https://publications.eai.eu/index.php/inis/issue/feed EAI Endorsed Transactions on Industrial Networks and Intelligent Systems 2022-11-09T13:55:23+00:00 EAI Publications Department publications@eai.eu 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> https://publications.eai.eu/index.php/inis/article/view/1415 Internet Traffic Prediction Using Recurrent Neural Networks 2022-09-02T13:34:34+00:00 Mircea Eugen Dodan mircea.eugen.dodan@gmail.com Quoc-Tuan Vien q.vien@mdx.ac.uk Tuan Thanh Nguyen Tuan.Nguyen@greenwich.ac.uk <p class="ICST-abstracttext"><span lang="EN-GB">Network traffic prediction (NTP) represents an essential component in planning large-scale networks which are in general unpredictable and must adapt to unforeseen circumstances. In small to medium-size networks, the administrator can anticipate the fluctuations in traffic without the need of using forecasting tools, but in the scenario of large-scale networks where hundreds of new users can be added in a matter of weeks, more efficient forecasting tools are required to avoid congestion and over provisioning. Network and hardware resources are however limited; and hence resource allocation is critical for the NTP with scalable solutions. To this end, in this paper, we propose an efficient NTP by optimizing recurrent neural networks (RNNs) to analyse the traffic patterns that occur inside flow time series, and predict future samples based on the history of the traffic that was used for training. The predicted traffic with the proposed RNNs is compared with the real values that are stored in the database in terms of mean squared error, mean absolute error and categorical cross entropy. Furthermore, the real traffic samples for NTP training are compared with those from other techniques such as auto-regressive moving average (ARIMA) and AdaBoost regressor to validate the effectiveness of the proposed method. It is shown that the proposed RNN achieves a better performance than both the ARIMA and AdaBoost regressor when more samples are employed.</span></p> 2022-09-02T00:00:00+00:00 Copyright (c) 2022 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://publications.eai.eu/index.php/inis/article/view/2218 An Accurate Viewport Estimation Method for 360 Video Streaming using Deep Learning 2022-09-21T13:35:42+00:00 Hung Nguyen hungnv@eaut.edu.vn Thu Ngan Dao dtngan1610@gmail.com Ngoc Son Pham son.pn212644m@sis.hust.edu.vn Tran Long Dang dangtranlong@gmail.com Trung Dung Nguyen dung.nguyentrung1@hust.edu.vn Thu Huong Truong huong.truongthu@hust.edu.vn <p>Nowadays, Virtual Reality is becoming more and more popular, and 360 video is a very important part of the system. 360 video transmission over the Internet faces many difficulties due to its large size. Therefore, to reduce the network bandwidth requirement of 360-degree video, Viewport Adaptive Streaming (VAS) was proposed. An important issue in VAS is how to estimate future user viewing direction. In this paper, we propose an algorithm called GLVP (GRU-LSTM-based-Viewport-Prediction) to estimate the typical view for the VAS system. The results show that our method can improve viewport estimation from 9.5% to near 20%compared with other methods.</p> 2022-09-21T00:00:00+00:00 Copyright (c) 2022 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://publications.eai.eu/index.php/inis/article/view/2467 Phase Impairment Estimation for mmWave MIMO Systems with Low Resolutions ADC and Imperfect CSI 2022-11-09T13:55:23+00:00 Nguyen Dinh Ngoc nguyendinhngoc@tcu.edu.vn Kien Truong kien.truong@fulbright.edu.vn <p class="ICST-abstracttext">Multiple-Input Multiple-Output systems operating at millimeter wave band (mmWave MIMO) are a promising technology next generations of mobile networks. In practice, the non-ideal hardware is a challenge for commercially viable mmWave MIMO transceivers and come from non-linearities of the amplifier, phase noise, quantization errors, mutual coupling between antenna ports, and In-phase/Quadrature (I/Q) imbalance. As a result, the received signals are affected by non-ideal transceiver hardware components, thus reduce the performance of such systems, especially phase impairment caused by phase noise and carrier frequency offset (CFO). In this paper, we consider a mmWave MIMO system model that takes into account many practical hardware impairments and imperfect channel state information (CSI). Our main contributions are a problem formulation of phase impairments with imperfect CSI and a low-complexity estimation method to solve the problem. Numerical results are provided to evaluate the performance of the proposed algorithm.</p> 2022-10-28T00:00:00+00:00 Copyright (c) 2022 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems https://publications.eai.eu/index.php/inis/article/view/2571 Human Activity Recognition System For Moderate Performance Microcontroller Using Accelerometer Data And Random Forest Algorithm 2022-11-09T13:55:21+00:00 To-Hieu Dao hieu.daoto@phenikaa-uni.edu.vn Hai-Yen Hoang hthyen@ictu.edu.vn Van-Nhat Hoang nhat.hoangvan@phenikaa-uni.edu.vn Duc-Tan Tran tan.tranduc@phenikaa-uni.edu.vn Duc-Nghia Tran nghiatd@ioit.ac.vn <p>There has been increasing interest in the application of artificial intelligence technologies to improve the quality of support services in healthcare. Some constraints, such as space, infrastructure, and environmental conditions, present challenges with assistive devices for humans. This paper proposed a wearable-based real-time human activity recognition system to monitor daily activities. The classification was done directly on the device, and the results could be checked over the internet. The accelerometer data collection application was developed on the device with a sampling frequency of 20Hz, and the random forest algorithm was embedded in the hardware. To improve the accuracy of the recognition system, a feature vector of 31 dimensions was calculated and used as an input per time window. Besides, the dynamic window method applied by the proposed model allowed us to change the data sampling time (1-3 seconds) and increase the performance of activity classification. The experiment results showed that the proposed system could classify 13 activities with a high accuracy of 99.4%. The rate of correctly classified activities was 96.1%. This work is promising for healthcare because of the convenience and simplicity of wearables.</p> 2022-11-09T00:00:00+00:00 Copyright (c) 2022 EAI Endorsed Transactions on Industrial Networks and Intelligent Systems