Latency and Energy-Consumption Optimized Task Allocation in Wireless Sensor Networks

Emerging applications in Wireless Sensor Networks (WSNs) demand notable in-network processing capacities rather than simple data gathering and dissemination. Therefore, the performances of the network like latency and energy consumption are greatly affected by how the various application requirements are mapped to the processing nodes in the network. This paper investigates intelligent task mapping and scheduling techniques based on Genetic Algorithm (GA), and proposes a novel task allocation model and a multi-hop communication model to schedule both computation and communication activities in the WSN environment. A hybrid fitness function which balances the energy consumption among collaborative sensor nodes with application tolerable delays is presented to extend the network lifetime. Simulation results show that the proposed algorithm has a better capability of balancing the network lifetime with latency constraints in both homogeneous and heterogeneous networks.