A Layered Energy-Efficient Multi-Node Scheduling Mechanism for Large-Scale WSN

In recent years, target tracking has been considered one of the most important applications of wireless sensor network (WSN). Optimizing target tracking performance and prolonging network lifetime are two equally critical objectives in this scenario. The existing mechanisms still have weaknesses in balancing the two demands. The proposed heuristic multi-node collaborative scheduling mechanism (HMNCS) comprises cluster head (CH) election, pre-selection, and task set selection mechanisms, where the latter two kinds of selections form a two-layer selection mechanism. The CH election innovatively introduces the movement trend of the target and establishes a scoring mechanism to determine the optimal CH, which can delay the CH rotation and thus reduce energy consumption. The pre-selection mechanism adaptively filters out suitable nodes as the candidate task set to apply for tracking tasks, which can reduce the application consumption and the overhead of the following task set selection. Finally, the task node selection is mathematically transformed into an optimization problem and the genetic algorithm is adopted to form a final task set in the task set selection mechanism. Simulation results show that HMNCS outperforms other compared mechanisms in the tracking accuracy and the network lifetime.