Actuators task assignment algorithm and its application for WSAN

Abstract

In the wireless sensor actuator network (WSAN), in order to make the sensor nodes (S) and the actuator nodes (A) work together more efficiently and obtain more accurate assignment information of actuators, a novel data fusion model of actuators assignment were constructed. In this paper, the weights and thresholds of BP neural network (BPNN) were optimized by genetic algorithm (GA), and the GA-BPNN model was applied to prefabricated substation. In order to verify the characteristics of the model, the simulation and analysis of GA-BPNN were carried out comparing with the traditional BPNN data fusion model. The results demonstrate that the running time of the whole system can greatly be reduced, and the efficiency of operation and the correctness of the actuator nodes task assignment information can also be improved by using GA-BPNN task assignment data fusion model.