Software-Defined Networking Strategies for Adaptive Resource Management in Wireless Mesh IoT Systems
Dr. P. BalamuruganAssociate Professor, Department of Networking and Communications, SRM Institute of Science and Technology, Kattankulathur, Tamil Nadu, India. balamurp@srmist.edu.in0000-0001-8394-1129
Dr. V. ArunaAssistant Professor, Department of Management Studies, St. Joseph’s Institute of Technology, OMR, Chennai, Tamil Nadu, India. arunasivakumar28@gmail.com0009-0009-0859-6439
Dr. R. UdayakumarProfessor & Director, Kalinga University, India. directoripr@kalingauniversity.ac.in0000-0002-1395-583X
Umidjon JurayevPhD in Technical Sciences, Associate Professor, Department of Information Technologies, Gulistan State University, Uzbekistan. pingo7520@gmail.com0000-0003-4624-699X
Madina SafarovaBasic doctorate, History of Pedagogical Teachings of Jizzakh State Pedagogical University, Uzbekistan. madinaturakulova54@gmail.com0009-0003-8073-0539
Dr. P. SrinivasanProfessor and Head, Department of Computer Science and Engineering (AI and ML), Paavai Engineering College, Namakkal, India. srinivasanp.dr@gmail.com0009-0009-7099-9255
Keywords: Wireless Mesh Networks (WMNs), Software-Defined Networking (SDN), Traffic Balancing, Internet of Things (IoT), Intelligent Artificial Fish Swarm Algorithm (IAFSA).
Abstract
Wireless Aries Network (WMN) provides scalability and flexibility, but faces challenges such as link failures, traffic imbalances, and inefficient resource usage. To address these limitations, the Software-Defined Networking Wireless Mesh Networks with Intelligent Artificial Fish Swarm Algorithm (SDN-WMN-IAFS) framework was introduced. The aim of this framework is to enhance the adaptability and optimization capabilities of WMNs, particularly in Internet of Things (IoT) environments. The framework integrates Software-Defined Networking (SDN) for centralized programmability and control, with the Intelligent Artificial Fish Swarm Algorithm (IAFSA) to optimize resource allocation and traffic management. The framework operates by dynamically monitoring network states to detect link failures and congestion. Using IAFSA, the system efficiently balances traffic loads and allocates resources by improving convergence speed and optimization accuracy. This process ensures reduced latency, efficient bandwidth utilization, and rapid recovery from link failures. The outcomes validate the effectiveness of the suggested framework, representing significant improvements in throughput, fault tolerance, and network resilience associated to traditional methods. The framework not only strengthens the robustness and scalability of WMNs but also offers a promising solution for IoT systems and other resource-constrained environments.