Portal de Periódicos da CAPES

Guest editorial: Deep learning‐based intelligent communication systems: Using big data analytics

2022; Institution of Engineering and Technology; Volume: 16; Issue: 5 Linguagem: Inglês

10.1049/cmu2.12374

1751-8636

Rohit Sharma, Qin Xin, Patrick Siarry, Wei‐Chiang Hong,

Internet of Things and AI

Deep learning and big data analytics can be attributed to recent trends and opportunities in many research activities and areas such as bioinformatics, beyond 5G and 6G communications, healthcare, internet of things (IoT), manufacturing business and social networks. Big data analytics and deep learning are sought-after and fastest-growing techniques for the enhancement of information and communication technology (ICT), and recent approaches are providing unexpected solutions that once seemed unachievable. Applications of big data analytics and deep learning in 5G and 6G are able to facilitate many new features in network management and operations, and 5G and beyond communication systems are expected to provide services with massive connectivity, ultra-low latency, extremely high security, extremely low energy consumption, and ultra-high data-rate. The main focus of this special issue is on deep learning and big data analytics to process and analyze data in 5G and 6G applications. The special issue includes novel studies about big data, IoT, Industry 4.0 applications, machine learning, deep learning solutions, 5th generation, and 6th generation technologies. There were in total nineteen papers accepted for publication in this special issue through careful peer review and revisions, and all are covered under the overarching theme of deep learning-based intelligent communication systems. The summary of every topic is given below. However, it is strongly encouraged to read the full paper if interested. Saeed et al., in their paper 'A comprehensive review on the users' identity privacy for 5G networks', aim to shed light on the survey about user privacy for 5G networks, which continues the identity and location privacy. Also, it discusses most of the studies which handle the user identifications in authentication, paging, and location update. The paper discusses various privacy issues in 5G network which use IMSI in clear text or join the temporary identities: TMSI and C-RNTI with IMSI to disclose the privacy of user indentity. After that the paper studies many proposed solutions which discuss user privacy (identity and location) and concludes that each of these studies has advantages and disadvantages for its proposed solutions. Elfatih et al., in their paper 'Internet of vehicle's resource management in 5G networks using AI technologies: Current status and trends', discuss and provide a comprehensive detail for resource allocation and management for IoV over 5G RAN network utilizing AI techniques. In addition, an extensive discussion of AI technologies that promise to be adopted and contributed to IoV and V2X applications is presented. The presented reviews in these areas have not taken into account the significance of integrating the multi-layers of vehicular network architecture for each AI strategy and how to be tailored for rapid and dynamic topology problems. Hence, this paper adresses these problems by describing how sophisticated and deep vehicle network architecture can be enhanced by AI techniques for layer-by-layer resources management and allocation problems. Hasan et al., in their paper 'A review on security threats, vulnerabilities, and counter measures of 5G enabled internet-of-medical-things', review the applications of the internet of medical things (IoMT) that has gained major attention as an ecosystem of connected clinical systems, computing systems, and medical sensors geared towards improving the quality of healthcare services. The 5G based AI technology can revolute the perception of healthcare and lifestyle. In light of the importance of IoT platforms and 5G networks, the purpose of this proposed research work is to identify threats that could undermine the integrity, privacy, and security of IoMT systems. Also, the novel blockchain-based approaches can help in improving the confidentiality of the IoMT network. It has been discovered that IoMT is vulnerable to various types of attacks, including denial of service (DoS), malware, and eavesdropping attack. In addition, IoMT is exposed to various vulnerabilities, such as security, privacy, and confidentiality. Le in his paper 'A comprehensive survey of imbalanced learning methods for bankruptcy prediction', gives a review about imbalanced learning methods. This study first reviews several state-of-the-art approaches for handling this problem in bankruptcy prediction, including an over sampling based (OSB) framework, a cost-sensitive method (the C Boost algorithm), a combination of resampling techniques and a cost-sensitive framework, and an ensemble-based model (the XGBS algorithm). The author also conducts empirical experiments to evaluate the methods surveyed here in terms of two performance metrics; the area under the ROC curve and the geometric mean. The results show that the ensemble-based model outperforms other methods in terms of bankruptcy prediction on the KB dataset. Poongodi et al., in their paper '5G Based blockchain network for authentic and ethical keyword search engine', carry out a proposed 5G-based blockchain network architecture for an encrypted keyword search engine. The suggested model permits to play out all connections amongst different users and mini-base stations through the use of diverse access nodes points and network brokers. It also helps to comprehend the complete application of blockchain technology, wherein the distribution of numerous digital ledgers and smart contracts were acknowledged between each network entity. Moreover, complete utilization of cryptocurrency is realized at essential points of the network layer to lessen the effect of interference rate and streamline the spectrum sharing when requested by the user. Alshammari et al., in their paper 'Technology-driven 5G enabled e-healthcare system during COVID-19 pandemic', reveal that most people receive information from social networking sites, health professionals, and television without facing any challenges. The analysis shows that, during the COVID-19 pandemic, about 42% of respondents felt tense always or most of the time on a daily basis. Only 28.6% of respondents felt tense sometimes, whereas the remainder (about 30%) did not feel tense in relation to the COVID-19 crisis. Satisfaction with COVID-19-related information is also positively correlated with COVID-19-related information literacy (r = 0.53, p < 0.01) that is also positively correlated with depression or emotion, anxiety, and stress (r = -0.15, p < 0.05). The long-term pandemic is creating several psychological symptoms including anxiety, stress, and depression, irrespective of age. Natarajan et al., in their paper 'An IoT and machine learning-based routing protocol for reconfigurable engineering application', present an upgradable cross-layer routing protocol based on CR-IoT to improve routing efficiency and optimize data transmission in a reconfigurable network. In this context, the system is developing a distributed controller which is designed with multiple activities, including load balancing, neighbourhood sensing and machine-learning path construction. The proposed approach is based on network traffic and load and various other network metrics including energy efficiency, network capacity and interference, on average of 2 bps/Hz/W. The trials are carried out with conventional models, demonstrating the residual energy and resource scalability and robustness of the reconfigurable CR-IoT Pandey et al., in their paper 'Lyapunov optimization machine learning resource allocation approach for uplink underlaid D2D communication in 5G networks', formulate the maximization of uplink and overall system capacity with resource management, which guarantees the signal to interference noise ratio for the D2D users. The optimization is a mixed-integer non-linear problem that uses the Lyapunov optimization method to optimize the BER value and an iterative algorithm to optimize the power value with different constraints. After attaining the optimized value, SVM (support vector machine) technique is utilized to ensure the spectral efficiency of the overall system in autonomous mode. Simulation results show that the proposed method provides higher reliability and power efficiency with higher system capacity in comparison to prevailing technologies. Liang et al., in their paper 'A new model path for the development of smart leisure sports tourism industry based on 5G technology', adopt the literature method to learn the theoretical basis of 5G technology and smart tourism in depth, establish a multi-dimensional resource allocation model for the smart leisure sports tourism industry, and conduct research on the influencing factors, information sources, channel factors and other aspects of the tourism industry. The general public's search for tourism strategies and attractions, food and specialty products, the use of online search information channels accounted for 70.3% and 69.3%, which further shows that the development of 5G technology has promoted the transformation and development of the sports tourism industry. Khan et al., in their paper '3D convolutional neural networks based automatic modulation classification in the presence of channel noise', consider the problem of multiclass (eight classes) classification of modulated signals (binary phase shift keying, quadrature phase shift keying, 16 and 64 quadrature amplitude modulation corrupted by additive white Gaussian noise, Rician and Rayleigh fading channels) using architectures in both frequency and spatial domains while deploying three approaches for data augmentation, such as random zoomed in/out, random shift and random weak Gaussian blurring augmentation techniques with a cross-validation (CV) based hyperparameter selection statistical approach. Simulation results testify the performance of 10-fold CV without augmentation in the spatial domain to be the best while the worst performing method happens to be 10-fold CV without augmentation in the frequency domain and learning in the spatial domain to be better than learning in the frequency domain. Chen et al., in their paper 'Resource electronic database for measuring regional cultural influence based on machine learning big data', aim to build a resource electronic database for measuring regional cultural influence through the current hot big data technology, and to provide some reference suggestions and data resources for the harmonious development of regional culture. In this article, the authors investigate the current cultural development in various regions of China and its impact on the development of Chinese culture and the culture of the world through literary research. Considering the current state of cultural development in the region, this article determines the key functional requirements for building an electronic database of cultural impact measurement resources in the region. In the specific process of designing the database, big data mining algorithms and machine learning classification and prediction algorithms are used to collect, categorize and process the data resources of the regional cultural influence measurement database. In the analysis of the measurement of regional cultural influence, this paper uses the regional cultural pattern index to evaluate and predict the distribution, concentration, prosperity and influence of regional culture. Duggal et al., in their paper 'A sequential roadmap to Industry 6.0: Exploring future manufacturing trends', scroll through patent pathways and intellectual developments throughout industrial revolutions listing significant products and services that landmarked each revolution up to Industry 4.0. The research pools of Industry 4.0 are classified and explored. A lack of human–machine workforce synergy in Industry 4.0 and the nascent 'customized manufacturing' concept is addressed in subsequent sections. The paper classifies two expected phases of Industry 5.0, highlighting the subdomains touted to be its focal areas. Gourisaria et al., in their paper 'Data science appositeness in diabetes mellitus diagnosis for healthcare systems of developing nations' use various machine learning, deep learning, and data dimensionality reduction techniques to detect diabetes mellitus. The research is principally conducted on two datasets, first from the Frankfurt hospital, Germany, second from the UCI repository. Models such as support vector machines, naïve Bayes, and random forests are implemented to classify diabetic patients from non-diabetic ones. Subsequently, after hyperparameter tuning, a comparative study on the results is done and the most prominent model promoted. This process is repeated for the datasets with reduced dimensionality using linear discriminant analysis (LDA) and principal component analysis (PCA). For the Frankfurt, Germany dataset, k-nearest neighbours showed the best accuracy of 98.2%, and the random forest classifier for the UCI repository showed 99.2%. Abbasi et al., in their paper 'An intelligent method for reducing the overhead of analyzing big data flows in OpenFlow switch', focus on developing a dynamic replacement method. This intelligent method utilizes the statistical features of the traffic flows in the table to select a table for replacement and makes use of the popularity of flows in the flow table for replacing entries and updating the flow table. The method aims to evaluate the existing entries according to the history of the activities of the flow, which was neglected in previous studies. For this purpose, the author uses the 'importance' feature which has been introduced in OpenFlow 1.4. Mohanty et al., in their paper 'Identification and evaluation of the effective criteria for detection of congestion in a smart city', propose a novel congestion detection system based on the combination of k-means clustering and analytical hierarchy process. A transport network is created in the simulation of urban mobility (SUMO) simulator. After receiving the parameters of vehicles from the simulator in a congested junction area, the key parameters are extracted by using the k-means clustering technique and mathematical mean algorithm. This key parameter is utilized in analytical hierarchy process to detect the highest priorities parameter. Based on that parameter the congestion is detected in a particular lane. Yadav et al., in their paper 'A secure data transmission and efficient data balancing approach for 5G based IOT data using UUDIS-ECC and LSRHS-CNN algorithms', propose a technique that contains authentication, destination selection, validation, secure DT, and also LB phases. The user and also the device are permitted to send the data towards the destination if they are authenticated. The UDDIS-ECC is employed for secure DT. For improving the SL, the SiP hash function is utilized and the 5G IoT data is balanced by employing the LSRHS-CNN algorithm. By deeming the input data's tasks, the LB is managed. Afterward, the performance analysis is conducted. Analysis for secure DT and also LB are the two parts wherein the analysis is handled. Centred upon the ET, DT, along with SL, the proposed UUDIS-ECC is analogized with the existent ECC, RSA, DES, and ECDSA in the secure DT analysis. Moorthy et al., in their paper 'Reduction of satellite images size in 5G networks using machine learning algorithms', propose a method which is implemented with a combination of intra-coding and machine learning algorithms. The standard compression technique does not give better results due to degradation of pixels, lack of spatial and spectral information. This paper enriches progressive results by reducing satellite images for transmission of data in IoT and 5G wireless networks, which qualitative results are compared by standard compression technique with suitable parameters. Li in his paper 'SWOT analysis of e-commerce development of rural tourism farmers' professional cooperatives in the era of big data', analyzes the e-commerce development strategy of China's rural tourism cooperatives in detail, and uses the analytic hierarchy process to analyze the establishment of the green development of e-commerce tourism business, affecting external opportunities and threats. This makes it possible to explore the sustainable development path for the follow-up development of e-commerce tourism business, which is conducive to the sustainable development path of rural tourism e-commerce tourism, and achieves multi-win business, environmental and social benefits. The experimental results of this paper show that through the calculation of the quadrangle of my country's tourism e-commerce enterprise development strategy, M1 = 0.0089, M2 = 0.0029, M3 = 0.0012, M4 = 0.0038, and M1 > M4 > M2 > M3 can be obtained. Singh et al., in their paper 'LoRa based intelligent soil and weather condition monitoring with internet of things for precision agriculture in smart cities', present the design of an intelligent irrigation system based on soil and weather conditions. The soil and weather parameters are selected through various research articles in Agriculture 4.0 and ML. The paper also juxtaposes the designed weather station with various patents developed. The system developed in this paper provides a cost-effective and state-of-the-art solution to local weather monitoring. Rohit Sharma is an associate professor in the Department of Electronics and Communication Engineering, SRM Institute of Science and Technology, India. He is an active member of ISTE, IEEE, ICS, IAENG, and IACSIT. He is an editorial board member and reviewer for more than 12 international journals and conferences, including IEEE Access and IEEE Internet of Things Journal. He has served as a book editor for seven different titles to be published by CRC Press, Taylor & Francis Group, USA and Apple Academic Press, CRC Press, Taylor & Francis Group, USA, and Springer. He has received the Young Researcher Award at the 2nd Global Outreach Research and Education Summit & Awards 2019 hosted by the Global Outreach Research & Education Association (GOREA). He has served as a guest editor in the SCI journal of Elsevier. He has actively organized various international conferences. He has served as an editor and organizing chair to the 3rd Springer International Conference on Microelectronics and Telecommunication (2019), IEEE International Conference on Microelectronics and Telecommunication (2018), IEEE International Conference on Microelectronics and Telecommunication (ICMETE-2016), and technical committee member of CSMA2017, EEWC 2017, IWMSE2017, ICG2016, and ICCEIS2016. Qin Xin received his PhD from the Department of Computer Science at the University of Liverpool, UK in December 2004. Currently, he is working as a professor of Computer Science and Faculty Research Leader in the Faculty of Science and Technology at the University of the Faroe Islands (UoFI), Faroe Islands. Prior to joining UoFI, he had held various research positions in world-leading universities and research laboratories including a Senior Research Fellowship at Universite Catholique de Louvain, Belgium, Research Scientist/Postdoctoral Research Fellowship at Simula Research Laboratory, Norway and Postdoctoral Research Fellowship at the University of Bergen, Norway. His main research focus is on design and analysis of sequential, parallel and distributed algorithms for various communication and optimization problems in wireless communication networks, as well as cryptography and digital currencies including quantum money. Moreover, he also investigates the combinatorial optimization problems with applications in Bioinformatics, Data Mining and Space Research. Currently, Prof. Dr. Xin is serving on the management committee board of Denmark for several EU ICT projects. Prof. Dr. Xin has produced more than 111 peer reviewed scientific papers. His works have been published in leading international conferences and journals, such as ICALP, ACM PODC, SWAT, IEEE MASS, ISAAC, SIROCCO, IEEE ICC, Algorithmica, Theoretical Computer Science, Distributed Computing, IEEE Transactions on Computers, Journal of Parallel and Distributed Computing, IEEE Transactions on Dielectrics and Electrical Insulation, IEEE Transactions on Sustainable Computing, ACM Transactions on Internet Technology, IEEE Transactions on Network Science and Engineering, ACM Transactions on Asian and Low-Resource Language Information Processing, and Advances in Space Research. He has been very actively involved in the services for the community in terms of acting (or acted) on various positions (e.g., Session Chair, Member of Technical Program Committee, Symposium Organizer and Local Organization Co-chair) for numerous international leading conferences in the fields of distributed computing, wireless communications and ubiquitous intelligence and computing, including IEEE MASS, IEEE LCN, ACM SAC, IEEE ICC, IEEE Globecom, IEEE WCNC, IEEE VTC, IFIP NPC, IEEE Sarnoff and so on. He is the organizing committee chair for the 17th and 18th Scandinavian Symposium and Workshops on Algorithm Theory (SWAT 2020 and SWAT 2022, Torshavn, Faroe Islands). Currently, he also serves on the editorial board for more than ten international journals. Patrick Siarry received the Ph.D. degree in computer science and optimization from University Paris VI, France, in 1986, and the Doctorate of Sciences (Habilitation) degree in computer science and optimization from University Paris XI, France in 1994. He was first involved in the development of analog and digital models of nuclear power plants with Electricité de France, Paris. Since 1995, he has been a Professor of Automatics and Informatics with Université Paris-Est Créteil, France. His main research interests include computer-aided design of electronic circuits, cognitive intelligence, and the applications of new stochastic global optimization heuristics to various engineering fields, also including the fitting of process models to experimental data, the learning of fuzzy rule bases, and of neural networks. Wei-Chiang Hong is a professor in the Department of Information Management at the Oriental Institute of Technology, Taiwan. His research interests mainly include computational intelligence (neural networks and evolutionary computation) and applications of forecasting technology (ARIMA, support vectorregression, and chaos theory). In 2012, his paper was evaluated as 'Top Cited Article 2007–2011' by Applied Mathematical Modelling (Elsevier). In 2014, he was elected to be awarded as 'Outstanding Professor Award' by the Far Eastern Y. Z. Hsu Science and Technology Memorial Foundation (Taiwan). In the same year, he was awarded the 'Taiwan Inaugural Scopus Young Researcher Award–Computer Science' by Elsevier, in the Presidents' Forum of Southeast and South Asia and Taiwan Universities. In 2015, he was recognized in the 'Top 10 Best Reviewers' of Applied Energy 2014. In 2017, he was recognized in the 'Top 10 Best Reviewers of Applied Energy 2016. Guest Editorial: Deep Learning-based Intelligent Communication Systems: Using Big Data Analytics.