EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 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.</p> en-US <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> publications@eai.eu (EAI Publications Department) publications@eai.eu (EAI Support) Tue, 03 Jan 2023 12:52:17 +0000 OJS http://blogs.law.harvard.edu/tech/rss 60 Deep Reinforcement Learning for Intelligent Reflecting Surface-assisted D2D Communications https://publications.eai.eu/index.php/inis/article/view/2864 <p>In this paper, we propose a deep reinforcement learning (DRL) approach for solving the optimisation problem of the network’s sum-rate in device-to-device (D2D) communications supported by an intelligent reflecting surface (IRS). The IRS is deployed to mitigate the interference and enhance the signal between the D2D transmitter and the associated D2D receiver. Our objective is to jointly optimise the transmit power at the D2D transmitter and the phase shift matrix at the IRS to maximise the network sum-rate. We formulate a Markov decision process and then propose the proximal policy optimisation for solving the maximisation game. Simulation results show impressive performance in terms of the achievable rate and processing time.</p> Khoi Khac Nguyen, Antonino Masaracchia, Cheng Yin Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/inis/article/view/2864 Tue, 03 Jan 2023 00:00:00 +0000 AgFAB - A Farmer-centered Agricultural Bower https://publications.eai.eu/index.php/inis/article/view/2714 <p>Digital Agriculture aims to raise agricultural productivity while empowering the farming stakeholders (especially the farmers) with the availability of ICT-based applications on smart devices. However, despite putting in much effort, smallholder farmers’ willingness for adopting digital technologies is low in developing countries. In this study, following the principles of the human-design process, we investigated the smallholder farmers’ core demands from mobile/computing application(s). Considering these core demands of the farming community, the developed prototypical interfaces were evaluated by farmers using the System Usability Scale (SUS) to check the acceptability of a proposed farmer-centered solution named AgFAB. The AgFAB prototypical interface design received an average SUS score of 72.37, which is an indication of an acceptable design. Moreover, the results of Paired T-test seem promising for the strong adoptability of AgFAB by farmers with reference to their aspect of usability in the agricultural context.</p> Muhammad Azhar Iqbal, Brianna B. Posadas, Fudi Qin, Bohan Liu, Ali Siddique Copyright (c) 2023 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://creativecommons.org/licenses/by/3.0/ https://publications.eai.eu/index.php/inis/article/view/2714 Wed, 08 Feb 2023 00:00:00 +0000