School of Automation, Beijing Institute of Technology, Beijing 100081, China.
Sensors (Basel). 2021 Jul 26;21(15):5060. doi: 10.3390/s21155060.
(1) Background: The scientific development in the field of industrialization demands the automization of electronic shelf labels (ESLs). COVID-19 has limited the manpower responsible for the frequent updating of the ESL system. The current ESL uses QR (quick response) codes, NFC (near-field communication), and RFID (radio-frequency identification). These technologies have a short range or need more manpower. LoRa is one of the prominent contenders in this category as it provides long-range connectivity with less energy harvesting and location tracking. It uses many gateways (GWs) to transmit the same data packet to a node, which causes collision at the receiver side. The restriction of the duty cycle (DC) and dependency of acknowledgment makes it unsuitable for use by the common person. The maximum efficiency of pure ALOHA is 18.4%, while that of slotted ALOHA is 36.8%, which makes LoRa unsuitable for industrial use. It can be used for applications that need a low data rate, i.e., up to approximately 27 Kbps. The ALOHA mechanism can cause inefficiency by not eliminating fast saturation even with the increasing number of gateways. The increasing number of gateways can only improve the global performance for generating packets with Poisson law having a uniform distribution of payload of 151 bytes. The maximum expected channel capacity usage is similar to the pure ALOHA throughput. (2) Methods: In this paper, the improved ALOHA mechanism is used, which is based on the orthogonal combination of spreading factor (SF) and bandwidth (BW), to maximize the throughput of LoRa for ESL. The varying distances (D) of the end nodes (ENs) are arranged based on the K-means machine learning algorithm (MLA) using the parameter selection principle of ISM (industrial, scientific and medical) regulation with a 1% DC for transmission to minimize the saturation. (3) Results: The performance of the improved ALOHA degraded with the increasing number of SFs and as well ENs. However, after using K-mapping, the network changes and the different number of gateways had a greater impact on the probability of successful transmission. The saturation decreased from 57% to 12% by using MLA. The RSSI (Received Signal Strength Indicator) plays a key role in determining the exact position of the ENs, which helps to improve the possibility of successful transmission and synchronization at higher BW (250 kHz). In addition, a high BW has lower energy consumption than a low BW at the same DC with a double-bit rate and almost half the ToA (time on-air).
（1）背景：工业化领域的科学发展要求电子货架标签（ESL）自动化。COVID-19 限制了负责频繁更新 ESL 系统的人力。当前的 ESL 使用 QR（快速响应）码、NFC（近场通信）和 RFID（射频识别）。这些技术的范围有限，或者需要更多的人力。LoRa 是此类技术中的佼佼者之一，因为它可以提供长距离连接，而能耗和位置跟踪较少。它使用许多网关（GW）将相同的数据分组传输到一个节点，这会在接收器端引起冲突。占空比（DC）的限制和对确认的依赖使得它不适合普通人使用。纯 ALOHA 的最大效率为 18.4%，而时隙 ALOHA 的最大效率为 36.8%，这使得 LoRa 不适合工业使用。它可用于需要低数据速率的应用，即高达约 27 Kbps。即使随着网关数量的增加，ALOHA 机制也无法消除快速饱和，从而导致效率低下。网关数量的增加只能提高具有均匀分布的负载为 1 到 51 字节的泊松定律生成数据包的全局性能。最大预期信道容量利用率与纯 ALOHA 吞吐量相似。
（2）方法：本文使用基于扩频因子（SF）和带宽（BW）正交组合的改进 ALOHA 机制，最大程度地提高 LoRa 用于 ESL 的吞吐量。根据 K-means 机器学习算法（MLA），根据 ISM（工业、科学和医学）法规的参数选择原则，排列末端节点（EN）的变化距离（D），将传输的 DC 降低到 1%，以最小化饱和。
（3）结果：改进的 ALOHA 的性能随着 SF 和 EN 数量的增加而降低。然而，使用 K 映射后，网络发生变化，不同数量的网关对成功传输的概率有更大的影响。使用 MLA 将饱和度从 57%降低到 1%到 2%。接收信号强度指示器（RSSI）在确定 EN 的准确位置方面起着关键作用，这有助于提高在更高 BW（250 kHz）下成功传输和同步的可能性。此外，高 BW 在相同的 DC 下比低 BW 具有更低的能耗，并且具有两倍比特率和几乎一半的 ToA（空中时间）。
