Splitting–merging-based automatic scheduling scheme for balancing energy consumption and task allocation in WSANs

Abstract

In wireless sensor and actuator networks (WSANs), sensing region partition, task allocation, and collector schedule greatly affect the performance of WSANs. Specifically, nonuniform data aggregation causes unbalanced energy consumption, thereby reducing the network lifetime. Additionally, unbalanced allocation of data collection tasks of collectors increases the gathering delay. Thus, it is critical to balance the energy consumption of sensor nodes, as well as task allocation and scheduling with the limited number of collectors. This study proposes a novel splitting–merging-based automatic scheduling scheme to balance energy consumption and task allocation for WSANs. First, a sensing region splitting–merging scheme is proposed to make the data load uniform, in which the sensing region is self-organized into many subregions taking into account the maximum number of sensor nodes and the maximum distance between sensor nodes in each subregion. Second, a task allocation strategy based on genetic algorithm is proposed to optimize the number of collectors and uniformly assign tasks to collectors. Finally, the trajectory of each collector is optimized by applying ant colony optimization algorithm. Numerical experiments show that the proposed method outperforms well in terms of prolonging the network lifetime and reducing the gathering delay when compared with several related methods.