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JACIII Vol.27 No.4 pp. 700-709
doi: 10.20965/jaciii.2023.p0700
(2023)

Research Paper:

Evaluation of Distributed Machine Learning Model for LoRa-ESL

Malak Abid Ali Khan* ORCID Icon, Hongbin Ma*,† ORCID Icon, Zia Ur Rehman* ORCID Icon, Ying Jin* ORCID Icon, and Atiq Ur Rehman** ORCID Icon

*State Key Laboratory of Intelligent Control and Decision of Complex Systems, Beijing Institute of Technology (BIT)
No.5 Zhongguancun South Street, Haidian District, Beijing 100081, China

Corresponding author

**Department of Electrical Engineering, Balochistan University of Information Technology, Engineering, and Management Sciences (BUITEMS)
Airport Road, Baleli, Quetta 87300, Pakistan

Received:
December 26, 2022
Accepted:
April 24, 2023
Published:
July 20, 2023
Keywords:
data parallelism, machine clustering, arithmetic distribution, LoRa-ESL
Abstract

To overcome the previous challenges and to mitigate the retransmission and acknowledgment of LoRa for electric shelf labels, the data parallelism model is used for transmitting the concurrent data from the network server to end devices (EDs) through gateways (GWs). The EDs are designated around the GWs based on machine clustering to minimize data congestion, collision, and overlapping during signal reception. Deployment and redeployment of EDs in the defined clusters depend on arithmetic distribution to reduce the near-far effect and the overall saturation in the network. To further improve the performance and analyze the behavior of the network, constant uplink power for signal-to-noise (SNR) while dynamic for received signal strength (RSS) has been proposed. In contrast to SNR, the RSS indicator estimates the actual position of the ED to prevent the capture effect. In the experimental implementation, downlink power at the connected EDs in the clusters illustrates higher values than the defined threshold.

Cite this article as:
M. Khan, H. Ma, Z. Rehman, Y. Jin, and A. Rehman, “Evaluation of Distributed Machine Learning Model for LoRa-ESL,” J. Adv. Comput. Intell. Intell. Inform., Vol.27 No.4, pp. 700-709, 2023.
Data files:
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Last updated on Mar. 01, 2024