Comparison of Hyper-DAG Based Task Mapping and Scheduling Heuristics for Wireless Sensor Networks

In-network processing emerges as an approach to reduce energy consumption in Wireless Sensor Networks (WSN) by decreasing the overall transferred data volume. Parallel processing among sensors is a promising approach to provide the computation capacity required by in-network processing methods. In this paper, Hyper-DAG based Mapping and Scheduling (HDMS) algorithms for energy constrained WSNs are introduced. The design objective of these algorithms is to minimize schedule lengths subject to energy consumption constraints. Simulation results show that the CNPT-based HDMS algorithm outperforms other heuristic algorithms with respect to schedule lengths and heuristic execution times subject to energy consumption constraints.