LoRa Networking Techniques for Large-scale and Long-term IoT: A Down-to-top Survey
LoRa Networking Techniques for Large-scale and Long-term IoT: A Down-to-top Survey
复制标题
DOI：
10.1145/3494673
复制
发表时间：
2023-04-01
影响因子：
16.6
通讯作者：
Cao, Zhichao
中科院分区：
文献类型：
--
作者：
Li, Chenning;Cao, Zhichao
低功耗广域网（LPWAN）是一种新兴的物联网（IoT）范式，它迎合了大规模和长期的传感数据收集需求。在商业化的LPWAN技术中，LoRa（长距离）由于其开源的物理（PHY）层和标准化的网络堆栈而引起学术界和工业界的极大兴趣。在蓬勃发展的LoRa社区中，已经提出了许多观察和对策，以了解和改善LoRa网络在实践中的性能。从LoRa网络堆栈的角度来看;然而，我们缺乏一个整体的图景来全面了解已经做了什么或没有做什么，并揭示未来的趋势是什么。这项调查提出了一个二维的分类（即，网络层，性能指标）来分类和比较尖端的LoRa网络技术。一个维度是LoRa网络堆栈的分层结构。从下到上，我们有PHY层，链路层，媒体访问控制（MAC）层和应用程序（App）层。在每一层中，我们专注于三个最具代表性的层特定的细粒度分类的研究问题。另一个维度是LoRa网络性能指标，包括范围、吞吐量、能量和安全性。我们根据这些指标比较了不同的技术，并进一步概述了开放的问题和挑战，然后是我们观察到的未来趋势。根据我们提出的分类法，我们旨在阐明实现更有效的LoRa网络堆栈的几种方法，并找到更多的LoRa适用场景，从而朝着大规模和长期物联网迈出全新的一步。
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.