A Fuzzy Congestion Control in Wireless Sensor Networks based on Spider Monkey Optimization Algorithm

Abstract

Wireless Sensor Networks (WSN) have been broadly applied in various fields, such as medicine and agriculture. A network is likely to experience information congestion when many sensors initiate sending data concurrently. This could lead to maximizing in the packet loss ratio, thereby reducing efficiency and affecting the total system performance, so congestion control is an utmost challenge. To solve this problem, a Fuzzy Congestion Control in WSN based on the Spider Monkey Optimization Algorithm (FCC-WSN-SMOA) is proposed. The proposed method combines random early detection with the fuzzy proportional integral derivative controller. The proportional integral derivative (PID) and fuzzy logic conjointly together to help control the target buffer queue. FLC regulates the transmitting rate of every node. Then, FLC input and output parameters are optimized by SMOA. The simulation is activated in MATLAB. The FCC-WSN-SMOA method attains 21.28%, 32.20%, and 17.42% lower packet loss ratio and 16.25%, 26.07%, and 23.38% lower packet loss probability compared with existing methods, such as progressive fuzzy PSO-PID congestion control approach for WSN (FCC-WSN-PSO), optimized fuzzy clustering utilizing moth-flame optimization approach in WSNs (FCC-WSN-MFOA), and time synchronization depending on Improved Wolf Colony Algorithm-Cuckoo Search Optimized Fuzzy PID Controller for Smart Grid (FCC-WSN-IWCA-CSO), respectively.