https://publications.eai.eu/index.php/inis <p>EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is open access, a peer-reviewed scholarly journal focused on ubiquitous computing, cloud computing, and cyber-physical system, all kinds of networks in large-scale factories, including a lot of traditional and new industries. The journal publishes research articles, review articles, commentaries, editorials, technical articles, and short communications with a quarterly frequency (four issues per year). Authors are not charged for article submission and processing. This journal is co-organized, and managed by Duy Tan University, Vietnam.</p> <p><strong>INDEXING</strong>: Scopus (CiteScore: 3.1), Compendex, DOAJ, ProQuest, EBSCO, DBLP</p> European Alliance for Innovation (EAI) en-US EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2410-0218 <p>This is an open-access article distributed under the terms of the Creative Commons Attribution <a href="https://creativecommons.org/licenses/by/3.0/" target="_blank" rel="noopener">CC BY 3.0</a> license, which permits unlimited use, distribution, and reproduction in any medium so long as the original work is properly cited.</p> https://publications.eai.eu/index.php/inis/article/view/6780 <p>The performance of energy harvesting (EH)-enabled long-range (LoRa) networks is analyzed in this work. Specifically, we employ deep learning (DL) to estimate the coverage probability (Pcov) of the considered networks. Our study incorporates a general fading distribution, specifically the Nakagami-m distribution, and utilizes tools from stochastic geometry (SG) to model the spatial distributions of all nodes and end-devices (EDs) with EH capability. The DL approach is employed to overcome the limitations of model-based methods that can only evaluate the Pcov under simplified network conditions. Therefore, we propose a deep neural network (DNN) that estimates the Pcov with high accuracy compared to the ground truth values. Additionally, we demonstrate that DL significantly outperforms the Monte Carlo simulation approach in terms of resource consumption, including time and memory.</p> Thi-Tuyet-Hai Nguyen Tran Cong-Hung Nguyen Hong-Son Tan Hanh Tran Trung Duy Lam-Thanh Tu Copyright (c) 2024 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/ 2024-12-05 2024-12-05 12 2 10.4108/eetinis.v12i2.6780