EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2022-06-14T11:40:57+00:00 EAI Publications Department Open Journal Systems <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, which is jointly sponsored and co-organized by Duy Tan University (Vietnam), 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> Retina-based quality assessment of tile-coded 360-degree videos 2022-05-18T16:05:08+00:00 Viet Hung Nguyen Ngoc Nam Pham Cong Thang Truong Duy Tien Bui Huu Thanh Nguyen Thu Huong Truong <p><span dir="ltr" role="presentation">Nowadays, omnidirectional content, which delivers 360-degree views of scenes, is a significant aspect of </span><span dir="ltr" role="presentation">Virtual Reality systems. While 360 video requires a lot of bandwidth, users only see visible tiles, therefore </span><span dir="ltr" role="presentation">a large amount of bitrate can be saved without a</span><span dir="ltr" role="presentation">ff</span><span dir="ltr" role="presentation">ecting the user’s experience on the service. The fact leads to </span><span dir="ltr" role="presentation">current video adaptation solutions to filter out superfluous parts and extraneous bandwidth. To form a good </span><span dir="ltr" role="presentation">basis for these adaptations, it is necessary to understand human’s video quality perception. In our research, </span><span dir="ltr" role="presentation">we contribute to building an e</span><span dir="ltr" role="presentation">ff</span><span dir="ltr" role="presentation">ective omnidirectional video database that can be applied to study the e</span><span dir="ltr" role="presentation">ff</span><span dir="ltr" role="presentation">ects </span><span dir="ltr" role="presentation">of the five zones of the human retina. We also design a new video quality assessment method to analyze </span><span dir="ltr" role="presentation">the impacts of those zones of a 360 video according to the human retina. The proposed scheme is found to </span><span dir="ltr" role="presentation">outperform 22 current objective quality measures by 11 to 31% in terms of the PCC parameter.</span></p> 2022-06-21T00:00:00+00:00 Copyright (c) 2022 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems A Framework of Deploying Blockchain in Wireless Sensor Networks 2022-06-13T04:37:51+00:00 Minh Nguyen Cuong Nguyen Hoang T. Tran <p><span dir="ltr" role="presentation">The most critical needs for wireless sensor networks (WSNs) are security, privacy, dependability, and </span><span dir="ltr" role="presentation">autonomy. The networks might be vulnerable to hostile users and harmful usage if these problems are </span><span dir="ltr" role="presentation">not ensured. Attacks and hazards are higher with centralized WSNs, particularly when data is shared with </span><span dir="ltr" role="presentation">other businesses and sent between devices. In this paper, a WSN model with integrated blockchain security </span><span dir="ltr" role="presentation">technology is proposed. Blockchains store the identity of each node. The validation is done by public </span><span dir="ltr" role="presentation">blockchains and private blockchains. For sensor nodes, the authentication is implemented on the private </span><span dir="ltr" role="presentation">blockchain. The public blockchain is used to authenticate cluster heads. Performing network attacks can easily </span><span dir="ltr" role="presentation">be performed by unregistered nodes to access resources in the network. Broadcasting false information on the </span><span dir="ltr" role="presentation">path of malicious nodes can increase packet latency and reduce packet delivery rate. In this paper, the model </span><span dir="ltr" role="presentation">recommends the most secure nodes in the network to be used for secure routing. The main purpose is to </span><span dir="ltr" role="presentation">reduce the attack of hackers from outside the network, improve the e</span><span dir="ltr" role="presentation">ffi</span><span dir="ltr" role="presentation">ciency of detecting malicious nodes.</span></p> 2022-08-04T00:00:00+00:00 Copyright (c) 2022 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems Big Data and Knowledge Graph Based Fault Diagnosis for Electric Power Systems 2022-06-14T11:40:57+00:00 Yuzhong Zhou Zhengping Lin Liang Tu Yufei Song Zhengrong Wu <p>Fault detection plays an important role in the daily maintenance of power electric system. Big data and knowledge graph (KG) have been proposed by researchers to solve many problems in industrial Internet of Things, which also give lots of potentials in improving the performance of fault detection for electric power systems. In particular, this paper analyzes a distributed knowledge graph framework for fault detection in the electric power systems, where multiple devices train their local detection models used for fault detection assisted with a central server. Each device owns its local data set composed of historical fault information and current device state, which can be used to train a local model for fault detection. To enhance the detection performance, the distributed devices interact with each other in the KG framework, where the devices ought to achieve the regional computation in addition to the model aggregation within a specified latency threshold. Through searching for the vibrant qualities together with determined ability at the devices, we enhance the knowledge graph framework by the optimum variety of energetic devices together with the restriction of latency as well as data transmission. Particularly, two data transmission bandwidth allocation (BA) schemes are developed for the distributed knowledge graph framework, through which scheme I is actually bared after the instantaneous device state information (DSI), and scheme II utilizes particle swarm optimization (PSO) technique along with the statistical DSI. The results of simulation on the examination as well as convergence are lastly demonstrated to show the advantages of the proposed distributed KG framework in the fault detection for the electric power systems.</p> 2022-06-14T00:00:00+00:00 Copyright (c) 2022 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems