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Title	Ca-Rpl - A Context Aware Reinforced Propagation Framework For Enhanced Energy Efficiency And Network Longevity In Iot Based Llns
ID_Doc	13211
Authors	Thrisha V.S.; Anitha T.N.
Year	2025
Published	Journal of Machine and Computing, 5, 3
DOI	http://dx.doi.org/10.53759/7669/jmc202505133
Abstract	Routing in Low-Power and Lossy Networks (LLNs) requires a careful balancing act between energy efficiency and network longevity, especially in situations motivated by the Internet of Things (IoT). Traditional RPL (Routing Protocol for Low-Power and Lossy Networks) often fails when confronted with changing climatic conditions and erratic node activity, thereby increasing energy consumption and reducing the network's lifespan. This work presents the CA-RPL (Context-Aware Reinforced Propagation Framework), which dynamically adjusts its routing decisions in real-time based on residual energy, node mobility, connection quality, and traffic patterns. The system utilizes reinforcement learning and a decision engine based on fuzzy logic to dynamically identify optimal parent nodes and alternative paths, thereby minimizing control packet overhead and balancing the energy burden throughout the network. Simulation results in Python show that CA-RPL increases the overall network lifetime by 30.2% and significantly reduces the average energy consumption by 21% compared to conventional and objective function-enhanced RPL versions. Where reliability and sustainability are critical for Internet of Things (IoT) implementations in industrial and smart city environments, the proposed approach offers an intelligent and adaptable pathing paradigm. ©2025 The Authors.
Author Keywords	Context-Aware Routing; Energy Efficiency; Fuzzy Logic; Internet of Things; Network Lifetime; RL; Terms

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