An efficient neural network optimized by fruit fly optimization algorithm for user equipment association in software‐defined wireless sensor network

Software‐defined wireless sensor network (SDWSN) has aroused great interest due to its new operating characteristics in recent years. For instance, centralized user association control, full network state information perception, and seamless switching. Furthermore, with the development of the Internet of things(IoT), all sensors and user equipment (UE) will be connected. The proposal of the Internet of everything concept has expanded the scope of SDWSN. It is a very challenging issue to ensure the quality of experience(QoE) while achieving load balancing when associating the UE and the base station (BS). Based on the new features of SDWSN, this paper studies a multi‐objective optimization model under multiple constraints in high‐density situations. The goal is to minimize the number of unsatisfied UEs and maximize system throughput by achieving load balancing. This problem was solved by employing an artificial neural network (ANN), which was optimized by the fruit fly optimization algorithm (FOA). This ANN–FOA scheme greatly reduced the execution time and significantly promoted the result of the multi‐objective optimization model. Through simulation, we verified the effectiveness of our proposed ANN–FOA scheme, and the influence of different sample quantity and different ANN architecture on the final result was explored.