EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems is an open access, 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.

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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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
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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
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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.
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Prediction of dogecoin price using deep learning and social media trends
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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.
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Reliability-Security Analysis for Harvest-to-Jam based Multi-hop Cluster MIMO Networks Using Cooperative Jamming Methods Under Impact of Hardware Impairments
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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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A compact 1×3 two-mode selective silicon photonic router/switch using two tunable phase shifters
Appears in:
inis 21(28): e4

Authors:
Duong Quang Duy, Truong Cao Dung, Dang Hoai Bac, Nguyen Binh, Nguyen Tan Hung, Tang Tan Chien, Le Thi Phuong Mai, Nguyen Vy Rin, Phan Thi Thanh Van

Published:
17th Sep 2021

Abstract:
In this work, we propose a design in the proof-of-concept of a 1×3 two-mode selective silicon-photonics router/switch. The proposed device composes of a Y-junction coupler, two multimode interference (MMI) couplers, and two phase-shifters on the silicon-on-insulator (SOI) rib waveguides. The input …In this work, we propose a design in the proof-of-concept of a 1×3 two-mode selective silicon-photonics router/switch. The proposed device composes of a Y-junction coupler, two multimode interference (MMI) couplers, and two phase-shifters on the silicon-on-insulator (SOI) rib waveguides. The input modes of TE0 and TE1 can be arbitrarily and simultaneously routed to the yearning output ports by setting appropriate values (ON/OFF) for two tunable phase shifters (PSs). The structural optimization and efficient characterization processes are carried out by numerical simulation via three-dimensional beam propagation method. The proposed device exhibits the operation ability over the C-band with good optical performances in terms of insertion loss smaller than 1 dB, crosstalk under -19 dB, and relatively large geometry tolerances. Moreover, the proposed device can integrate into a footprint as compact as 5 μm ×475 μm. Such significant advantages are beneficial and promising potentials for very large-scale photonic integrated circuits, high-speed optical interconnects, and short-haul few-mode fiber communication systems.
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A Deep Neural Network-Based Approach for Extracting Textual Images from Deteriorate Images
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inis 21(28): e3

Authors:
Binay Kumar Pandey, Digvijay Pandey, Subodh Wariya, Gaurav Agarwal

Published:
17th Sep 2021

Abstract:
INTRODUCTION: The quantity of audio and visual data is increasing exponentially due to the internet's rapid growth. The digital information in images and videos could be used for fully automated captions, indexing, and image structuring. The online image and video data system …INTRODUCTION: The quantity of audio and visual data is increasing exponentially due to the internet's rapid growth. The digital information in images and videos could be used for fully automated captions, indexing, and image structuring. The online image and video data system has seen a significant increase. In such a dataset, images and videos must be retrieved, explored, as well as inspected.

OBJECTIVES: Text extraction is crucial for locating critical as well as important data. Disturbance is indeed a critical factor that affects image quality, and this is primarily generated during image acquisition and communication operations.
An image can be contaminated by a variety of noise-type disturbances. A text in the complex image includes a variety of information which is used to recognise textual as well as non-textual particulars. The particulars in the complicated
corrupted images have been considered important for individuals seeing the entire issue. However, text in complicated degraded images exhibits a rapidly changing form in an unconstrained circumstance, making textual data recognition complicated.

METHODS: The naïve bayes algorithm is a weighted reading technique is used to generate the correct text data from the complicated image regions. Usually, images hold some disturbance as a result of the fact that filtration is proposed during the early pre-processing step. To restore the image's quality, the input image is processed employing gradient and contrast image methods. Following that, the contrast of the source images would be enhanced using an adaptive image map. Stroke width transform, Gabor transform, and weighted naïve bayes classifier methodologies have been used in complicated degraded images to segment, features extraction, and detect textual and non-textual elements.

RESULTS: Finally, to identify categorised textual data, the confluence of deep neural networks and particle swarm optimization is being used. The dataset IIIT5K is used for the development portion, and also the performance of the suggested methodology is assessed by utilizing parameters like as accuracy, recall, precision, and F1 score. It performs well enough for record collections such as articles, even when significantly distorted, and is thus suitable for creating library information system databases.

CONCLUSION: A combination of deep neural network and particle swarm optimization is being used to recognise classified text. The dataset IIIT5K is used for the development portion, and while high performance is achieved with parameters such as accuracy, recall, precision, and F1 score, characters may occasionally deviate. Alternatively, the same character is frequently extracted [3] multiple times, which may result in incorrect textual data being extracted from natural
images. As a result, an efficient technique for avoiding such flaws in the text retrieval process must be implemented in the near future.
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Review of Optimization in Improving Extreme Learning Machine
Appears in:
inis 21(28): e2

Authors:
Nilesh Rathod, Sunil Wankhade

Published:
17th Sep 2021

Abstract:
Now a days Extreme Learning Machine has gained a lot of interest because of its noteworthy qualities over single hidden-layer feedforward neural networks and the kernel functions. Even if ELM has many advantages, it has some potential shortcomings such as performance sensitivity to…Now a days Extreme Learning Machine has gained a lot of interest because of its noteworthy qualities over single hidden-layer feedforward neural networks and the kernel functions. Even if ELM has many advantages, it has some potential shortcomings such as performance sensitivity to the underlying state of the hidden neurons, input weights and the choice of functions of activation. To overcome the limitations of traditional ELM, analysts have devised numerical methods to optimise specific parts of ELM in order to enhance ELM performance for a variety of complicated difficulties and applications. Hence through this study, we intend to study the different algorithms developed for optimizing the ELM to enhance its performance in the aspects of survey criteria such as datasets, algorithm, objectives, training time, accuracy, error rate and the hidden neurons. This study will help other researchers to find out the research issues that lowering the performance of the ELM.
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Student’s Perception towards Mobile learning using Interned Enabled Mobile devices during COVID-19
Appears in:
inis 21(29): e1

Authors:
Pooja Gupta, Vimal Kumar, Vikash Yadav

Published:
16th Sep 2021

Abstract:
INTRODUCTION: The novel corona disease disrupted education all around the world. This shifted people to mobile learning in real time wireless classroom from the physical face-to-face classroom. OBJECTIVE: Mobile learning has been present for years but the use of mobile learning is more in the c…INTRODUCTION: The novel corona disease disrupted education all around the world. This shifted people to mobile learning in real time wireless classroom from the physical face-to-face classroom.

OBJECTIVE: Mobile learning has been present for years but the use of mobile learning is more in the current scenario due to COVID-19. However, people’s acceptance of mobile learning education at institutions is still low. Thus, this research seeks
to understand the student’s perspective by analysing constructs hypothesized in the proposed hybrid model.

METHOD: Data is collected using a survey from an Indian institute of the Meerut region with a total of 1022 students.

RESULT: Data analysis and research findings showed that Random Forest and K-Nearest Neighbour Algorithms outperforms than other classifiers in predicting the dependent variables with better accuracy rate, precision, and recall value in this study.

CONCLUSION: The research findings will help the designers and software development to design learning applications considering the perspective of students with respect to 5G technology.
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Application of Artificial Intelligence for the Optimization of Hydropower Energy Generation
Appears in:
inis 21(28): e1

Authors:
Krishna Kumar, R. P. Saini

Published:
6th Aug 2021

Abstract:
Hydropower is one of the most promising sources of renewable energy. However, a substantial initial investment requires for the construction of large civil structures. Feasibility study, detailed project report preparation, construction planning, and timely execution of work are the important a…Hydropower is one of the most promising sources of renewable energy. However, a substantial initial investment requires
for the construction of large civil structures. Feasibility study, detailed project report preparation, construction planning, and
timely execution of work are the important activities of a hydropower plant. Energy generation in hydropower plants are
mainly depends on discharge and head. Therefore, an accurate estimation of discharge and head is important to decide the
plant capacity. Erosion, cavitation, and operation & maintenance are the key challenges in hydropower energy generation.
Artificial Intelligence (AI) has become popular, which can be utilized for site selection, parameters assessment, and operation
& maintenance optimization. In this paper, a literature review on applications of AI in hydropower has been presented, and
an attempt has also been made to identify the future potential areas of hydropower plants.
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Smart Grid Attacks and Countermeasures
Appears in:
inis 15(2): e4

Authors:
Eric McCary, Yang Xiao

Downloads:
2152

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.
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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:
1721

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:
1605

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:
1406

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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Security Issues in ProtoGENI
Appears in:
inis 16(6): e1

Authors:
Fnu Shalini, Yang Xiao, Bo Sun

Downloads:
1351

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:
1248

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:
1244

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:
1239

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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Attribution of Cyber Attacks on Industrial Control Systems
Appears in:
inis 16(7): e3

Authors:
Allan Cook, Andrew Nicholson, Helge Janicke, Leandros Maglaras, Richard Smith

Downloads:
1210

Abstract:
In order to deter or prosecute for cyber attacks on industrial control systems it is necessary to assign attribution to the attacker and define the type of attack so that international law enforcement agencies or national governments can decide on appropriate recourse. In this paper we identify the…In order to deter or prosecute for cyber attacks on industrial control systems it is necessary to assign attribution to the attacker and define the type of attack so that international law enforcement agencies or national governments can decide on appropriate recourse. In this paper we identify the current state of the art of attribution in industrial control systems. We highlight the critical differences between attribution in enterprise networks and attribution in industrial networks. In doing so we provide a roadmap for future research.
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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:
1201

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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Publisher: EAI
ISSN: 2410-0218
Number of Volumes: 8

Last Published: 2021-11-03

EAI Endorsed Transactions on Industrial Networks and Intelligent Systems

Trung Q. Duong1

Le Nguyen Bao2

Nguyen-Son Vo2

Trung Q. Duong¹

Le Nguyen Bao²

Nguyen-Son Vo²