Optimizing Actuators Deployment for WSAN Using Hierarchical Intermittent Communication Particle Swarm Optimization

In wireless sensor networks, the energy hole problem severely reduces network lifetime, which is an issue that needs to be urgently solved. This problem has been alleviated to some certain extent by wireless sensor and actuator networks that introduce actuators. Actuators can act as collectors to gather data from the sensor nodes of the corresponding sub-sensing regions. However, the deployment of actuators has a significant impact on energy hole problem. Only when actuators reasonably cover the sensor nodes can the energy hole issue be effectively solved. Hence, it is very important to investigate the deployment of actuators. Moreover, the distribution of sensor nodes is usually random and non-uniform, and the complexity of sensor nodes distribution increases the difficulty in determining the deployment solution of actuators. This study optimizes the deployment of actuators using hierarchical intermittent communication particle swarm optimization (HICPSO) method. The coverage rate of actuators to sensor nodes and the energy consumption rate of sensor nodes are considered as optimization goals, which can balance the energy consumption among sensor nodes and solve energy hole problem. The HICPSO method is proposed to improve the performance of particle swarm optimization method by distributing the population into a hierarchical structure and maintaining the independent development of particles via intermittent learning from upper hierarchies. The results show that the proposed method can effectively increase the coverage rate of actuators to sensor nodes, reduce the energy consumption rate of the sensor nodes, and reduce the packet drop ratio.