Evaluation of Distributed Machine Learning Model for LoRa-ESL

被引：3
|
作者：
Khan, Malak Abid Ali [1 ]
Ma, Hongbin [1 ]
Rehman, Zia Ur [1 ]
Jin, Ying [1 ]
Rehman, Atiq Ur [2 ]
机构：
[1] Beijing Inst Technol BIT, State Key Lab Intelligent Control & Decis Complex, 5 Zhongguancun South St, Beijing 100081, Peoples R China
[2] Balochistan Univ Informat Technol Engn & Managemen, Dept Elect Engn, Airport Rd, Quetta 87300, Pakistan
来源：
JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS | 2023年 / 27卷 / 04期
基金：
中国国家自然科学基金;
关键词：
data parallelism; machine clustering; arith-metic distribution; LoRa-ESL;
D O I：
10.20965/jaciii.2023.p0700
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
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 nearfar 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 defined threshold.
引用
收藏
页码：700 / 709
页数：10
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