survey

LoRa Networking Techniques for Large-scale and Long-term IoT: A Down-to-top Survey

Authors:

Zhichao CaoAuthors Info & Claims

ACM Computing Surveys (CSUR), Volume 55, Issue 3

Article No.: 52, Pages 1 - 36

https://doi.org/10.1145/3494673

Published: 03 February 2022 Publication History

Abstract

Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm, which caters to large-scale and long-term sensory data collection demand. Among the commercialized LPWAN technologies, LoRa (Long Range) attracts much interest from academia and industry due to its open-source physical (PHY) layer and standardized networking stack. In the flourishing LoRa community, many observations and countermeasures have been proposed to understand and improve the performance of LoRa networking in practice. From the perspective of the LoRa networking stack; however, we lack a whole picture to comprehensively understand what has been done or not and reveal what the future trends are.

This survey proposes a taxonomy of a two-dimensional (i.e., networking layers, performance metrics) to categorize and compare the cutting-edge LoRa networking techniques. One dimension is the layered structure of the LoRa networking stack. From down to the top, we have the PHY layer, Link layer, Media-access Control (MAC) layer, and Application (App) layer. In each layer, we focus on the three most representative layer-specific research issues for fine-grained categorizing. The other dimension is LoRa networking performance metrics, including range, throughput, energy, and security. We compare different techniques in terms of these metrics and further overview the open issues and challenges, followed by our observed future trends. According to our proposed taxonomy, we aim at clarifying several ways to achieve a more effective LoRa networking stack and find more LoRa applicable scenarios, leading to a brand-new step toward a large-scale and long-term IoT.

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Wu Q(2024)A Review of Recent Patents on LoRaWANRecent Patents on Engineering10.2174/187221211866623050916492918:3Online publication date: Apr-2024
https://doi.org/10.2174/1872212118666230509164929
Zheng XLi RWang YLiu LMa H(2024) PolarScheduler : Dynamic Transmission Control for Floating LoRa Networks ACM Transactions on Sensor Networks10.1145/365285620:3(1-33)Online publication date: 23-Apr-2024
https://doi.org/10.1145/3652856
Lalor JAbbasi AOketch KYang YForsgren N(2024)Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning PipelinesACM Transactions on Information Systems10.1145/364127642:4(1-41)Online publication date: 22-Mar-2024
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Index Terms

LoRa Networking Techniques for Large-scale and Long-term IoT: A Down-to-top Survey
1. General and reference
  1. Document types
    1. Surveys and overviews
2. Networks
  1. Network performance evaluation
    1. Network measurement
    2. Network performance modeling

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Published In

cover image ACM Computing Surveys

ACM Computing Surveys Volume 55, Issue 3

March 2023

772 pages

ISSN:0360-0300

EISSN:1557-7341

DOI:10.1145/3514180

Editor:
Albert Zomaya
University of Sydney, Australia

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Publication History

Published: 03 February 2022

Accepted: 01 October 2021

Revised: 01 August 2021

Received: 01 March 2021

Published in CSUR Volume 55, Issue 3

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Cited By

Wu Q(2024)A Review of Recent Patents on LoRaWANRecent Patents on Engineering10.2174/187221211866623050916492918:3Online publication date: Apr-2024
https://doi.org/10.2174/1872212118666230509164929
Zheng XLi RWang YLiu LMa H(2024) PolarScheduler : Dynamic Transmission Control for Floating LoRa Networks ACM Transactions on Sensor Networks10.1145/365285620:3(1-33)Online publication date: 23-Apr-2024
https://doi.org/10.1145/3652856
Lalor JAbbasi AOketch KYang YForsgren N(2024)Should Fairness be a Metric or a Model? A Model-based Framework for Assessing Bias in Machine Learning PipelinesACM Transactions on Information Systems10.1145/364127642:4(1-41)Online publication date: 22-Mar-2024
https://dl.acm.org/doi/10.1145/3641276
Kou T(2024)From Model Performance to Claim: How a Change of Focus in Machine Learning Replicability Can Help Bridge the Responsibility GapProceedings of the 2024 ACM Conference on Fairness, Accountability, and Transparency10.1145/3630106.3658951(1002-1013)Online publication date: 3-Jun-2024
https://dl.acm.org/doi/10.1145/3630106.3658951
Mei LYin ZWang SZhou XLing THe T(2024)ECRLoRa: LoRa Packet Recovery under Low SNR via Edge–Cloud CollaborationACM Transactions on Sensor Networks10.1145/360493620:2(1-25)Online publication date: 9-Jan-2024
https://dl.acm.org/doi/10.1145/3604936
Huan XChen WWang THu H(2024)A Microsecond Energy-Efficient LoRa Time Synchronization Based on Low-Layer Timestamping and Asymmetric Time TranslationIEEE Transactions on Vehicular Technology10.1109/TVT.2023.333916973:5(7328-7332)Online publication date: May-2024
https://doi.org/10.1109/TVT.2023.3339169
Liu BGu CHe SChen J(2024)LoPhy: A Resilient and Fast Covert Channel Over LoRa PHYIEEE/ACM Transactions on Networking10.1109/TNET.2024.339881432:5(3792-3807)Online publication date: Oct-2024
https://doi.org/10.1109/TNET.2024.3398814
Hou NXia XWang YZheng Y(2024)One Shot for All: Quick and Accurate Data Aggregation for LPWANsIEEE/ACM Transactions on Networking10.1109/TNET.2024.335379232:3(2285-2298)Online publication date: Jun-2024
https://doi.org/10.1109/TNET.2024.3353792
Shao CMuta ODu QDandekar KWang X(2024)Multiple Access in Large-Scale LoRaWAN: Challenges, Solutions, and Future PerspectivesIEEE Consumer Electronics Magazine10.1109/MCE.2022.318251813:5(36-46)Online publication date: Sep-2024
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Lee GPark EPark MPaek JBahk S(2024)BIC-LoRa: Bits in Chirp Shapes to Boost Throughput in LoRa2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00024(237-248)Online publication date: 13-May-2024
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