Multi-objective fuzzy krill herd congestion control algorithm for WSN

Wireless Sensor Network (WSN) consists of hundreds of devices with limited resources that collect, analyze, and transmit data to a base station. The carry-send nature and inconsistent transmission rate caused network congestion. Congestion incites decreased throughput, increased packet loss, and energy depletion. The existing congestion control strategies address congestion problems but still lack performance and quality of service issues. The optimal source transmission rate helps to alleviate congestion. The article proposes a Multi-objective Fuzzy Krill Herd Algorithm (MFKHA) control network congestion by optimizing the source sending rate. This innovative multi-objective outflow rate optimization mechanism improves network performance by designing a unique probability-based data differentiation mechanism coupled with an optimal source outflow rate optimization. To minimize network congestion by achieving fast convergence, this optimization algorithm incorporates the five objectives (congestion level, inflow rate, outflow rate, bandwidth, and queue length). To validate the performance of the proposed MFKHA algorithm, extensive simulations are carried out using MATLAB. Moreover, the proposed MFKHA algorithm is compared to those of cutting-edge meta-heuristic algorithms such as ECA-HA, ACSRO, and PSOGSA. The simulation result shows that the proposed MFKHA outperformed all counterparts and specifically improved the sending rate, throughput, and fairness and friendliness index. Furthermore, it has also reduced packet loss, delay, queue size, energy usage, and congestion against ECA-HA.