EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index

Editor(s)-in-Chief: Trung Q. Duong, Le Nguyen Bao and Nguyen-Son Vo

Aims & Scope
Indexing
Editors in Chief
Editorial Board

Along with the fast development of computer technologies, e.g., ubiquitous computing, cloud computing and cyber-physical system, all kinds of networks (e.g., control network, communication network, sensor network, body area network, social network, opportunistic network, cloud-based network, etc.) appeared and were applied in large-scale factories, including a lot of traditional and new industries, e.g., textile industry, coal industry, mining industry, steel industry, machinery industry, petrochemical industry, and biomedical industry, etc. Assisted by various industrial networks, automation in industry can reduce cost greatly because it takes advantage of control systems and information technologies to optimize productivity in the production of goods and delivery of services. However, the industrial environment is dynamic and harsh usually, including extreme temperature, humidity, electromagnetic interference and vibration, which proposed specific requirements to intelligent industrial systems under certain circumstances. All these highlight the criticality of the design, analysis and implementation of intelligent industrial systems.

Applications of wireless sensor networks, body area networks in large-scale industrial applications, such as fault theories of wireless networks, including routing, network control and management, reliable transmission and architectures, etc.
Applications of social networking, big data, ubiquitous computing, mobile computing, and cloud computing in various industries and services (e.g., intelligent systems enhanced by social networking, cloud-based industrial networks, cloud-assisted intelligent systems, etc.)
Analysis of industrial control and communication networks, including network lifetime, security, network scalability, reliability, stability, etc.
Design and choice of industrial, intelligent, application-specific network protocols and algorithms (e.g., EtherNet/IP, Ethernet Powerlink, EtherCAT, Modbus-TCP, Profinet, SERCOS III, etc.) at any communication layer
Opportunistic networks in the industry, such as underwater sensor networks in sewage treatment systems, including establishing a temporary data transmission structure using available devices (e.g., underwater robot, surface data station, surface sink and under water sink), optimizing horizontal multi-hop data links (e.g., 3D data transmission), etc.
Applications of intelligent systems in various industries, including collaborative systems, quality control, optimization, decision support, planning, high-level control concepts (e.g., multi-agent and holonic systems, service-oriented architectures), low-level control concepts (e.g., IEC 61131-3 and IEC 61499-based control), advanced system engineering concepts (e.g., model-driven development, component-based design), supply chains, value chains, virtual organizations, and virtual societies, emergency preparedness, crisis management, business channels, electronic marketplaces, enterprise resources planning, etc.
Design and analysis of real-time embedded industrial systems, including real-time computing, real-time operating systems, real-time communications, networked embedded systems technology, etc.
Novel control techniques, with respect to process control, equipment control, supervisory control, adaptive control, motion control, etc.
Automated manufacturing systems, regarding formal modeling and analysis of manufacturing systems, scheduling of manufacturing systems, queuing systems and petri nets in manufacturing systems, etc.
Computational intelligence in automation, including neural, fuzzy, evolutionary approaches in automation, ant colonies optimization and swarm intelligence in automation, machine learning, expert systems, etc.
Hardware and software design and development for intelligent systems, such as intelligent and humanized production monitoring and control, etc.
Big data analysis and processing in various industries and services, including constructing data analysis models, providing data analysis and processing tools and designing various optimization algorithms based on data analysis.
Crowd-sourced behavior analysis in various industry and services, such as measuring and calculating the diffusion direction and speed of gas in the petrochemical industry based on crowd-sourced data from a large number of and various types of sensors, as well as product and service evaluation.
Simulation and testbed of current industrial networks and intelligent systems, including network performance analysis, automated manufacturing, intelligent monitoring, disaster prevention, etc.
Vision of future smart factories, service, marketing, and their integration, incorporating current existing technologies.
Multimedia applications, content management, process management and knowledge management for various industries, services, and engineering education: including multimedia processing, multimedia retrieval, multimedia indexing, image sensing, image processing, image coding, image recognition, etc.
Pattern recognition methods for various industries and services: including statistical theory, clustering, similarity measures, unsupervised learning, supervised learning, etc.
Survey, review and essay of current industrial networks researches and intelligent systems development.

Ala Al-Fuqaha (Western Michigan University, USA)
Al-Sakib Khan Pathan (Southeast University, Bangladesh)
Ammar Rayes (Cisco Systems, USA)
Antonino Masaracchia (IIT-CNR, Italy)
Athanasios Maglaras (Dr, Prof . ofT.E.I. of Larissa)
Ayse Kortun (Queen's University Belfast, UK)
Berk Canberk (Northeastern University, USA)
Ca V. Phan (Ho Chi Minh City University of Technology and Education, Vietnam)
Chau Yuen (Singapore University of Technology and Design, Singapore)
Chengfei Liu (Swinburne University of Technology, Australia)
Chinmoy Kundu (University of Texas at Dallas, USA)
Christer Carlsson (Åbo Akademi University, Finland)
Chunsheng Zhu (University of British Columbia)
Constandinos Mavromoustakis (University of Nicosia, Cyprus)
Der-Jiunn Deng (National Changhua University of Education, Taiwan)
Dickson Chiu (The University of Hong Kong)
Doan Xuan Toan (Thu Dau Mot University, Vietnam)
Eleanna Kafeza (Athens University of Economics and Business, Greece)
Fu-ren Lin (National Tsing Hua University, Taiwan)
Gerhard Hancke (University of London, UK)
Guangjie Han (Hohai University, China)
Guojun Wang (Central South University, China)
Hacene Fouchal (University of Reims Champagne-Ardenne, France)
Haklae Kim (Chung-Ang University, South Korea)
Halil Yetgin (Bitlis Eren University, Turkey)
Hideyasu Sasaki (Ritsumeikan University, Kyoto, Japan)
Ho-fung Leung (Chinese University of Hong Kong, Hong Kong)
Honggang Wang (University of Massachusetts Dartmouth, USA)
Hua Hu (Hangzhou Dianzi University, China)
Huy T. Nguyen (Inje University, South Korea)
Ibrahim Kushchu (Mobile Government Consortium International, UK)
Irene Kafeza (Irene Law Office, Greece)
Isabelle Comyn-Wattiau (ESSEC Business School Paris, France)
Jaime Lloret- Mauri (Universitat Politècnica de València, Spain)
Javier M. Aguiar (Universidad de Valladolid, Valladolid, Spain)
Jesus Alonso-Zarate (Telecommunications Technology Center of Catalonia, Spain)
Jian Yang (Macquarie University, Australia)
Jiankun Hu (University of New South Wales, Australia)
Jianmin Jiang (Shenzhen University)
Jianwei Niu (Beihang University, China)
Jinlei Jiang (Tsinghua University, China)
Jinsong Wu (Bell Laboratory, China)
Joel Rodrigues (Inst. Telecomunicações, Univ. of Beira Interior, Portugal)
Juan Trujillo (University of Alicante, Spain)
Jucheng Yang (Tianjing University of Technology, China)
Junqing Zhang (Queen's University Belfast)
KUN WANG (Nanjing University of Posts and Telecommunications)
Kuo-Ming Chao (Leader – Distributed Systems and Modelling Research Group, UK)
Leandros A. Maglaras (De Montfort University, UK)
Lei Wang (Dalian University of Technology, China)
Liang Zhou (Nanjing University of Posts and Telecommunications, China)
Long D. Nguyen (Dong Nai University, Vietnam)
Maggie M. Wang (The University of Hong Kong, Hong Kong)
Malcolm Egan (INSA Lyon)
Marijn Janssen (Delft University of Technology, The Netherlands)
Nicholas C Romano (Oklahoma State University, USA)
Noel Crespi (Institut Mines-Telecom, Telecom SudParis, France)
Panlong Yang (PLA University of Science and Technology, China)
Pasi Tyrväinen (University of Jyväskylä, Finland)
Patrick C.K. Hung (University of Ontario Institute of Technology, Canada)
Periklis Chatzimisios (Alexander TEI of Thessaloniki, Greece)
Phong Nam Nguyen (Memorial University of Newfoundland, Canada)
Pierluigi Siano (Università degli Studi di Salerno, Italy)
Pirkko Walden (Abo Akademi University, Finland)
Raymond Y.K Lau (City University of Hong Kong, Hong Kong)
Richard Yu (Carleton University, Canada)
Rong Yu (Guangdong University of Technology, China)
Rose Hu (Utah State University, USA)
Sammy Chan (City University of HongKong, HK)
Shing-Chi Cheung (Hong Kong University of Science and Technology, Hong Kong)
Stephen J. H. Yang (National Central University, Taiwan)
Syed Hassan Ahmed (University of Central Florida, USA)
Tran Trung Duy (PTIT, VietNam)
Trung Q. Duong (Blekinge Institute of Technology, Sweden)
Umar Zakir Abdul Hamid (Sensible 4 Oy, Helsinki)
Victor Leung (The University of British Columbia)
Vo Nguyen Son Dr. (Duy Tan University, Vietnam)
Wai-Wa Fung (Information Security and Forensics Society, Hong Kong)
Walid Gaaloul (Institut National des Télécommunications, France)
Wendy W. Y. Hui (University of Nottingham at Ningbo, China)
William Cheung (Hong Kong Baptist University, Hong Kong)
Xianfu Chen (VTT Technical Research Centre of Finland, Finland)
Xiang Gui (Massey University, New Zealand)
Xiaoling Wu (Chinese Academy of Sciences, China)
Xu Wang (Heriot Watt University, UK)
Yan Bai (University of Washington Tacoma, USA)
Yan Zhang (Simula Research Laboratory and University of Oslo, Norway)
Yi Zhuang (Zhejian Gongshang University, China)
Yong Li (Tsinghua University, China)
Yong Tang (South China Normal University, China)
Yuanfang Chen (Institute Mines-Telecom, University Pierre and Marie Curie )
Yuexing Peng (Beijing University of Posts and Telecommunications, China)
Yuqing Sun (Shangdong University, China)
Zakaria Maamar (Zayed University, UAE)
Zhangbing Zhou (China University of Geosciences, China)
ZhiMing Cai (Macau University of Science and Technology, Macau)
Mithun Mukherjee (Nanjing University of Information Science and Technology, China)
Zichao Sheng (ZTE, China)

Trung Q. Duong¹

Prof. Trung Q. Duong is a Chair Professor of Telecommunications at Queen’s University Belfast, U.K. and a Research Chair of Royal Academy of Engineering. His current research interests include machine learning, signal processing, data analytics, and IoT (applied to disaster management, agriculture, hydrometeorological hazards, healthcare), and 5G networks (URLLC, ultra-dense networks, UAV, physical layer security, massive MIMO, energy-harvesting). He has authored or co-authored over 400 publications (Google Scholar: 13000 citations and h-index 61). He is currently serving as an Editor for IEEE Trans on Wireless Communications, IEEE Trans on Vehicular Technology, Nature Scientific Reports and an Executive Editor for IEEE Communications Letters. He has served as the Guest Editor for more than 22 times on top-tier ISI-index journals including the IEEE Journal in Selected Areas on Communications 2015, IET Communications 2014, 2016, 2017, IEEE Communications Magazine 2014 and 2015, IEEE Access 2016, 2017, 2018, IEEE Wireless Communications Magazine 2015, EURASIP JWCN, EURASIP JASP. He was an Editor of IEEE Trans on Communications, IEEE Communications Letters, IET Communications, Electronics Letters, and Emerging Telecommunications Technologies. He has served as a chair for more than 20 IEEE conferences/workshops including the IEEE GLOBECOM 2016 Symposium Chair. Dr. Duong was awarded the Best Paper Award at the IEEE Vehicular Technology Conference (VTC-Spring) in 2013, the IEEE International Conference on Communications (ICC) 2014, the IEEE Global Communications Conference (GLOBECOM) 2016, IEEE Digital Signal Processing (DSP) 2017, and IEEE GLOBECOM 2019. He is a recipient of the prestigious Royal Academy of Engineering Research Fellowship (2016-2020) and has won the prestigious Newton Prize 2017.

Le Nguyen Bao²

Dr. Le Nguyen Bao is the Vice Provost and Head of the Technology & Science division as well as the R&D Center of Duy Tan University (DTU), which is widely considered to be the No. 1 private university in Vietnam. He earned his doctorate degree in Computer Science also from DTU, specializing in the field of facial recognition. His research interest is in network security policy, data warehousing, and 3D animation design. He helped initiate the CDIO (Conceive-Design-Implement-Operate) framework for engineering education first time in Central Vietnam back in 2010. His efforts ultimately led to the championship of Vietnamese students at the CDIO Academy, organized at Harvard and MIT in 2013. In 2017, he and his team again won the prestigious award for Vietnamese No. 1 Talents in Information Technology (IT) & Entrepreneurship for a 3D human-anatomy simulation. Under his leadership, Duy Tan University is aiming to become the top university for IT in Vietnam.

Nguyen-Son Vo²

Dr. Nguyen-Son Vo received a Ph.D. degree in communication and information systems from the Huazhong University of Science and Technology, Wuhan, China, in 2012. He is currently with the Institute of Fundamental and Applied Sciences, Duy Tan University, Ho Chi Minh City, Vietnam. His research interests include self-powered multimedia wireless communications, quality of experience provision in wireless networks for smart cities, and Internet of Things for disaster and environment management. Dr. Vo was the recipient of the Best Paper Award at the IEEE Global Communications Conference 2016 and the prestigious Newton Prize 2017. He has been serving as an Associate Editor for the IEEE COMMUNICATIONS LETTERS since 2019, Guest Editor for Elsevier Physical Communication (Special Issue on “Mission Critical Communications and Networking for Disaster Management”) since 2019, Guest Editor for IET Communications (Special Issue on “Recent Advances on 5G Communications”) since 2018, and Guest Editor for ACM/Springer Mobile Networks & Applications (Special Issues on “Wireless Communications and Networks for 5G and Beyond” since 2018 and “Wireless Communications and Networks for Smart Cities” since 2017).

1: Queen's University Belfast, UK
2: Duy Tan University, Vietnam

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An Efficient Traffic-aware Caching Mechanism For Information-centric Wireless Sensor Networks
Appears in:
inis 22(30): e5

Author:
Ngoc-Thanh Dinh

Published:
21st Apr 2022

Abstract:
This paper proposes a traffic-aware caching mechanism (TCM) for resource-constrained nodes in information- centric WSNs. TCM pushes up popular upstream content objects to be cached nearby the sink. Less popular content objects are cached farther from the sink compared to popular content objects. …This paper proposes a traffic-aware caching mechanism (TCM) for resource-constrained nodes in information-
centric WSNs. TCM pushes up popular upstream content objects to be cached nearby the sink. Less popular
content objects are cached farther from the sink compared to popular content objects. TCM also pushes down
popular downstream content objects to be cached inside the network. The objective of TCM is to reduce the
number of interest messages required forwarding in multiple hops inside WSNs to optimize stretch ratio,
energy efficiency, and content retrieval latency. We implement TCM on the top of existing popularity-based
caching schemes to improve their performance. Through analysis and experimental results, we show that TCM
achieves a significant improvement in terms of energy efficiency, content retrieval latency, cache hit ratio, and
stretch ratio compared to state-of-the-art popularity-based caching schemes.
more »
Automated evaluation of Tuberculosis using Deep Neural Networks
Appears in:
inis 22(30): e4

Authors:
Truong-Minh Le, Bao-Thien Nguyen-Tat, Vuong M. Ngo

Published:
14th Apr 2022

Abstract:
INTRODUCTION: Tuberculosis (TB) is a chronic, progressive infection that often has a latent period after the initial infection period. Early awareness from those period to have better prevention steps becomes an indispensable part for patients who want to lengthen their lives. Hence, applying cutti…INTRODUCTION: Tuberculosis (TB) is a chronic, progressive infection that often has a latent period after the initial infection period. Early awareness from those period to have better prevention steps becomes an indispensable part for patients who want to lengthen their lives. Hence, applying cutting-edge technologies to support the medical business domain plays a key role in improving speed and accuracy in methods of diagnosis. Deep Neural Network-based technique (DNN) is one of such methods which offers positive results by leveraging the advantages of analyzing deeply the data, especially image data format via tons of deep layers of the neural networks. Our study was wrapped up by objectively assessing the performance of modern Deep Neural Network approaches and suggesting a model offering good results in terms of the selected metrics as defined later. In order to achieve optimized results, the chosen model must adapt well to the datasets but require less hardware and computational resources.

OBJECTIVES: Our objective is to pick up and train a Deep Neural Network architecture which is highly
trusted and flexibly fitted and applied to various datasets with minimum configurations. This will be used to produce good predictions based on the input data which are Chest X-ray images retrieved from the published datasets.

METHODS: We have been approaching this problem by using the recognized datasets which have already
been published before, then resizing them to the consistent input data for training purposes. In terms of
Deep Neural Networks, we picked up VGG16 as the baseline network architecture, then use other ones
which are state-of-the-art networks for comparison purposes. After all, we recommend the neural network
architecture offering the most positive results based on accuracy and recall measurements. So that, this
network architecture will show flexibility when fitting into diverse datasets representing different areas in
the world that suffered from Tuberculosis before.

RESULTS: After conducting the experiments, we observed that the Mobilenet model produced great
results based on the predefined metrics for most of the proposed datasets. It shows the versatility which is applicable to all CXR datasets, especially for the Tuberculosis ones.

CONCLUSION: Tuberculosis is still one of the most dangerous illnesses in the world that needs vital
methods to prevent and detect soon so that patients are able to keep their lives longer. After this research, we are constantly improving the current accuracy of the models and applying the current results of this research for later problems such as detecting the Tuberculosis areas in real-time and supporting doctors to make decisions based on the current status of patients.
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LSB Data Hiding in Digital Media: A Survey
Appears in:
inis 22(30): e3

Authors:
Dang Ninh Tran, Hans-Jürgen Zepernick, Thi My Chinh Chu

Published:
5th Apr 2022

Abstract:
Data hiding or information hiding is a prominent class of information security that aims at reliably conveying secret data embedded in a cover object over a covert channel. Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret dat…Data hiding or information hiding is a prominent class of information security that aims at reliably conveying
secret data embedded in a cover object over a covert channel. Digital media such as audio, image, video, and three-dimensional (3D) media can act as cover objects to carry such secret data. Digital media security has acquired tremendous significance in recent years and will be even more important with the development and delivery of new digital media over digital communication networks. In particular, least significant bit (LSB) data hiding is easy to implement and to combine with other hiding techniques, offers high embedding capacity for data, can resist steganalysis and several types of attacks, and is well suited for real-time applications. This article provides a comprehensive survey on LSB data hiding in digital media. The fundamental concepts and terminologies used in data hiding are reviewed along with a general data hiding model. The five attributes of data hiding, i.e., capacity, imperceptibility, robustness, detectability, and security, and the related performance metrics used in this survey to compare the characteristics of the different LSB data hiding methods are discussed. Given the classification of data hiding methods with respect to audio, image, video, and 3D media, a comprehensive survey of LSB data hiding for each of these four digital media is provided. In particular, landmark studies, state-of-the-art approaches, and applications of LSB data hiding are described for each of the four digital media. Their performance is compared with respect to the data hiding attributes which illustrates benefits and drawbacks of the reviewed LSB data hiding methods. The article concludes with summarizing main findings and suggesting directions for future research. This survey will be helpful for researchers and practitioners to keep abreast about the potential of LSB data hiding in digital media and to develop novel applications based on suitable performance trade-offs between data hiding attributes.
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Content Delivery From the Sky: Drone-Aided Load Balancing for Mobile-CDN
Appears in:
inis 22(30): e2

Authors:
T. Bilen, B. Canberk

Published:
9th Mar 2022

Abstract:
The Base Station based Mobile-CDN architecture redirects the content request of mobile users to other base stations during storage misses. These request redirections increase the latency of a mobile client through unbalanced load distributions among base stations. To solve the unbalanced load distr…The Base Station based Mobile-CDN architecture redirects the content request of mobile users to other base stations during storage misses. These request redirections increase the latency of a mobile client through unbalanced load distributions among base stations. To solve the unbalanced load distribution and latency problems, we propose to deliver the content from the sky by deploying drones as aerial content delivery points. This drone based deployment enables a more effective and inexpensive solution without changing Mobile-CDN architecture. Here, we select different queuing theoretical models for drones and base stations due to the drones’ small capacity. With base station modeling, we can decide the loaded base stations to transfer the drones by utilizing the Barabasi-Albert Model. With drone modeling, we can obtain blocking probabilities with the Erlang-B parameter to determine additional drone transfer. According to simulations, the latency of mobile client originating requests are reduced by 25% compared to conventional Base Station based Mobile-CDN architecture.
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Extending Color Properties for Texture Descriptor Based on Local Ternary Patterns to Classify Rice Varieties
Appears in:
inis 22(30): e1

Authors:
Tran Thi Kim Nga, Tuan Pham-Viet, Dang Minh Nhat, Dang Minh Tam, Insoo Koo, Vladimir Y. Mariano, Tuan Do-Hong

Published:
7th Mar 2022

Abstract:
In this study, a proposed descriptor based on the improved local ternary patterns (ILTP) that also uses the color properties of rice varieties is presented. Not only gray-scale intensity, but R, G, and B color components of the rice grains are considered. Combining a support vector machine (SVM) wi…In this study, a proposed descriptor based on the improved local ternary patterns (ILTP) that also uses the color properties of rice varieties is presented. Not only gray-scale intensity, but R, G, and B color components of the rice grains are considered. Combining a support vector machine (SVM) with the proposed descriptor for classification of 17 rice varieties planted in Vietnam gives an overall accuracy of 95.53%. To evaluate and compare the effectiveness of the proposed descriptor with other analysis techniques for rice varieties classification, texture descriptors based on local binary pattern and local ternary patterns are combined with SVM to classify these 17 rice varieties. Experiment results show that the classification accuracy from the SVM using the proposed descriptor is significantly higher than using ILTP or other texture descriptors from the literature. This technique can be used to build an automatic system of rice varieties identification and classification.
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Information Outage Probability and Constrained Capacity of Moderate-Length Codes over AWGN Channels
Appears in:
inis 21(29): e5

Authors:
Lan K. Nguyen, Duy H. N. Nguyen, Richard Wells, Nghi H. Tran

Published:
3rd Nov 2021

Abstract:
We study the information outage probability (IOP) and constrained capacity of moderate-length codes over AWGN channels based on M-ary phase-shift keying signals. The IOP provides an important benchmark for performance evaluation of moderate-length codes. We analytically compute the IOP and compar…We study the information outage probability (IOP) and constrained capacity of moderate-length codes over
AWGN channels based on M-ary phase-shift keying signals. The IOP provides an important benchmark
for performance evaluation of moderate-length codes. We analytically compute the IOP and compare it
with numerical simulations using the DVB-S2 error-correcting code with numerous code rates employed in Protected Tactical Waveform (PTW). Numerical results confirm the tightness of the analytical results.
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Classification of UNSW-NB15 dataset using Exploratory Data Analysis using Ensemble Learning
Appears in:
inis 21(29): e4

Authors:
Neha Sharma, Narendra Singh Yadav, Saurabh Sharma

Published:
13th Oct 2021

Abstract:
Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be d…Recent advancements in machine learning have made it a tool of choice for different classification and analytical problems. This paper deals with a critical field of computer networking: network security and the possibilities of machine learning automation in this field. We will be doing exploratory data analysis on the benchmark UNSW-NB15 dataset. This dataset is a modern substitute for the outdated KDD’99 dataset as it has greater uniformity of pattern distribution. We will also implement several ensemble algorithms like Random Forest, Extra trees, AdaBoost, and XGBoost to derive insights from the data and make useful predictions. We calculated all the standard evaluation parameters for comparative analysis among all the classifiers used. This analysis gives knowledge, investigates difficulties, and future opportunities to propel machine learning in networking. This paper can give a basic understanding of data analytics in terms of security using Machine Learning techniques.
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Performance Analysis of Deep Neural Networks Using Computer Vision
Appears in:
inis 21(29): e3

Authors:
Nidhi Sindhwani, Rohit Anand, Meivel S., Rati Shukla, Mahendra Pratap Yadav, Vikash Yadav

Published:
13th Oct 2021

Abstract:
INTRODUCTION: In recent years, deep learning techniques have been made to outperform the earlier state-of-the-art machine learning techniques in many areas, with one of the most notable cases being computer vision. Deep learning is also employed to train the neural networks wi…INTRODUCTION: In recent years, deep learning techniques have been made to outperform the earlier state-of-the-art machine learning techniques in many areas, with one of the most notable cases being computer vision. Deep learning is also
employed to train the neural networks with the images and to perform the various tasks such as classification and segmentation using several different models. The size and depth of current deep learning models have increased to solve
certain tasks as these models provide better accuracy. As pre-trained weights may be used for further training and prevent costly computing, transfer learning is therefore of vital importance. A brief account is given of their history, structure, benefits, and drawbacks, followed by a description of their applications in the different tasks of computer vision, such as object detection, face recognition etc.

OBJECTIVE: The purpose of this paper is to train a deep neural network to properly classify the images that it has never seen before, define techniques to enhance the efficiency of deep learning and deploy deep neural networks in various applications.

METHOD: The proposed approach represents that after the reading of images, 256x256 pixel image’s random parts are extracted and noise, distortion, flip, or rotation transforms are applied. Multiple convolution and pooling steps are applied by
controlling the stride lengths.

RESULT: Data analysis and research findings showed that DNN models have been implemented in three main configurations of deep learning: CNTK, MXNet and TensorFlow. The proposed work outperforms the previous techniques in predicting the dependent variables, learning rate, image count, image mean, performance analysis of loss rate and learning rate during training, performance Analysis of Loss with respect to Epoch for Training, Validation and Accuracy.

CONCLUSION: This research encompasses a large variety of computer applications, from image recognition and machine translation to enhanced learning. DNN models have been implemented in three main configurations of deep learning: CNTK,
MXNet and TensorFlow. Extensive research has been conducted using the various deep architectures such as AlexNet, InceptionNet, etc. To the best of authors’ knowledge, this is the first work that presents a quantitative analysis of the deep architectures mentioned above.
more »
Prediction of dogecoin price using deep learning and social media trends
Appears in:
inis 21(29): e2

Authors:
Basant Agarwal, Priyanka Harjule, Lakshit Chouhan, Upkar Saraswat, Himanshu Airan, Parth Agarwal

Published:
29th Sep 2021

Abstract:
INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction. There has been a huge increase in the number of cryptocurrencies in the past few years. Cryptocurrencies such as Bitc…INTRODUCTION: Cryptocurrency is a digital, decentralized form of money based on blockchain technology, which makes it the most secure method of making a transaction. There has been a huge increase in the number of cryptocurrencies in the past few years. Cryptocurrencies such as Bitcoin and Ethereum have become an interesting subject of study in fields such as finance. In 2021, over 4,000 cryptocurrencies are already listed. There are many past studies that focus on predicting the price of cryptocurrencies using machine learning, but the majority of them only focused on Bitcoin. Moreover, the majority of the models implemented for price prediction only used the historical market prices, and do not utilize social signals related to the cryptocurrency.

OBJECTIVES: In this paper, we propose a deep learning model for predicting the prices of dogecoin cryptocurrency. The proposed model is based on historical market price data as well as social trends of Dogecoin cryptocurrency.

METHODS: The market data of Dogecoin is collected from Kaggle on the granularity of a day and for the same duration the verified tweets have also been collected with hashtags “Dogecoin” and “Doge”. Experimental results show that the proposed model yields a promising prediction of future price of Dogecoin, a cryptocurrency that has recently become the talk of the town of the crypto market.

RESULTS: Minimum achieved RMSE in predicted price of Dogecoin was 0.02 where the feature vector consisted of OCVP (Open, Close, Volume, Polarity) values from combined dataset.

RESULTS: Experimental results show that the proposed approach performs efficiently.
more »
Reliability-Security Analysis for Harvest-to-Jam based Multi-hop Cluster MIMO Networks Using Cooperative Jamming Methods Under Impact of Hardware Impairments
Appears in:
inis 21(28): e5

Authors:
Ngo The Anh, Nguyen Canh Minh, Tran Trung Duy, Tan Hanh, Hoang Dang Hai

Published:
17th Sep 2021

Abstract:
In this paper, we consider harvest-to-jam based secure multi-hop cluster multi-input multi-output networks, where a multi-antenna source sends its data to a multi-antenna destination via multi-antenna intermediate cluster heads. The data transmission at each hop is realized by using transmit antenn…In this paper, we consider harvest-to-jam based secure multi-hop cluster multi-input multi-output networks, where a multi-antenna source sends its data to a multi-antenna destination via multi-antenna intermediate cluster heads. The data transmission at each hop is realized by using transmit antenna selection and selection combining techniques, and is overheard by a multi-antenna eavesdropper using selection combining. In addition, joint antenna and jammer selection methods are performed at each hop to reduce quality of the eavesdropping channels. The cluster members can harvest wireless energy from the previous cluster head, and use the harvested energy for emitting jamming noises on the eavesdropper. We propose three cooperative
jamming algorithms, named best antenna and best jammer selection (BA-BJ), random antenna and all jammer selection (RA-AJ) and all antenna and all jammer selection (AA-AJ). Then, end-to-end outage probability and intercept probability of the proposed algorithms are evaluated via both simulation and analysis, under impact of hardware impairments, over Rayleigh fading channel.
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Smart Grid Attacks and Countermeasures
Appears in:
inis 15(2): e4

Authors:
Eric McCary, Yang Xiao

Downloads:
2254

Abstract:
The term “Smart Grid” has been coined and used for several years to describe the efforts of the current power grid modernization effort. This effort plans to introduce self-healing, energy efficiency, reliability, and security using two-way digital communications and control technology, along with …The term “Smart Grid” has been coined and used for several years to describe the efforts of the current power grid modernization effort. This effort plans to introduce self-healing, energy efficiency, reliability, and security using two-way digital communications and control technology, along with a host of other valuable attributes. As a bi-product of this modernization and newly gained systems interoperability, new communications and management interfaces are produced in both the cyber realm and physical domains. The increase of the public physical presence and cyber footprint opens up avenues for compromise to hackers and individuals with malicious intent. This survey paper will categorize and summarize vulnerabilities in the framework of the current power grid and the software and hardware which is currently being used to upgrade the grid. The paper will also detail known countermeasures which can be used to mitigate or eliminate attacks which exploit such vulnerabilities.
more »
Innovative Application of 5G and Blockchain Technology in Industry 4.0
Appears in:
inis 19(18): e4

Authors:
Ivan Jovović, Siniša Husnjak, Ivan Forenbacher, Sven Maček

Downloads:
1877

Abstract:
The Industry 4.0 is experiencing significant challenges, including the need for an increased amount of data transmission with improved security, transparency and credibility. The 5th Generation Mobile Network (5G) and Blockchain are innovative emerging technologies that can respond to these needs. …The Industry 4.0 is experiencing significant challenges, including the need for an increased amount of data transmission with improved security, transparency and credibility. The 5th Generation Mobile Network (5G) and Blockchain are innovative emerging technologies that can respond to these needs. 5G will enable extremely large channel capacities and reduce data latency, while Blockchain's innovative data-sharing mode of operation delivers an improved high level of security, transparency, and credibility of stored data. Therefore, this paper presents a general survey of the potential application of 5G network and Blockchain technology in Industry 4.0. The results may be used by first-movers firms for gaining technological leadership on the market.
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A Survey of System Level Power Management Schemes in the Dark-Silicon Era for Many-Core Architectures
Appears in:
inis 18(15): e5

Authors:
Emmannuel Ofori-Attah, Xiaohang Wang, Michael Opoku Agyeman

Downloads:
1739

Abstract:
Power consumption in Complementary Metal Oxide Semiconductor (CMOS) technology has escalated to a point that only a fractional part of many-core chips can be powered-on at a time. Fortunately, this fraction can be increased at the expense of performance through the dark-silicon solution. However, w…Power consumption in Complementary Metal Oxide Semiconductor (CMOS) technology has escalated to a point that only a fractional part of many-core chips can be powered-on at a time. Fortunately, this fraction can be increased at the expense of performance through the dark-silicon solution. However, with many-core integration set to be heading towards its thousands, power consumption and temperature increases per time, meaning the number of active nodes must be reduced drastically. Therefore, optimized techniques are demanded for continuous advancement in technology. Existing eﬀorts try to overcome this challenge by activating nodes from diﬀerent parts of the chip at the expense of communication latency. Other eﬀorts on the other hand employ run-time power management techniques to manage the power performance of the cores trading-oﬀ performance for power. We found out that, for a signiﬁcant amount of power to saved and high temperature to be avoided, focus should be on reducing the power consumption of all the on-chip components. Especially, the memory hierarchy and the interconnect. Power consumption can be minimized by, reducing the size of high leakage power dissipating elements, turning-oﬀ idle resources and integrating power saving materials.
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Improving Customer Behaviour Prediction with the Item2Item model in Recommender Systems
Appears in:
inis 18(17): e4

Authors:
T. T. S. Nguyen, P. M. T. Do, T. T. Nguyen

Downloads:
1591

Abstract:
Recommender Systems are the most well-known applications in E-commerce sites. However, the trade-off between runtime and the accuracy in making recommendations is a big challenge. This work combines several traditional techniques to reduce the limitation of each single technique and exploits the It…Recommender Systems are the most well-known applications in E-commerce sites. However, the trade-off between runtime and the accuracy in making recommendations is a big challenge. This work combines several traditional techniques to reduce the limitation of each single technique and exploits the Item2Item model to improve the prediction accuracy. As a case study, this paper focuses on user behaviour prediction in restaurant recommender systems and uses a public dataset including restaurant information and user sessions. Within this dataset, user behaviour can be discovered for the collaborative filtering, and restaurant information is extracted for the content-based filtering. The idea of the pre-trained word embedding in Natural Language Processing is utilized in the item-based collaborative filtering to find the similarity between restaurants based on user sessions. Experimental results have shown that the combination of these techniques makes valuable recommendations.
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Wireless Power Transfer Near-field Technologies for Unmanned Aerial Vehicles (UAVs): A Review
Appears in:
inis 20(22): e5

Authors:
Anh M. Le, Linh H. Truong, Toan V. Quyen, Cuong V. Nguyen, Minh T. Nguyen

Downloads:
1561

Abstract:
Wireless power transfer (WPT) techniques are being popular currently with the development of midrange wireless powering and charging technology to gradually substitute the need for wired devices during charging. Unmanned Aerial Vehicles (UAVs) are also being used with many practical purposes for a…Wireless power transfer (WPT) techniques are being popular currently with the development of midrange wireless powering and charging technology to gradually substitute the need for wired devices during
charging. Unmanned Aerial Vehicles (UAVs) are also being used with many practical purposes for agriculture,
surveillance, and healthcare, etc. There is a trade-off between the weight of the UAVs or their batteries and
their flying time. In order to support those UAVs perform better in their tasks, WPT is applied in UAVs to
recharge batteries which help to increase their working time. This paper highlights up-to-date studies that are
specific to near-field WPT deploying into UAVs. The charging distances, the transfer efficiency, and transfer
power, etc. are considered to provide an overview of all common problems in using and charging UAVs,
especially for autonomous landing and charging. By classification and suggestions in specific problems will
be provided opportunities and challenges with respect to apply near-field WPT techniques for charging the
battery of UAVs and other applications in the real world.
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Inpainting large missing regions based on Seam Carving
Appears in:
inis 18(16): e4

Authors:
Ahmed K. Al-Jaberi, Sabah A. Jassim, Naseer Al-Jawad

Downloads:
1427

Abstract:
Inpainting techniques are developed to recover missing image information. Existing inpainting approaches are: Partial Differential Equations Based Inpainting (PDE-BI) and Exemplar-Based Inpainting (EBI). PDE-BI methods used to fill in the missing information via information propagation from neighbo…Inpainting techniques are developed to recover missing image information. Existing inpainting approaches are: Partial Differential Equations Based Inpainting (PDE-BI) and Exemplar-Based Inpainting (EBI). PDE-BI methods used to fill in the missing information via information propagation from neighbouring areas. However, it can only reconstruct successfully small missing regions that are surrounded by limited texture. However, EBI methods are used to recover large regions with richly-textured/structured areas around them, moreover, artefacts are likely to occur. This paper proposes a technique to reduce the missing region size based on seam carving approach, which enables EBI and PDE-BI to recover the missing part. In our proposal, seam carving is used to reduce only the size of the missing region, to be subsequently recovered using EBI method. The added extra paths resulting from the added seams is repaired using PDE-BI. This method outperformed the state-of-art EBI methods.
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Security Issues in ProtoGENI
Appears in:
inis 16(6): e1

Authors:
Fnu Shalini, Yang Xiao, Bo Sun

Downloads:
1426

Abstract:
Network security consists of primary concerns in future Internet development due to the ever increasing threats to current Internet. ProtoGENI is a federated testbed facility supporting slice-based experiments to manage, utilize, and monitor the resources for innovative network research. Security…Network security consists of primary concerns in future Internet development due to the ever increasing threats to current
Internet. ProtoGENI is a federated testbed facility supporting slice-based experiments to manage, utilize, and monitor the
resources for innovative network research. Security research in ProtoGENI is crucial because experiments conducted in
manipulated or corruptted test environment can mislead about security mechanism’s capabilities in a system. In this paper,
existing ProtoGENI security mechanisms and functions are tested and analyzed through different experiments to find out
the exploitable attacking loopholes. Experiments elaborate the existing functioning and security issues that can cause nonfunctional,
semi non-functional or malfunctioned systems. Results indicate threats to ProtoGENI resources and run-time
interactions. Cross-experiment communication in Emulab wireless nodes have the capability of assisting in verifying
isolation between ProtoGENI slices. Host security is one of the security components, which can be enhanced by modifying
default security settings including SSH port number and root login rights. Documentation of experiment environment,
experiment design, results, and analysis including many observations is helpful to understand the basic functioning and
security issues to improve the overall functioning and security of ProtoGENI.
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Design of Cloud-Connected IoT System for Smart Buildings on Energy Management (Invited paper)
Appears in:
inis 16(6): e3

Authors:
Wayes Tushar, Chau Yuen, Kai Li, Kristin L. Wood, Zhang Wei, Liu Xiang

Downloads:
1373

Abstract:
Building energy management (BEM) system can provide cost-effective energy management solutions for smart buildings. However, the success of BEM is contingent on identifying large energy consuming devices, monitoring energy usage, optimal scheduling of flexible appliances, and distinguishing and con…Building energy management (BEM) system can provide cost-effective energy management solutions for smart buildings. However, the success of BEM is contingent on identifying large energy consuming devices, monitoring energy usage, optimal scheduling of flexible appliances, and distinguishing and controlling energy wastage. To this end, this paper discusses the design of Internet of Things (IoT) for a flexible BEM with the backend analytic that performs analysis on energy usage pattern and the wastage. We present the experimental results of real-time monitoring of consumers’ energy usage in a commercial space to identify the electricity wastage. The experiment is conducted at an office testbed using a comprehensive wireless sensor network. Extensive analysis is done to identify the energy wastage pattern of users for reducing the electricity cost. It is demonstrated that it is possible to reduce significant energy wastage when an office building is equipped with the proposed IoT system.
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Improving ns-3 Emulation Support in Real-World Networking Scenarios
Appears in:
inis 16(9): e5

Authors:
Helder Fontes, Rui Campos, Manuel Ricardo

Downloads:
1361

Abstract:
A common problem in networking research and development is the duplicate effort of writing simulation and implementation code. This duplication can be avoided through the use of fast-prototyping methodologies, which enable reusing simulation code in real prototyping and in production environments. …A common problem in networking research and development is the duplicate effort of writing simulation and implementation code. This duplication can be avoided through the use of fast-prototyping methodologies, which enable reusing simulation code in real prototyping and in production environments. Although this functionality is already available by using ns-3 emulation, there are still limitations regarding the support of real network interfaces and easy configuration of the network settings, such as IP and MAC addresses.
In this paper we propose an improved version of the ns-3 emulation component by introducing new functionalities that address these limitations. The new functionalities include the support of new types of real network interfaces and the easier integration of emulation nodes with existing networks by means of a new auto-configuration mechanism for ns-3 nodes. Experimental results obtained in a laboratorial testbed and in a real vehicular network testbed demonstrate the new functionalities proper operation, and their backwards compatibility with previously coded ns-3 scenarios.
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An Introduction of Real-time Embedded Optimisation Programming for UAV Systems under Disaster Communication
Appears in:
inis 18(17): e5

Authors:
Long D. Nguyen, Ayse Kortun, Trung Q. Duong

Downloads:
1350

Abstract:
For disaster communications, it is very challenging for the contemporary wireless technology and infrastructure to meet the demands for connectivity. Modern wireless networks should be developed to satisfy the increasing demand of quality-of-service (QoS) in mission-critical communications for disa…For disaster communications, it is very challenging for the contemporary wireless technology and infrastructure to meet the demands for connectivity. Modern wireless networks should be developed to satisfy the increasing demand of quality-of-service (QoS) in mission-critical communications for disaster management which are currently faced with the challenges of limited spectrum, expensive resources, reliable and green communication. There is a tremendous need for optimisation techniques in the study and design of the key functionalities of wireless systems. Until now, almost current optimisation scenarios are often carried out on large timescales (e.g., minutes or hours) without strict time constraints for solving the problems. With the improvement of computational speed, eﬃcient algorithms and advanced coding approaches, a framwork of real-time optimisation programming, which plays a major role in the trend of modern engineering such as mission-critical communications, is introduced for the first time in this paper in the context of natural disaster. In particular, we propose an introduction of embedded convex optimisation programming for unmanned ariel vehicles (UAVs) communications in disaster networks with the strict supervision of executive time in real-time scenarios.
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Publisher: EAI
ISSN: 2410-0218
Number of Volumes: 9

Last Published: 2022-04-21

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

Visit the new journal website to submit and consult our contents: https://publications.eai.eu/index.php/inis/index

Trung Q. Duong1

Le Nguyen Bao2

Nguyen-Son Vo2

Trung Q. Duong¹

Le Nguyen Bao²

Nguyen-Son Vo²