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.
Keywords
Terms, Energy Efficiency, Context-Aware Routing, RL, Fuzzy Logic, Network Lifetime, Internet of Things.
Yesuf, F. Y., & Prathap, M.. CARL-DTN: Context Adaptive Reinforcement Learning based Routing Algorithm in Delay Tolerant Network. arXiv preprint arXiv:2105.00544.
Tarif, M., Mirzaei, A., & Nouri-Moghaddam, B. (2024). Optimizing RPL Routing Using Tabu Search to Improve Link Stability and Energy Consumption in IoT Networks. arXiv preprint arXiv:2408.06702.
H. Lamaazi and N. Benamar, “OF-EC: A novel energy consumption aware objective function for RPL based on fuzzy logic.,” Journal of Network and Computer Applications, vol. 117, pp. 42–58, Sep. 2018, doi: 10.1016/j.jnca.2018.05.015.
C. Kim, C. So-In, Y. Kongsorot, and P. Aimtongkham, “FLSec-RPL: a fuzzy logic-based intrusion detection scheme for securing RPL-based IoT networks against DIO neighbor suppression attacks,” Cybersecurity, vol. 7, no. 1, Sep. 2024, doi: 10.1186/s42400-024-00223-x.
M. Alilou, A. Babazadeh Sangar, K. Majidzadeh, and M. Masdari, “QFS-RPL: mobility and energy aware multi path routing protocol for the internet of mobile things data transfer infrastructures,” Telecommunication Systems, vol. 85, no. 2, pp. 289–312, Dec. 2023, doi:10.1007/s11235-023-01075-5.
I. Rabet, H. Fotouhi, M. Alves, M. Vahabi, and M. Björkman, “ACTOR: Adaptive Control of Transmission Power in RPL,” Sensors, vol. 24, no. 7, p. 2330, Apr. 2024, doi: 10.3390/s24072330.
P. Singh and Y.-C. Chen, “RPL Enhancement for a Parent Selection Mechanism and an Efficient Objective Function,” IEEE Sensors Journal, vol. 19, no. 21, pp. 10054–10066, Nov. 2019, doi: 10.1109/jsen.2019.2927498.
A. Seyfollahi and A. Ghaffari, “A Review of Intrusion Detection Systems in RPL Routing Protocol Based on Machine Learning for Internet of Things Applications,” Wireless Communications and Mobile Computing, vol. 2021, no. 1, Jan. 2021, doi: 10.1155/2021/8414503.
A. P., H. S. Vimala, and S. J., “Comprehensive review on congestion detection, alleviation, and control for IoT networks,” Journal of Network and Computer Applications, vol. 221, p. 103749, Jan. 2024, doi: 10.1016/j.jnca.2023.103749.
H. Farag and C. Stefanovic, “Congestion-Aware Routing in Dynamic IoT Networks: A Reinforcement Learning Approach,” 2021 IEEE Global Cmmunications Conference (GLOBECOM), pp. 1–6, Dec. 2021, doi: 10.1109/globecom46510.2021.9685191.
D. Dey and N. Ghosh, “Itrpl: An Intelligent and Trusted Rpl Protocol Based on Multi-Agent Reinforcement Learning,” 2024, doi:10.2139/ssrn.4752236.
A. Mehbodniya et al., “Energy-Aware Routing Protocol with Fuzzy Logic in Industrial Internet of Things with Blockchain Technology,” Wireless Communications and Mobile Computing, vol. 2022, pp. 1–15, Jan. 2022, doi: 10.1155/2022/7665931.
Arivubrakan, P., & Kanagachidambaresan, G. R. (2021). Fuzzy Logic based Object Function to Enhance the Quality of Service in Internet of Things. International Journal of Intelligent Systems and Applications in Engineering, 9(1), 4725.
Z. Royaee, H. Mirvaziri, and A. Khatibi Bardsiri, “Designing a context-aware model for RPL load balancing of low power and lossy networks in the internet of things,” Journal of Ambient Intelligence and Humanized Computing, vol. 12, no. 2, pp. 2449–2468, Aug. 2020, doi:10.1007/s12652-020-02382-4.
Y. Chen, J.-P. Chanet, K.-M. Hou, H. Shi, and G. De Sousa, “A Scalable Context-Aware Objective Function (SCAOF) of Routing Protocol for Agricultural Low-Power and Lossy Networks (RPAL),” Sensors, vol. 15, no. 8, pp. 19507–19540, Aug. 2015, doi: 10.3390/s150819507.
Jiang, J., & Liu, Y. (2022). Secure IoT Routing: Selective Forwarding Attacks and Trust-based Defenses in RPL Network. arXiv preprint arXiv:2201.06937.
D. Sundareesh, “Exploring Capital and Agglomeration Effects on Entrepreneurship,” Journal of Digital Business and International Marketing, pp. 87–98, Apr. 2025, doi: 10.64026/jdbim/2025010.
The authors confirm contribution to the paper as follows:
Conceptualization: Thrisha V S and Anitha T N;
Methodology: Thrisha V S;
Software: Anitha T N;
Data Curation: Thrisha V S;
Writing- Original Draft Preparation: Thrisha V S and Anitha T N;
Visualization: Anitha T N;
Investigation: Thrisha V S;
Supervision: Anitha T N;
Validation: Thrisha V S;
Writing- Reviewing and Editing: Thrisha V S and Anitha T N; All authors reviewed the results and approved the final version of the manuscript.
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Author(s) thanks to Dr.Anitha T N for this research completion and support.
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Thrisha V S
Department of Computer Science Engineering, Sir M. Visvesvaraya Institute of Technology, Bangalore, Karnataka, India.
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Cite this article
Thrisha V S and Anitha T N, “CA-RPL - A Context Aware Reinforced Propagation Framework for Enhanced Energy Efficiency and Network Longevity in IoT Based LLNs”, Journal of Machine and Computing, vol.5, no.3, pp. 1685-1699, July 2025, doi: 10.53759/7669/jmc202505133.
