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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Albinali, Hussah Azzedin, Farag Riaz, Muhammad King Fahd University of Petroleum and Minerals Information & Computer Science Department Dhahran Saudi Arabia Imam Abdulrahman Bin Faisal University College of Computer Science and Information Technology Networks and Communication Department Dammam Saudi Arabia King Fahd University of Petroleum and Minerals Department of Mathematics Dhahran Saudi Arabia

ISBN: (纸本)9798350351637

The Internet of Things is rapidly advancing, enabling interactions with the physical world and presenting various applications. Routing Protocol over Low-Power and Lossy networks (RPL) is the routing protocol introduced by the IETF for IoT environments. RPL has been subjected to multiple attacks, making it essential to develop a model that defines its behavior. To achieve this, we propose DEVS-RPL, which employs the discrete event model to specify the normal behavior of network nodes in generating RPL messages. DEVS-RPL estimates the mean and frequency of each generated RPL message by defining the participant states and utilizing the time advance function. As a proof of concept, the normal network traffic produced by the Cooja simulator is compared to the RPL messages generated by the DEVS-RPL model. We find that the mean absolute percentage error (MAPE) of DEVS-RPL is less than 9%, indicating the high accuracy of the proposed model. © 2024 IEEE.

关键词： Discrete event simulation

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Answering the Call to Go Beyond Accuracy: An Online Tool for the Multidimensional Assessment of Decision Support systems

Answering the Call to Go Beyond Accuracy: An Online Tool for...

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17th International Joint Conference on Biomedical Engineering systems and Technologies, BIOSTEC 2024

作者： Natali, Chiara Campagner, Andrea Cabitza, Federico Department of Computer Science Systems and Communication University of Milano-Bicocca Milan Italy IRCCS Ospedale Galeazzi - Sant’Ambrogio Milan Italy

The research about, and use of, AI-based Decision Support systems (DSS) has been steadily increasing in the recent years: however, tools and techniques to validate and evaluate these systems in an holistic manner are still largely lacking, especially in regard to their potential impact on actual human decision-making. This paper challenges the accuracy-centric paradigm in DSS evaluation by introducing the nuanced, multi-dimensional approach of the DSS Quality Assessment Tool. Developed at MUDI Lab (University of Milano-Bicocca), this free, open-source tool supports the quality assessment of AI-based decision support systems (DSS) along six different and complementary dimensions: model robustness, data similarity, calibration, utility, data reliability and impact on human decision making. Each dimension is analyzed for its relevance in the Medical AI domain, the metrics employed, and their visualizations, designed according to the principle of vague visualizations to promote cognitive engagement. Such a tool can be instrumental to foster a culture of continuous oversight, outcome monitoring, and reflective technology assessment. © 2024 by SCITEPRESS–science and Technology Publications, Lda.

关键词： Data reliability

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Traffic Prediction- and Explainable Artificial Intelligence-Based Dynamic Routing in Software-Defined Elastic Optical networks

Traffic Prediction- and Explainable Artificial Intelligence-...

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IFIP Networking Conference

作者： Róża Goścień Department of Systems and Computer Networks Faculty of Information and Communication Technology Wroclaw University of Science and Technology Wroclaw Poland

ISBN: (数字)9783903176638

ISBN: (纸本)9798350390605

The recent research reveals a high potential and efficiency of machine learning (ML) algorithms applied to aid the optimization of telecommunication networks. Moreover, thanks to the tools of explainable artificial intelligence (XAI), it is possible to better understand and interpret the decisions of ML models, identify the features influencing them the most, and use that knowledge for further targeted optimization improvements. In this paper, we use these approaches to aid the optimization of the well-known and essential problem of dynamic routing in software-defined elastic optical networks (EONS). We use the XAI to verify which transmission and network state parameters influence the performance the most (expressed as a bandwidth blocking probability (BBP)) of a dynamic EON. The analysis reveals that two of the most relevant features are related to characteristics of the incoming traffic requests - the source node and the traffic prediction for the demand's source-destination nodes. Next, we use that data to propose six traffic prediction-and XAI-based demands ordering policies, which are used to improve a benchmark allocation algorithm. Then, we perform simulations to evaluate the policies' efficiency and compare them with reference methods. The results show that our targeted proposal is exceptionally efficient - the best policy allowed to reduce the BBP up to about 6.1%, representing 4203 Gbps of more served traffic compared to the best reference policy.

关键词： Machine learning algorithms Explainable AI Optical design Optical fiber networks Routing Prediction algorithms Resource management

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Reversible Data Hiding With Secret Encrypted Image Sharing and Adaptive Coding

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IEEE Internet of Things Journal 2025年第13期12卷 23933-23945页

作者： Fang, Guangtian Wang, Feng Zhao, Chenbin Qin, Chuan Chang, Ching-Chun Chang, Chin-Chen Fujian University of Technology College of Computer Science and Mathematics Fuzhou350118 China Fujian University of Technology College of Computer Science and Mathematics Key Laboratory of Big Data Mining and Applications Fuzhou350118 China Monash University Faculty of IT Department of Software Systems and Cybersecurity VIC3800 Australia Ministry of Education School of Cyber Science and Engineering Key Laboratory of Aerospace Information Security and Trusted Computing Wuhan430072 China University of Shanghai for Science and Technology School of Optical-Electrical and Computer Engineering Shanghai200093 China Feng Chia University Information and Communication Security Research Center Taichuang 40724 Taiwan Feng Chia University Department of Information Engineering and Computer Science Taichung40724 Taiwan

To ensure the security of image information and facilitate efficient management in the cloud, the utilization of reversible data hiding in encrypted images (RDHEI) has emerged as pivotal. However, most existing RDHEI schemes suffer from lower security and limited embedding capacity. To tackle these challenges, we propose a reversible data hiding with secret encrypted image sharing and adaptive coding scheme. Specifically, in the encryption phase, we introduce an improved secret sharing (SS) encryption method based on the chinese remainder theorem for polynomials (CRTP). This method not only improves the security of encrypted images but also vacates a larger room for embedding. In the embedding phase, we introduce an adaptive coding embedding approach using XOR preservation (XORP) and huffman coding, which provides high embedding capacity. Experimental results and security analysis demonstrate that our proposed encryption method achieves optimal values in security indicators for encrypted images, such as information entropy, histograms, NPCR and UACI. The proposed embedding method is superior to some state-of-the-art schemes in terms of embedding capacity. Furthermore, in datasets BOSSBase and BOWS2, the average embedding rates of the proposed embedding approach can reach 2.1745 bpp and 2.0656 bpp, respectively. © 2025 IEEE.

关键词： Cryptography Encryption Security Data mining Adaptive coding Correlation Cloud computing Polynomials Image coding Streaming media

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A Survey of Integrating Generative Artificial Intelligence and 6G Mobile Services: Architectures, Solutions, Technologies and Outlooks

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IEEE Transactions on Cognitive communications and Networking 2025年第3期11卷 1334-1356页

作者： Liu, Yi-Jing Du, Hongyang Xu, Xinyi Zhang, Ruichen Feng, Gang Cao, Bin Niyato, Dusit Kim, Dong In Jamalipour, Abbas Letaief, Khaled B. Tafazolli, Rahim University of Electronic Science and Technology of China National Key Lab on Wireless Communications Chengdu China University of Hong Kong Department of Electrical and Electronic Engineering Pok Fu Lam Hong Kong Nanyang Technological University College of Computing and Data Science Singapore Singapore Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing China Sungkyunkwan University Department of Electrical and Computer Engineering Suwon Korea Republic of University of Sydney School of Electrical and Computer Engineering Sydney Australia Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong Hong Kong University of Surrey Institute for Communication Systems School of Computer Science and Electronic Engineering United Kingdom

Generative artificial intelligence (GenAI) is rapidly driving a new phase of artificial intelligence revolution, marked by various applications such as ChatGPT, Sora and DeepSeek. With powerful capabilities in content generation, personalization and user interaction, GenAI can drive significant enhancements in various mobile services to satisfy individual user preferences. Meanwhile, mobile services offer substantial support for accessibility, functionality and intelligence of GenAI applications. However, current research on integrating GenAI and mobile services remains nascent, lacking necessary theoretical and technical support. In this survey, we break down a new vision to guide research on integrating GenAI and mobile services, aiming to improve 6G intelligence levels and enable 6G mobile networks to support the integration of GenAI and mobile services effectively. We begin by examining the necessity of integrating GenAI with 6G mobile services and exploring potential learning architectures of GenAI models within mobile networks. Next, we analyze potential solutions from two perspectives: how 6G mobile networks empower GenAI and how GenAI enables mobile services. We subsequently explore a promising use case, along with essential features and techniques, to facilitate the integration of GenAI and mobile services. Finally, we discuss future research directions, aiming at realizing intelligent 6G networks. © 2015 IEEE.

关键词： Generative adversarial networks

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A Review of Key Elements for Underwater Wireless Sensor networks

A Review of Key Elements for Underwater Wireless Sensor Netw...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Najim, Ali Hamza Al-Dulaimi, Aymen Mohammed Ali Alheeti, Khattab M. Mohd Satar, Nurhizam Safie Aliesawi, Salah A. Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq College of Engineering Techniques Department of Communication Technical Engineering Al-Farahidi University Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq

ISBN: (纸本)9798350369106

Underwater Wireless Sensor networks (UWSNs) are emerging and have huge prospects since they have various applications in oceanography, environmental monitoring, and underwater communication. These networks contain low-cost submerged sensors that are mostly connected by way of acoustic media. Sensors are relatively new to underwater research, and thanks to recent technological development, they can now function at different depths. In this article, the author explores what UWSNs must do to operate smoothly by exploring the basic features that they must meet for further development. Real-time Underwater communication remains an open problem due to underwater acoustic channels important for undersea data acquisition, undersea navigation, and undersea remote sensing. To mitigate such challenges and in light of the use of new technologies, researchers seek to improve the reliability and reliability of UWSNs. Finally, there is great potential with UWSNs for investigating and exploiting the underwater environment by improving scenarios of different domains in line with subaqueous data and communications. © 2024 IEEE.

关键词： Wireless sensor networks

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Intelligent Smart Grid Stability Predictive Model for Cyber-Physical Energy systems

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computer systems science & Engineering 2023年第2期44卷 1219-1231页

作者： Ashit Kumar Dutta Manal Al Faraj Yasser Albagory Mohammad zeid M Alzamil Abdul Rahaman Wahab Sait Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd DiriyahRiyadh13713Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl AhsaHofuf31982Kingdom of Saudi Arabia

A cyber physical energy system(CPES)involves a combination of pro-cessing,network,and physical *** smart grid plays a vital role in the CPES model where information technology(IT)can be related to the physical *** the same time,the machine learning(ML)modelsﬁnd useful for the smart grids integrated into the CPES for effective decision ***,the smart grids using ML and deep learning(DL)models are anticipated to lessen the requirement of placing many power plants for electricity *** this aspect,this study designs optimal multi-head attention based bidirectional long short term memory(OMHA-MBLSTM)technique for smart grid stability predic-tion in *** proposed OMHA-MBLSTM technique involves three subpro-cesses such as pre-processing,prediction,and hyperparameter *** OMHA-MBLSTM technique employs min-max normalization as a pre-proces-sing ***,the MBLSTM model is applied for the prediction of stability level of the smart grids in *** the same time,the moth swarm algorithm(MHA)is utilized for optimally modifying the hyperparameters involved in the MBLSTM *** ensure the enhanced outcomes of the OMHA-MBLSTM technique,a series of simulations were carried out and the results are inspected under several *** experimental results pointed out the better outcomes of the OMHA-MBLSTM technique over the recent models.

关键词： Stability prediction smart grid cyber physical energy systems deep learning data analytics moth swarm algorithm

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Hybrid near- and far-field three-stage beam training with beam split for RIS-assisted OFDM communications

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Frontiers of Information Technology & Electronic Engineering 2024年第12期25卷 1664-1678页

作者： Zhichao CHENG Shupei ZHANG Shu FU Boya DI School of Electronics Engineering and Computer Science Peking UniversityBeijing 100871China State Key Laboratory of Advanced Optical Communication Systems and Networks School of ElectronicsPeking UniversityBeijing 100871China Department of Microelectronics and Communication Engineering Chongqing UniversityChongqing 400044China

With the development of millimeter-wave(mmWave)communication systems,large-scale reconfigurable intelligent surfaces(RISs)have gained considerable attention as a promising technology for signal strength enhancement and coverage ***,as the antenna scale and bandwidth increase,RIS-assisted wideband orthogonal frequency division multiplexing(OFDM)communication systems face challenges due to the near-field range expansion and the beam split effect over the high-frequency band,complicating the acquisition of channel state information(CSI).To tackle these challenges,we present a codebook-based three-stage beam training scheme by using the beam split effect to bypass CSI ***,by analyzing the beam split effect in RIS-assisted OFDM communication systems,we propose a beam-split-aware codebook capable of covering both the near and far fields with fewer codewords compared to conventional narrow-band *** such a codebook,a three-stage beam training mechanism is adopted to obtain the optimal codeword with low time overhead,thereby facilitating subsequent *** results demonstrate that the proposed scheme outperforms existing near-and far-field codebook-based schemes in terms of the beam training resolution and sum rate in the hybrid near–far field.

关键词： Near-far field Beam split Codebook design Beam training Reconfigurable intelligent surfaces

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Edge-Driven Industrial Computing Power networks: Digital Twin-Empowered Service Provisioning by Hybrid Soft Actor-Critic

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 8095-8109页

作者： Zhang, Long Song, Deng-Ao Zhang, Hongliang Tian, Ni Zhuang, Zirui Niyato, Dusit Han, Zhu Hebei University of Engineering School of Information and Electrical Engineering Handan056038 China Peking University State Key Laboratory of Advanced Optical Communication Systems and Networks School of Electronics Beijing100871 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanyang Technological University College of Computing and Data Science 639798 Singapore University of Houston Department of Electrical and Computer Engineering HoustonTX77004 United States Kyung Hee University Department of Computer Science and Engineering Seoul446-701 Korea Republic of

With the proliferation of data-intensive industrial applications, the collaboration of computing powers among standalone edge servers is vital to provision such services for smart devices. In this paper, we propose an edge-driven industrial computing power network (CPN) by orchestrating the computing and network resources of edge servers through the centralized resource scheduling and decentralized task computing. However, efficient task offloading and collaborative processing is challenging, which requires higher degrees of network automation and intelligence. Therefore, we incorporate digital twins (DTs) into the edge-driven CPN architecture, where the DTs are created as the digital replicas to assist both the computation offloading and collaborative processing. A joint optimization problem of the computing power assignment, service association, task partition, and transmit power control is formulated for maximizing the system average weighted utility. Due to the temporal-spatial variability of tasks and the resulted dynamic environment, we transform the original problem as a Markov decision process aiming at maximizing the long-term average weighted utility. To efficiently handle the high-dimensional discrete-continuous action space, a hybrid soft actor-critic based deep reinforcement learning algorithm is developed for optimizing the joint design. Simulation results validate the superiority of our proposed algorithm over the benchmarks, showing the significant gains obtained by integrating DTs into the edge-driven CPN. © 1967-2012 IEEE.

关键词： Markov processes

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THE GAME PROBLEM OF ASSIGNING STAFF TO PROJECT IMPLEMENTATION

Decision Making: Applications in Management and Engineering

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Decision Making: Applications in Management and Engineering 2023年第2期6卷 691-721页

作者： Kowalska-Styczeń, Agnieszka Kravets, Petro Lytvyn, Vasyl Vysotska, Victoria Markiv, Oksana Faculty of Organization and Management Silesian University of Technology Zabrze Poland Information Systems and Networks Department Lviv Polytechnic National University Lviv Ukraine Institute of Computer Science Osnabrück University Osnabrück Germany

This article describes how to solve the game problem of assigning staff to work on projects based on the ontological approach. The stochastic game algorithm for colouring an undirected random graph has been used to plan project implementation. The stochastic game mathematical model has been described, and the self-learning Markov method has been used for its solution. It is highlighted that the goal of the players is to minimize the functions of average losses. The Markov recurrent method that provides the adaptive choice of colours for the vertices of the random graph based on dynamic vectors of mixed strategies, the values of which depend on the current losses of players has been used. A computer experiment was carried out, which confirmed the convergence of the stochastic game for the problem of colouring the random graph. In conclusion. the possibility of defining the procedure for appointing staff to implement projects has been justified. © 2023 American Meteorological Society. All rights reserved.

关键词： Stochastic systems

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