Energy Efficient and QoS-Aware Tasks Allocation for Sensor Networks

Collaborative processing among sensors to fulfill given tasks is a promising solution to save significant energy in resource-limited wireless sensor networks (WSN). Quality-of-Service (QoS) such as lifetime and latency is largely affected by how tasks are mapped to sensors in network. Tasks allocation is a well-defined problem in the area of high performance computing and has been extensively studied in the past. Due to the limitations of WSN, existing algorithms cannot be directly used. In this paper, a novel nested optimization technique based on genetic algorithm is proposed to assign tasks onto sensors with minimal cost while meeting application's QoS requirements. Optimal solution can be achieved by incorporating task mapping, routing path allocation, communication scheduling, dynamic voltage scaling. Performance is evaluated through experiments with randomly generated Directed Acyclic Graphs (DAG) and experiments results show better solution compared with existing methods.