Journal Article10.48550/arXiv.2208.01824
A Lightweight Transmission Parameter Selection Scheme Using Reinforcement Learning for LoRaWAN
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TL;DR: A lightweight transmission-parameter selection scheme using reinforcement learning for low-power wide area networking (LoRaWAN) that avoids collisions between LoRa devices irrespective of changes in the available channels and improves the frame success rate and fairness.
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Abstract: —The number of IoT devices is predicted to reach 125 billion by 2023. The growth of IoT devices will intensify the collisions between devices, degrading communication per- formance. Selecting appropriate transmission parameters, such as channel and spreading factor (SF), can effectively reduce the collisions between long-range (LoRa) devices. However, most of the schemes proposed in the current literature are not easy to implement on an IoT device with limited computational complexity and memory. To solve this issue, we propose a lightweight transmission-parameter selection scheme, i.e., a joint channel and SF selection scheme using reinforcement learning for low-power wide area networking (LoRaWAN). In the proposed scheme, appropriate transmission parameters can be selected by simple four arithmetic operations using only Acknowledge (ACK) information. Additionally, we theo- retically analyze the computational complexity and memory requirement of our proposed scheme, which verified that our proposed scheme could select transmission parameters with extremely low computational complexity and memory requirement. Moreover, a large number of experiments were implemented on the LoRa devices in the real world to evaluate the effectiveness of our proposed scheme. The experimental results demonstrate the following main phenomena. (1) Com- pared to other lightweight transmission-parameter selection schemes, collisions between LoRa devices can be efficiently avoided by our proposed scheme in LoRaWAN irrespective of changes in the available channels. (2) The frame success rate (FSR) can be improved by selecting access channels and using SFs as opposed to only selecting access channels. (3) Since interference exists between adjacent channels, FSR and fairness can be improved by increasing the interval of adjacent available channels.
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Citations
Combinatorial MAB-Based Joint Channel and Spreading Factor Selection for LoRa Devices
Ikumi Urabe,Aohan Li,Minoru Fujisawa,Song-Ju Kim,Mikio Hasegawa +4 more
- 26 Jul 2023
TL;DR: The learning methods evaluated in this paper were the Tug of War dynamics, Upper Confidence Bound 1, and ϵ-greedy algorithms, and a combinational multi-armed bandit-based joint channel and SF-selection method.
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Deep Reinforcement Learning Based Resource Allocation for LoRaWAN
Aohan Li
- 01 Sep 2022
TL;DR: In this article , a deep Q learning-based intelligent resource allocation (DQLRA) method for LoRaWAN is proposed, where the gateway (GW) trains the deep neural network (DNN) only based on the transmission state, i.e., transmission failure or success, and the corresponding device number of each LoRa device.
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References
A Survey on LoRa Networking: Research Problems, Current Solutions, and Open Issues
TL;DR: This article provides a comprehensive survey on LoRa networks, including the technical challenges of deployingLoRa networks and recent solutions, and some open issues of LoRa networking are discussed.
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Federated Learning for Internet of Things: Recent Advances, Taxonomy, and Open Challenges
TL;DR: The recent advances of federated learning towards enabling Federated learning-powered IoT applications are presented and a set of metrics such as sparsification, robustness, quantization, scalability, security, and privacy, is delineated in order to rigorously evaluate the recent advances.
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Improving Reliability and Scalability of LoRaWANs Through Lightweight Scheduling
TL;DR: This paper proposes a new MAC layer—RS-LoRa—to improve reliability and scalability of LoRa wide-area networks (LoRaWANs) and implement it in NS-3 and demonstrates the benefit of RS-Lo Ra over the legacy LoRaWan, in terms of packet error ratio, throughput, and fairness.
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Deep Reinforcement Learning for Internet of Things: A Comprehensive Survey
TL;DR: A comprehensive survey that overviews DRL algorithms and discusses DRL-enabled IoT applications and highlights emerging challenges and outline future research directions in driving the further success of DRL in IoT applications.
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Spreading Factor Allocation for Massive Connectivity in LoRa Systems
Jin-Taek Lim,Youngnam Han +1 more
TL;DR: This letter analyzes LoRa systems for increasing average system packet success probability (PSP) under unslotted ALOHA random access protocol and formulate an optimization problem for maximizing the average system PSP to propose a sub-optimal SF allocation scheme to each traffic.
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