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<p>Existing IoT routing protocols tend to prioritize Quality of Service (QoS) either in independently or using a centralized or computationally-intensive framework and are thus constrained in scalability and flexibility in dynamic and large-scale networks. The mobility of nodes is coupled with energy depletion, which results in link failures that negatively impact the longevity of mobile ad-hoc networks. Nevertheless, conventional single-path algorithms tend to require a new path discovery process which introduces additional delays within the network. To tackle these challenges, a new routing optimization technique called Federated Recurrent Deep Graph Linear Transition Network (FRDGLTN) has been developed to balance the energy efficiency, identification of malicious nodes and performance of lightweight routing. The Improved Chaotic Evolution Optimization Algorithm (ICEOA) is employed to dynamically predict the optimal inertia weight for energy and link-aware Quality of Service (QoS) routing. Additionally, the trickle timer has been improved with an Online Gradient Descent (OGD) method to enable flexible modifications based on network conditions, which minimizes excessive retransmissions through effective route updates. The functionality of the suggested approach is tested with the help of MATLAB-based simulations and compared to the representative state-of-the-art routing protocols with the same network settings. The proposed approach shows impressive results, achieving energy consumption of 100&#xa0;J, a packet delivery ratio of 99.6%, reduced latency of 40&#xa0;ms, a throughput of 750&#xa0;s and a network lifespan of 700&#xa0;s, outperforming existing methods. Overall, the developed approach provides a reliable route selection process by considering link quality and remaining energy, adapting dynamically to changes in parent selection criteria.</p>

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Federated graph-based QoS routing for energy-efficient IoT networks

  • 期刊论文

  • 发表日期：

    2026年3月2日

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  • D. Anandhasilambarasan,
  • B. Arun Kumar

摘要

Existing IoT routing protocols tend to prioritize Quality of Service (QoS) either in independently or using a centralized or computationally-intensive framework and are thus constrained in scalability and flexibility in dynamic and large-scale networks. The mobility of nodes is coupled with energy depletion, which results in link failures that negatively impact the longevity of mobile ad-hoc networks. Nevertheless, conventional single-path algorithms tend to require a new path discovery process which introduces additional delays within the network. To tackle these challenges, a new routing optimization technique called Federated Recurrent Deep Graph Linear Transition Network (FRDGLTN) has been developed to balance the energy efficiency, identification of malicious nodes and performance of lightweight routing. The Improved Chaotic Evolution Optimization Algorithm (ICEOA) is employed to dynamically predict the optimal inertia weight for energy and link-aware Quality of Service (QoS) routing. Additionally, the trickle timer has been improved with an Online Gradient Descent (OGD) method to enable flexible modifications based on network conditions, which minimizes excessive retransmissions through effective route updates. The functionality of the suggested approach is tested with the help of MATLAB-based simulations and compared to the representative state-of-the-art routing protocols with the same network settings. The proposed approach shows impressive results, achieving energy consumption of 100 J, a packet delivery ratio of 99.6%, reduced latency of 40 ms, a throughput of 750 s and a network lifespan of 700 s, outperforming existing methods. Overall, the developed approach provides a reliable route selection process by considering link quality and remaining energy, adapting dynamically to changes in parent selection criteria.