Evaluation of Distributed Machine Learning Model for LoRa-ESL

• Published in: Journal of advanced computational intelligence and intelligent informatics Vol. 27; no. 4; pp. 700 - 709
• Main Authors: Khan, Malak Abid Ali, Ma, Hongbin, Rehman, Zia Ur, Jin, Ying, Rehman, Atiq Ur
• Format: Journal Article
• Language: English
• Published: Tokyo Fuji Technology Press Co. Ltd 01.07.2023
• Subjects: Access control; Capture effect; Clustering; Communication; Computer networks; Informatics; Internet of Things; LCDs; Liquid crystal displays; Machine learning; Network servers; Position indicators; Signal reception; Signal strength; Signal to noise ratio; Spectrum allocation; Spread spectrum
• ISSN: 1343-0130; 1883-8014
• DOI: 10.20965/jaciii.2023.p0700

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.