Soft Computing-Based Congestion Control Schemes in Wireless Sensor Networks: Research Issues and Challenges

  • Shoorangiz Shams Shamsabad Farahani Department of Electrical Engineering, Islamshahr Branch, Islamic Azad University, Islamshahr, Iran.
Keywords: Congestion control, Game theory, Wireless Sensor Networks (WSNs), Fuzzy Logic, Learning Automata, Neural Network, , Soft Computing, Swarm Intelligence


Wireless Sensor Networks (WSNs) are a special class of wireless ad-hoc networks where their performance is affected by different factors. Congestion is of paramount importance in WSNs. It badly affects channel quality, loss rate, link utilization, throughput, network life time, traffic flow, the number of retransmissions, energy, and delay. In this paper, congestion control schemes are classified as classic or soft computing-based schemes. The soft computing-based congestion control schemes are classified as fuzzy logic-based, game theory-based, swarm intelligence-based, learning automata-based, and neural network-based congestion control schemes. Thereafter, a comprehensive review of different soft computing-based congestion control schemes in wireless sensor networks is presented. Furthermore, these schemes are compared using different performance metrics. Finally, specific directives are used to design and develop novel soft computing-based congestion control schemes in wireless sensor networks.  


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How to Cite
Shams Shamsabad Farahani, S. (2021). Soft Computing-Based Congestion Control Schemes in Wireless Sensor Networks: Research Issues and Challenges. Majlesi Journal of Electrical Engineering, 15(1), 39-52.