. In initial deployment, randomness is often adopted which, however, always does not lead to effective coverage. The effectiveness of wireless sensor networks (WSNs) depends on the coverage and target detection probability provided by dynamic deployment. Applications for wireless sensor networks may be decomposed into the deployment of tasks on different sensor nodes in the network. Task allocation algorithms assign these tasks to specific sensor nodes in the network for execution. Given the resource-constrained and distributed nature of wireless sensor networks (WSNs), existing static (offline) task scheduling may not be practical. Therefore there is a need for an adaptive task allocation scheme that accounts for the characteristics of the WSN environment such as unexpected communication delay and node failure. In this project, the system proposed a modified version of binary particle swarm optimization (MBPSO) for task allocation.