Open Access

ARTICLE

Research on Key Technologies of Electronic Shelf Labels Based on LoRa

Malak Abid Ali Khan^1,2, Xiaofeng Lian^1,*, Imran Khan Mirani^1,3, Li Tan⁴

1 School of Artificial Intelligence, Beijing Technology & Business University, Beijing, 100048, China
2 School of Automation, Beijing Institute of Technology, Beijing, 100081, China
3 School of Information and Computer Engineering, Beijing University of Technology, Beijing, 100124, China
4 School of Computer Science & Engineering, Beijing Technology & Business University, Beijing, 100048, China

* Corresponding Author: Xiaofeng Lian. Email:

Journal on Big Data 2021, 3(2), 49-63. https://doi.org/10.32604/jbd.2021.016213

Received 12 December 2020; Accepted 01 April 2021; Issue published 13 April 2021

Abstract

The demand for Electronic Shelf Labels (ESL), according to the Internet of Things (IoT) paradigm, is expected to grow considerably in the immediate future. Various wireless communication standards are currently contending to gain an edge over the competition and provide the massive connectivity that will be required by a world in which everyday objects are expected to communicate with each other. Low-Power Wide-Area Networks (LPWANs) are continuously gaining momentum among these standards, mainly thanks to their ability to provide long-range coverage to devices, exploiting license-free frequency bands. The main theme of this work is one of the most prominent LPWAN technologies, LoRa. The purpose of this research is to provide long-range, less intermediate node, less energy dissipation, and a cheaper ESL system. Much research has already been done on designing the LoRaWAN network, not capable to make a reliable network. LoRa is using different gateways to transmit the same data, collision, data jamming, and data repetition are expected. According to the transmission behavior of LoRa, 50% of data is lost. In this paper, the Improved Backoff Algorithm with synchronization technique is used to decrease overlapping and data loss. Besides, the improved Adaptive Data Rate algorithm (ADR) avoids the collision in concurrently transmitted data by using different Spreading Factors (SFs). The allocation of SF has the main role in designing LoRa based network to minimize the impact of the intra-interference, cost function, and Euclidean distance. For this purpose, the K- means machine learning algorithm is used for clustering. The data rate model is using an intra-slicing technique based on Maximum Likelihood Estimation (MLE). The data rate model includes three critical communication slices, High Critical Communication (HCC), Medium Critical Communication (MCC), and Low Critical Communication (LCC), having the specified number of End devices (EDs), payload budget delay, and data rate. Finally, different combinations of gateways are used to build ESL for 200 electronic shelf labels.

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Keywords

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