RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT Networks
The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and low energy consumption. The l...
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MDPI AG
2025-06-01
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| author | Raghad Albalawi Fatma Bouabdallah Linda Mohaisen Shireen Saifuddin |
| author_facet | Raghad Albalawi Fatma Bouabdallah Linda Mohaisen Shireen Saifuddin |
| author_sort | Raghad Albalawi |
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| description | The Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and low energy consumption. The low data rate of low-power wide-area network (LPWAN) technologies, particularly SigFox, makes them appropriate for Internet of Things applications since the longer the radio link’s useable distance, the lower the data rate. Network reliability is the primary goal of SigFox technology, which aims to deliver data messages successfully through redundancy. This raises concerns about SigFox’s scalability and leads to one of its flaws, namely the high collision rate. In this paper, the goal is to prevent collisions by switching to time division multiple access (TDMA) from SigFox’s Aloha-based medium access protocol, utilizing only orthogonal channels, and eliminating redundancy. Consequently, during a designated time slot, each node transmits a single copy of the data message over a particular orthogonal channel. To achieve this, a multi-agent, off-policy reinforcement learning (RL) Q-Learning technique will be used on top of SigFox. In other words, the objective is to increase SigFox’s scalability through the use of Reinforcement Learning based time slot allocation (RL-SCAP). The findings show that, especially in situations with high node densities or constrained communication slots, the proposed protocol performs better than the basic SCAP (Slot and Channel Allocation Protocol) by obtaining a higher Packet Delivery Ratio (PDR) in average of 60.58%, greater throughput in average of 60.90%, and a notable decrease in collisions up to 79.37%. |
| format | Article |
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| institution | Kabale University |
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| language | English |
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| series | Technologies |
| spelling | doaj-art-e55e046596b94d89b3bcd784dbbf39d32025-08-20T03:27:26ZengMDPI AGTechnologies2227-70802025-06-0113625510.3390/technologies13060255RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT NetworksRaghad Albalawi0Fatma Bouabdallah1Linda Mohaisen2Shireen Saifuddin3Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaSchool of Engineering, Computing and Mathematics, University of Plymouth, Plymouth PL4 8AA, UKDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaDepartment of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi ArabiaThe Internet of Things (IoT) aims to wirelessly connect billions of physical things to the IT infrastructure. Although there are several radio access technologies available, few of them meet the needs of Internet of Things applications, such as long range, low cost, and low energy consumption. The low data rate of low-power wide-area network (LPWAN) technologies, particularly SigFox, makes them appropriate for Internet of Things applications since the longer the radio link’s useable distance, the lower the data rate. Network reliability is the primary goal of SigFox technology, which aims to deliver data messages successfully through redundancy. This raises concerns about SigFox’s scalability and leads to one of its flaws, namely the high collision rate. In this paper, the goal is to prevent collisions by switching to time division multiple access (TDMA) from SigFox’s Aloha-based medium access protocol, utilizing only orthogonal channels, and eliminating redundancy. Consequently, during a designated time slot, each node transmits a single copy of the data message over a particular orthogonal channel. To achieve this, a multi-agent, off-policy reinforcement learning (RL) Q-Learning technique will be used on top of SigFox. In other words, the objective is to increase SigFox’s scalability through the use of Reinforcement Learning based time slot allocation (RL-SCAP). The findings show that, especially in situations with high node densities or constrained communication slots, the proposed protocol performs better than the basic SCAP (Slot and Channel Allocation Protocol) by obtaining a higher Packet Delivery Ratio (PDR) in average of 60.58%, greater throughput in average of 60.90%, and a notable decrease in collisions up to 79.37%.https://www.mdpi.com/2227-7080/13/6/255optimizationLow-Power Wide-Area Networks (LPWAN)multiagentstransmission parametersreinforcement learningSigFox technology |
| spellingShingle | Raghad Albalawi Fatma Bouabdallah Linda Mohaisen Shireen Saifuddin RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT Networks Technologies optimization Low-Power Wide-Area Networks (LPWAN) multiagents transmission parameters reinforcement learning SigFox technology |
| title | RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT Networks |
| title_full | RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT Networks |
| title_fullStr | RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT Networks |
| title_full_unstemmed | RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT Networks |
| title_short | RL-SCAP SigFox: A Reinforcement Learning Based Scalable Communication Protocol for Low-Power Wide-Area IoT Networks |
| title_sort | rl scap sigfox a reinforcement learning based scalable communication protocol for low power wide area iot networks |
| topic | optimization Low-Power Wide-Area Networks (LPWAN) multiagents transmission parameters reinforcement learning SigFox technology |
| url | https://www.mdpi.com/2227-7080/13/6/255 |
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