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<p>The rapid deployment of Internet of Things (IoT) devices in 5G networks introduces significant challenges, particularly in path optimization where interference and energy efficiency are critical. Traditional routing methods in IoT environments often suffer from high interference and inefficient resource usage, leading to increased delays and energy consumption. To address these issues, we propose an interference-aware path optimization scheme using Particle Swarm Optimization (PSO) combined with Learning Automata (LA). The PSO algorithm efficiently identifies optimal paths by considering interference metrics such as network congestion metric, and residual energy, allowing for an intelligent route selection that minimizes packet collisions and retransmissions. The Learning Automata (LA) is integrated to dynamically adjust successor node selection based on real-time network conditions, ensuring reliable packet delivery even under high interference. Unlike conventional methods that rely on static routing or simple retransmissions during packet loss, the proposed LA mechanism adjusts node selection probabilities by evaluating multiple parameters, including communication cost, residual energy, distance from the base station, buffer size, interference levels, and historical success rates of nodes. This adaptive decision-making process enhances routing reliability while reducing the energy overhead associated with retransmissions. Furthermore, by optimizing both path stability and interference mitigation, the proposed solution provides an end-to-end framework for ensuring energy-efficient, low-latency, and interference-resilient communication in 5G IoT networks. Performance evaluations demonstrate that the PSO-LA-based technique significantly improves network throughput, reduces data transmission delays, and extends the overall network lifespan compared to traditional methods. This novel approach offers a scalable solution for managing the dense and dynamic environments characteristic of 5G-enabled IoT networks.</p>

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Interference-aware path optimization using PSO-LA for IoT in 5G networks

  • 期刊论文

  • 发表日期：

    2025年7月30日

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  • D. Mythrayee,
  • R. Nidhya,
  • D. Pavithra,
  • C. Vinothini,
  • V. K. Gnanavel,
  • G. Baskar

摘要

The rapid deployment of Internet of Things (IoT) devices in 5G networks introduces significant challenges, particularly in path optimization where interference and energy efficiency are critical. Traditional routing methods in IoT environments often suffer from high interference and inefficient resource usage, leading to increased delays and energy consumption. To address these issues, we propose an interference-aware path optimization scheme using Particle Swarm Optimization (PSO) combined with Learning Automata (LA). The PSO algorithm efficiently identifies optimal paths by considering interference metrics such as network congestion metric, and residual energy, allowing for an intelligent route selection that minimizes packet collisions and retransmissions. The Learning Automata (LA) is integrated to dynamically adjust successor node selection based on real-time network conditions, ensuring reliable packet delivery even under high interference. Unlike conventional methods that rely on static routing or simple retransmissions during packet loss, the proposed LA mechanism adjusts node selection probabilities by evaluating multiple parameters, including communication cost, residual energy, distance from the base station, buffer size, interference levels, and historical success rates of nodes. This adaptive decision-making process enhances routing reliability while reducing the energy overhead associated with retransmissions. Furthermore, by optimizing both path stability and interference mitigation, the proposed solution provides an end-to-end framework for ensuring energy-efficient, low-latency, and interference-resilient communication in 5G IoT networks. Performance evaluations demonstrate that the PSO-LA-based technique significantly improves network throughput, reduces data transmission delays, and extends the overall network lifespan compared to traditional methods. This novel approach offers a scalable solution for managing the dense and dynamic environments characteristic of 5G-enabled IoT networks.