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RI-RPL: a new high-quality RPL-based routing protocol using Q-learning algorithm

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Abstract

The lack of a central controller, severe resource constraints, and multi-path data routing have turned data exchanges into one of the fundamental challenges of the Internet of Things. Despite numerous research efforts on various aspects of routing and data exchanges, some fundamental challenges such as the instant negative impacts of selecting the best possible path and the absence of measures to observe the dynamic conditions of nodes still exist. This study introduces a method called RI-RPL, based on the development of the RPL routing protocol, along with the use of reinforcement learning to address these challenges effectively. To achieve this, RI-RPL is designed in three general stages. In the first stage, routers are aligned with optimizing the RPL protocol with a focus on the Q-learning algorithm. In the second stage, based on learning and convergence, changes in the parents’ learning in different network conditions are supported. In the third stage, control and management changes are coordinated. The reason for choosing this algorithm is its ability to address the desired challenges effectively without wasting network resources for calculations. Simulation results using the Cooja software show that the proposed RI-RPL method, compared to similar recent methods such as ELBRP, RLQRPL, and RPL, has improved successful delivery rates by 4.03%, 13.26%, and 28.87%, respectively, for end-to-end delay by 3.04%, 9.82%, and 13.12%, respectively, for energy consumption optimization by 10.43%, 28.91%, and 36.35%, respectively, for throughput by 10.23%, 28.45%, and 46.88%, respectively, and for network data loss rate by 15.06%, 34.95%, and 49.66%, respectively.

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Notes

  1. Routing Protocol for Low-Power and Lossy Networks.

  2. Routing Over Low Power and Lossy.

  3. Internet Engineering Task Force.

  4. Energy-Aware Grid-based Data Aggregation Scheme in Routing.

  5. Energy-Aware RPL.

  6. A New Load Balancing Objective Function for Low-Power and Lossy Networks.

  7. Composite Routing Technique in IoT Application Networks.

  8. Lightweight Load Balancing and Route Minimizing Solution for RPL.

  9. Forwarding Traffic Consciousness Objective Function for RPL Routing Protocol.

  10. QoS‑Centric Fault‑Resilient Routing Protocol for Mobile‑WSN-Based Low-Power and Lossy Networks.

  11. Weighted Random Forward RPL for High Traffic and Energy Demanding Scenarios.

  12. Rank Remain Energy RPL.

  13. Enhance-Minimum Rank with Hysteresis Objective Function.

  14. RPL Powered by Laplacian Energy for Stable Path Selection During Link Failures in an Internet of Things Network.

  15. Fuzzy Logic Approach for Routing in Internet of Things Network.

  16. Congestion and QoS Aware RPL for IoT Applications Under Heavy Traffic.

  17. Improvement of Minimum Rank Hysteresis Objective Function.

  18. Reliable Link Quality-Based RPL Routing.

  19. Link Quality-Based Objective Function.

  20. Energy-Efficient Priority-Based Multi-Objective QoS Routing.

  21. Fuzzy Logic Objective Function.

  22. An Effective Routing Algorithm for Low-Power and Lossy Networks Using Multi-Criteria Decision-Making Techniques.

  23. Vlse Kriterijumsk Optimizacija Kompromisno Resenje.

  24. Analytical Hierarchy Process.

  25. Energy and Load Balancing Routing Protocol for IoT.

  26. Reinforcement Learning-Based RPL Routing Protocol.

  27. Point-To-Point.

  28. Point-To-Multipoint.

  29. Multipoint-To-Point.

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Authors and Affiliations

  1. Faculty of Computer Engineering, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Niloofar Zahedy, Behrang Barekatain & Alfonso Ariza Quintana

  2. Big Data Research Center, Najafabad Branch, Islamic Azad University, Najafabad, Iran

    Behrang Barekatain

  3. E.T.S.I. Telecomunicación, Universidad de Málaga, Málaga, Spain

    Alfonso Ariza Quintana

Authors
  1. Niloofar Zahedy
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  2. Behrang Barekatain
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  3. Alfonso Ariza Quintana
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Contributions

The first two authors worked on all aspects of the paper and the third one checked the simulation process as well as the idea.

Corresponding author

Correspondence to Behrang Barekatain.

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Zahedy, N., Barekatain, B. & Quintana, A.A. RI-RPL: a new high-quality RPL-based routing protocol using Q-learning algorithm. J Supercomput 80, 7691–7749 (2024). https://doi.org/10.1007/s11227-023-05724-z

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