CITS 2024: The 2024 International Conference on Computer, Information and Telecommunication Systems

Bio

Sudeep Tanwar (Senior Member, IEEE) is working as a professor at the Nirma University, India. He is also a Visiting Professor with WSG University, Bydgoszczy, Poland, Jan Wyzykowski University, Poland, and the University of Pitesti, Romania. He is senior member of IEEE and also Vice Chair of IEEE Computer Society Gujarat Section. He received B.Tech in 2002 from Kurukshetra University, India, M.Tech (Honor’s) in 2009 from Guru Gobind Singh Indraprastha University, Delhi, India and Ph.D. in 2016 with specialization in Wireless Sensor Network. He has authored 05 books and edited 25 books, more than 400 technical articles, including top cited journals and conferences, such as IEEE TNSE, IEEE TVT, IEEE TII, IEEE TGCN, IEEE TCSC, IEEE IoTJ, IEEE NETWORKS, IEEE WCM, ICC, IWCMC, GLOBECOM, CITS, and INFOCOM. He initiated the research field of blockchain technology adoption in various verticals in the year 2017. His H-index as per Google Scholar and Scopus is 73 and 61, respectively. His research interests include blockchain technology, D2D communication, Deep Learning/machine Learning, wireless sensor networks, fog computing, smart grid, and the IoT. He is a member of the Technical Committee on Tactile Internet of IEEE Communication Society. Recently, He has been awarded a cash prize of Rs, 50,000 for publishing papers with 5+ Impact factor and publication of books with Springer, IET & CRC under the scheme of "Faculty Awards and Incentives" of Nirma University for the year 2019-2020. He has been awarded the Best Research Paper Awards from IEEE SCIoT 2022, IEEE IWCMC-2021, IEEE ICCCA-2021, IEEE GLOBECOM 2018, IEEE ICC 2019, and Springer ICRIC-2019. He has won Dr KW Wong Annual Best Paper Prize (with 750 USD) for 2021 sponsored by Elsevier (publishers of JISA). He has served many international conferences as a member of the Organizing Committee, such as the Publication Chair for FTNCT-2020, ICCIC 2020, and WiMob2019, and a General Chair for IC4S 2019, 2020, 2021, 2022, ICCSDF 2020, FTNCT 2021. He is also serving the editorial boards of COMCOM-Elsevier, IJCS-Wiley, Cyber Security and Applications- Elsevier, Frontiers of blockchain, SPY, Wiley, IJMIS journal of Inderscience, JCCE, and JSSS. He is also leading the ST Research Laboratory, where group members are working on the latest cutting-edge technologies.

Abstract

The convergence of Blockchain and Artificial Intelligence (AI) with 5G and beyond networks promises transformative advancements in telecommunications. This integration enhances security, trust, and efficiency in data management while optimizing network performance. Blockchain's decentralized ledger and AI's advanced analytics create a robust framework for managing 5G environments. This talk discusses opportunities including improved cybersecurity, efficient spectrum management, and autonomous network operations. Also, highlight challenges such as high computational demands, latency, interoperability, and standardization must be addressed. Discussed opportunities and challenges, providing insights into future research and practical implementations to achieve secure, intelligent, and efficient next-generation networks.

Bio

Jose L Marzo is Full Professor at the Computer Architecture and Technology Department at the University of Girona, Spain. He was with Telefonica at the engineering and development departments before joining academia in 1991. His research interests are in the fields of communication networks, complex networks control and management, and nanonetworks. He leads the research Lab in Broadband Communications and Distributed Systems at the University of Girona. He coordinated the participation of the research group in some national Spanish and European research projects. Jose L Marzo was adjunct Professor at the Department of Electrical and Computer Engineering, Kansas State University (2012-2019). Jose L Marzo has participated to the technical program committees and chairing sessions of several conferences, including SPECTS, IEEE Globecom, ICC and Infocom. He served as associated editor of the International Journal of Communications Systems. He has co-authored several papers published in international journals and presented in leading international conferences.

Abstract

The association of nanomachines, i.e. nanonetworks, is assumed to enlarge the capacities of single nanomachines. This interexchange of information let them cooperating for a common goal. Nowadays systems normally use electromagnetic signals to encode, send and receive information, whereas molecular transceivers, channel models or protocols, use molecules as a novel communication paradigm. During the talk, the architecture of future nanomachines and current developments are presented for a better understanding of nanonetwork scenarios in biomedical applications.

As a study case, the connectivity of a cohort of nanorobots in the circulatory system (blood) is presented. THz omnidirectional and optical directional communications are considered where ranges are around 10 mm. Connectivity results show a strong dependency of the diameter of the vessel, the larger the better.

Bio

Chi Lin received B.E. and Ph.D. degrees from Dalian University of Technology, Dalian, China, in 2008 and 2013, respectively. He served as an Assistant Professor at the School of Software, Dalian University of Technology form 2013-2017. Since 2017, he has been an Associate Professor with the School of Software, Dalian University of Technology. He has authored or co-authored more than 90 scientific papers in several journals and conferences, including MobiCom, INFOCOM, Ubicomp, ICDCS, ICNP, SECON, ICPP, IEEE/ACM ToN, IEEE TMC, and a special issue of SCIENCE. His research interests include pervasive computing and wireless sensor networks. In 2015, he was the recipient of the ACM Academic Rising Star award. In 2022, he was awarded the gold medal of the 2022 International Exhibition of Inventions Geneva.

Abstract

Wireless Rechargeable Sensor Networks (WRSNs) are networks of wireless sensors that can be recharged without physical connections. These networks consist of small, low-power devices equipped with sensors for monitoring and collecting data in various environments. The key feature of WRSNs is their ability to recharge their energy sources wirelessly, which is particularly useful for applications where frequent battery replacement or recharging is impractical or impossible. This report provides a detailed introduction to the concept, characteristics, existing issues, and commonly used solutions of WRSNs. The analysis includes aspects such as network modeling, charging modeling, charging scheduling, and charging efficiency. Special focus is placed on the latest advancements in complex environment modeling, 3D wireless charging, and Fresnel zone diffraction theory and system optimizations. Finally, future development directions are outlined.

Bio

Dr Saru Kumari is currently an Associate Professor with the Department of Mathematics, Chaudhary Charan Singh University, Meerut, Uttar Pradesh, India. She received her PhD degree in Mathematics in 2012 from Chaudhary Charan Singh University, Meerut, UP, India. She has published more than 290 research papers in reputed international journals and conferences, including more than 260 research papers in various SCI and SCIE Indexed Journals such as IEEE TDSC, IEEE TII, IEEE JBHI, IEEE T-ITS, IEEE IoTJ, Information Fusion, ACM TOIT, etc. She received the best paper award from Journal of Network and Computer Applications, Elsevier in 2020 and from IEEE Consumer Electronics Magazine in 2022. She is on the editorial board of more than a dozen of International Journals of high repute, under IEEE, Elsevier, Springer, Wiley and others including SCI and SCIE journals such as IEEE Transactions on Intelligent Transport Systems, (SCIE); IEEE Systems Journal, AEÜ - International Journal of Electronics and Communications, Elsevier (SCI); International Journal of Communication Systems, Wiley (SCIE); Concurrency and Computation: Practice and Experience, Wiley (SCIE); Telecommunication Systems, Springer (SCI); Human-centric Computing and Information sciences, Springer (SCIE); Transactions on Emerging Telecommunications Technologies; Wiley (SCIE), etc. She has served as the Guest Editor of many special issues in SCI and SCIE Journals under IEEE, Elsevier, Springer and Wiley. She has been involved in the research community as a Technical Program Committee (TPC) member or PC chair for more than a dozen of international conferences of high repute. She is also serving as a reviewer of dozens of reputed Journals, including SCI-Indexed Journals, under IEEE, Elsevier, Springer, Wiley, Taylor & Francis, etc. Her current research interests include applied cryptography, information security, Internet of Things, Information Fusion, blockchain technology, and Security.

Abstract

Federated learning (FL) is a decentralized method for training machine learning models, where local nodes retain data and only share learning parameters with other nodes, thereby enhancing privacy and data security. There are some potential attacks that compromise security and privacy concern of the data. In model poisoning attacks, some of the nodes are compromised, and the attacker may inject false information to degrade the performance of the consolidated model. Data poisoning and model poisoning, where adversaries share the incorrect local model with the central server, are potential methods for model poisoning attacks. Sybil attacks and collusion attacks are also paramount concerns for AI researchers, in which attackers create multiple malicious nodes that participate in the federated learning process, triggering their influence on the global model and attackers are able to generate the skewed model. Another attack is an inference attack, in which attackers attempt to get the sensitive information that was used in the training process. While in free-rider attacks, some nodes do not participate in the training process and keeps the advantages of globally-trained models. In model replacement attacks, attackers may share their own crafted model to update the global model as per their requirements. After several training rounds, an attacker gradually shifts the global model parameters by sharing a pre-trained malicious model. The gradient leakage attacks may deduce important insights from the gradients.

Byzantine node-based collusion attack, one of the most sensitive attacks will be discussed in detail. The possibility of collusion attacks occurring and their possible remedies will be explored in detail. Some algorithms based on robust aggregation methods, anomaly detection, reputation systems, redundancy, and cross-checking are quite popular. Particularly reputation based mechanism will be focussed on.

Bio

Panagiotis Katsaros is an Associate Professor at Aristotle University of Thessaloniki. He holds a Bachelor degree in Mathematics (1992), a Master of Science in Software Engineering (1993) and a Ph.D. in Computer Science (2002). His research interests lie in the areas of formal verification, Internet of Things, (cyber-)security, runtime monitoring, software engineering. Prof. Katsaros has coordinated 2 R&D projects on software and system security, 2 R&D projects on IoT, 2 R&D projects on spacecraft on-board software design and he has been principal investigator in 1 European R&D project on AI safety. He has published 36 journal articles, 79 conference papers, 3 books, 5 edited books (incl. 3 LNCS conference proceedings). Prof. Katsaros has served as the General Chair of the 21st edition of the European Joint Conferences on Theory and Practice of Software (ETAPS), the General Chair of the 6th Int. Symp. on Model-Based Safety and Assessment (IMBSA), the General Chair & Program Co-chair of the 23rd International Conference on Runtime Verification (RV) and is currently reviewer in more than 13 international journals.

Abstract

With the formal analysis of adversarial behaviour we can either verify that a system (or protocol) satisfies essential qualitative (e.g. security-related) and quantitative (e.g. bounded power consumption) properties in the adversarial environment, or else detect vulnerabilities against the adversary at the system design, code or configuration levels. We focus on the recent advances of probabilistic verification techniques for specifying and verifying properties of stochastic systems. First we review some illustrative cases of formal analyses of attacks against the DNS, as well as the formal analysis of a social network in which multiple agents interact with each other via opinion influence relationships. In the latter case, we assume the existence of strategic entities who seek to maximise their influence in the convergence of the social network, whereby we are interested for a quantitative reward property that represents the final opinion of the agents. These case studies cover a wide range of probabilistic models (Discrete Time Markov Chains, Continuous Time Markov Chains, Markov Decision Processes and Stochastic Games), probabilistic reachability properties and reward properties. We study various kinds of adversarial behaviour, including those that preserve and those that don’t preserve the structure of the probabilistic model. Finally, we present the utility of the most recent probabilistic verification techniques, namely parametric model checking, parameter synthesis (e.g. finding the worst-case attack), model repair (e.g. finding the countermeasure withe least cost) and robustness analysis (e.g. estimate the resilience of the system/protocol against the adversary).

Bio

Jordi Paillissé Vilanova received a degree in Telecommunications Engineering (2013) and a PhD in Computer Architecture (2021) from the Technical University of Catalonia - BarcelonaTech. He has been a visiting student at École Polytechnique Fédérale de Lausanne (Switzerland), and a visiting researcher at Cisco Systems (USA) and Universitat de Girona (Spain). He is currently a postdoctoral researcher in the Barcelona Neural Networking Center, working at the intersection of computer networks and machine learning. His main research interests are AI-assisted network modeling, future Internet architectures, Software-Defined Networking and blockchain applications for the Internet.

Abstract

Digital Twins are a technology to create virtual replicas of existing physical systems. They are being used in different industries with varying degrees of sophistication, but they do not seem to take off in the field of computer networks. In this talk, we'll attempt to give a formal definition of a Digital Twin for computer networks, and describe several relevant use cases. We will also present the concept of a Digital Twin to predict the performance of computer networks, such as the delay or loss of a flow, and review the options to implement this type of a Network Digital Twin, especially regarding AI-based alternatives. Finally, we'll outline several open challenges in the area that limit widespread deployment of such Network Digital Twins.