Design a mobile sensor system based on bee colony optimization: modeling and simulation

Mobile Wireless Sensor Networks (MWSN) plays a central role in modern artificial intelligence-based systems, as they corresponds to the part that senses data and equal human senses in their work. The parts responsible for converting the environmental signal into a digital signal are the sensors. The higher the quality of the sensors, the greater the readability. Because of this fame, the wireless sensor network has become a research field for a lot of research that has worked to develop both the work and performance of wireless sensor networks through the use of artificial intelligence algorithms. This research paper discusses the effect of using beehive algorithms, which is one of the swarm intelligence algorithms within natural computing. This paper discusses the process of creating a three-dimensional simulation model to simulate the MWSN system based on BCO, as well as a simulation model for the network itself. The simulation was tested for the new system and the communication performance of MWSN was improved by adding AI to the working principle of each component of the system. The simulation results were compared with similar protocols to show the extent of the impact of the BCO algorithm on the overall performance of the network. It was found that the use of the BCO algorithm gives MWSN more efficiency in many ways, depending on the indicators of the efficiency of wireless networks. The model was named IAANET for short, which means Intelligent Aerial Ad-hoc Network because the aerial components within MWSN is the optimization target.