Task mapping and scheduling in wireless sensor networks

Collaborative processing among sensors to fulfill given tasks is a promising solution to save significant energy in resource - limited wireless sensor networks. Quality of Service such as lifetime and latency is largely affected by how tasks are mapped to sensors in a network. Due to the limitations of wireless sensor networks, existing algorithms cannot be directly used. This paper presents an efficient allocating algorithm that allocates a set of real-time tasks with dependencies onto a sensor network. The proposed algorithm comprises linear task clustering algorithm and sensor assignment mechanism based on a task duplication and migration scheme. It simultaneously schedules the computation tasks and associated communication events of real time applications. It reduces inter-task communication costs and moderates local communication overhead incurred due to communication medium contention. Performance is evaluated through experiments with both randomly generated Directed Acyclic Graph (DAG) and real-world applications. Simulated results and qualitative comparisons with the most related literature, Multi-Hop Task Mapping and Scheduling (MTMS), Distributed Computing Architecture (DCA), and Energy-Balance Task Allocation (EBTA), demonstrated that the proposed scheme significantly surpasses the other approaches in terms of deadline missing ratio, schedule length, and total application energy consumption.