A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges
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Abstract (arXiv)
Long-range (LoRa) technology is most widely used for enabling low-power wide area networks (WANs) on unlicensed frequency bands. Despite its modest data rates, it provides extensive coverage for low-power devices, making it an ideal communication system for many internet of things (IoT) applications. In general, LoRa is considered as the physical layer, whereas LoRaWAN is the medium access control (MAC) layer of the LoRa stack that adopts a star topology to enable communication between multiple end devices (EDs) and the network gateway. The chirp spread spectrum modulation deals with LoRa signal interference and ensures long-range communication. At the same time, the adaptive data rate mechanism allows EDs to dynamically alter some LoRa features, such as the spreading factor (SF), code rate, and carrier frequency to address the time variance of communication conditions in dense networks. Despite the high LoRa connectivity demand, LoRa signals interference and concurrent transmission collisions are major limitations. Therefore, to enhance LoRaWAN capacity, the LoRa Alliance released many LoRaWAN versions, and the research community has provided numerous solutions to develop scalable LoRaWAN technology. Hence, we thoroughly examine LoRaWAN scalability challenges and state-of-the-art solutions in both the physical and MAC layers. These solutions primarily rely on SF, logical, and frequency channel assignment, whereas others propose new network topologies or implement signal processing schemes to cancel the interference and allow LoRaWAN to connect more EDs efficiently. A summary of the existing solutions in the literature is provided at the end of the paper, describing the advantages and disadvantages of each solution and suggesting possible enhancements as future research directions.
Key facts. Cited by 12 papers in the map (data through 7 Oct 2026). First posted on arXiv 22 Feb 2022.
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Citing papers per year: 2022: 2; 2023: 1; 2024: 4; 2025: 3; 2026: 2.
12 papers.
- 2022Federated Distillation based Indoor Localization for IoT Networkscited by 6
- 2023Federated Learning based Hierarchical 3D Indoor Localizationcited by 6
- 2022Spreading Factor assisted LoRa Localization with Deep Reinforcement Learningcited by 3
- 2024FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networkscited by 3
- 2024A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environmentscited by 2
- 2024LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directionscited by 1
- 2024Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learningcited by 1
- 2025A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factorscited by 1
- 2025Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networkscited by 1
- 2025Energy-Efficient UAV-assisted LoRa Gateways: A Multi-Agent Optimization Approachcited by 0
- 2026Hetero-Net: An Energy-Efficient Resource Allocation and 3D Placement in Heterogeneous LoRa Networks via Multi-Agent Optimizationcited by 0
- 2026Utility-Aware DRL-Based TXOP Adaptation for NR-U and Wi-Fi Coexistence Networkscited by 0
- 2026Utility-Aware DRL-Based TXOP Adaptation for NR-U and Wi-Fi Coexistence Networks1 May 2026
- 2026Hetero-Net: An Energy-Efficient Resource Allocation and 3D Placement in Heterogeneous LoRa Networks via Multi-Agent Optimization20 Mar 2026
- 2025Agentic DDQN-Based Scheduling for Licensed and Unlicensed Band Allocation in Sidelink Networks8 Sep 2025
- 2025A Comprehensive Data Description for LoRaWAN Path Loss Measurements in an Indoor Office Setting: Effects of Environmental Factors9 May 2025
- 2025Energy-Efficient UAV-assisted LoRa Gateways: A Multi-Agent Optimization Approach5 Feb 2025
- 2024Adversarial Attack and Defense for LoRa Device Identification and Authentication via Deep Learning30 Dec 2024
- 2024LoRa Communication for Agriculture 4.0: Opportunities, Challenges, and Future Directions17 Sep 2024
- 2024FeMLoc: Federated Meta-learning for Adaptive Wireless Indoor Localization Tasks in IoT Networks17 May 2024
- 2024A Unified Deep Transfer Learning Model for Accurate IoT Localization in Diverse Environments16 May 2024
- 2023Federated Learning based Hierarchical 3D Indoor Localization1 Mar 2023
- 2022Federated Distillation based Indoor Localization for IoT Networks23 May 2022
- 2022Spreading Factor assisted LoRa Localization with Deep Reinforcement Learning23 May 2022