Open Access
ARTICLE
Research on Key Technologies of Electronic Shelf Labels Based on LoRa
Malak Abid Ali Khan1,2, Xiaofeng Lian1,*, Imran Khan Mirani1,3, 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.
Keywords
Cite This Article
M. Abid Ali Khan, X. Lian, I. Khan Mirani and L. Tan, "Research on key technologies of electronic shelf labels based on lora,"
Journal on Big Data, vol. 3, no.2, pp. 49–63, 2021. https://doi.org/10.32604/jbd.2021.016213