RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks
Future generation Internet of Things (IoT) communication infrastructure is expected to pave the path for innovative applications like smart cities, smart grids, smart industries, and smart healthcare. To support these diverse applications, the communication protocols are required to be adaptive and...
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| Format: | Article |
| Language: | English |
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IEEE
2021-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9442721/ |
| _version_ | 1818591822541225984 |
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| author | Zulqar Nain Arslan Musaddiq Yazdan Ahmad Qadri Ali Nauman Muhammad Khalil Afzal Sung Won Kim |
| author_facet | Zulqar Nain Arslan Musaddiq Yazdan Ahmad Qadri Ali Nauman Muhammad Khalil Afzal Sung Won Kim |
| author_sort | Zulqar Nain |
| collection | DOAJ |
| description | Future generation Internet of Things (IoT) communication infrastructure is expected to pave the path for innovative applications like smart cities, smart grids, smart industries, and smart healthcare. To support these diverse applications, the communication protocols are required to be adaptive and intelligent. At the network layer, an efficient and lightweight algorithm known as trickle-timer is designed to perform the route updates and it utilizes control messages to share the updated route information between IoT nodes. Trickle-timer tends to generate higher control overhead ratio and achieves lower reliability. Therefore, this article aims to propose an RL-based Intelligent Adaptive Trickle-Timer Algorithm (RIATA). The proposed algorithm performs three-fold optimization of the trickle-timer algorithm. Firstly, the RIATA assigns higher probability to control message transmission to nodes that have received an inconsistent control message in the past intervals. Secondly, the RIATA utilizes RL to learn the optimal policy to transmit or suppress a control message in the current network environment. Lastly, the RIATA selects an adaptive redundancy constant value to avoid unnecessary transmissions of control messages. Simulation results show that RIATA outperforms the other state-of-the-art mechanisms in terms of reducing control overhead ratio by an average of 21%, decreasing the average total power consumption by 10%, and increasing the packet delivery ratio by 4% on an average. |
| first_indexed | 2024-12-16T10:18:33Z |
| format | Article |
| id | doaj.art-800f31a18b924b77962193afa25f7456 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-16T10:18:33Z |
| publishDate | 2021-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-800f31a18b924b77962193afa25f74562022-12-21T22:35:22ZengIEEEIEEE Access2169-35362021-01-019811618117210.1109/ACCESS.2021.30842179442721RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT NetworksZulqar Nain0https://orcid.org/0000-0001-8915-8987Arslan Musaddiq1Yazdan Ahmad Qadri2https://orcid.org/0000-0001-5708-1532Ali Nauman3https://orcid.org/0000-0002-2133-5286Muhammad Khalil Afzal4https://orcid.org/0000-0002-6161-1310Sung Won Kim5https://orcid.org/0000-0001-8454-6980Department of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaICT Convergence Research Center, Kumoh National Institute of Technology, Gumi, South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaDepartment of Computer Science, Comsats University Islamabad, Wah campus Rawalpindi, PakistanDepartment of Information and Communication Engineering, Yeungnam University, Gyeongsan, South KoreaFuture generation Internet of Things (IoT) communication infrastructure is expected to pave the path for innovative applications like smart cities, smart grids, smart industries, and smart healthcare. To support these diverse applications, the communication protocols are required to be adaptive and intelligent. At the network layer, an efficient and lightweight algorithm known as trickle-timer is designed to perform the route updates and it utilizes control messages to share the updated route information between IoT nodes. Trickle-timer tends to generate higher control overhead ratio and achieves lower reliability. Therefore, this article aims to propose an RL-based Intelligent Adaptive Trickle-Timer Algorithm (RIATA). The proposed algorithm performs three-fold optimization of the trickle-timer algorithm. Firstly, the RIATA assigns higher probability to control message transmission to nodes that have received an inconsistent control message in the past intervals. Secondly, the RIATA utilizes RL to learn the optimal policy to transmit or suppress a control message in the current network environment. Lastly, the RIATA selects an adaptive redundancy constant value to avoid unnecessary transmissions of control messages. Simulation results show that RIATA outperforms the other state-of-the-art mechanisms in terms of reducing control overhead ratio by an average of 21%, decreasing the average total power consumption by 10%, and increasing the packet delivery ratio by 4% on an average.https://ieeexplore.ieee.org/document/9442721/Internet of Things (IoT)trickle-timerreinforcement learningRPL |
| spellingShingle | Zulqar Nain Arslan Musaddiq Yazdan Ahmad Qadri Ali Nauman Muhammad Khalil Afzal Sung Won Kim RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks IEEE Access Internet of Things (IoT) trickle-timer reinforcement learning RPL |
| title | RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks |
| title_full | RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks |
| title_fullStr | RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks |
| title_full_unstemmed | RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks |
| title_short | RIATA: A Reinforcement Learning-Based Intelligent Routing Update Scheme for Future Generation IoT Networks |
| title_sort | riata a reinforcement learning based intelligent routing update scheme for future generation iot networks |
| topic | Internet of Things (IoT) trickle-timer reinforcement learning RPL |
| url | https://ieeexplore.ieee.org/document/9442721/ |
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