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2nd International Conference on Cyber Warfare, Security and Space Computing, SpacSec 2024

作者： Yadav, Sameer Venkataratnam, Surepalli Srikaanth, P. Balaji Madhavi, Jetti Reddy, A. Basi Selvan, R. Senthamil Department of Commerce and Business Administration University of Allahabad Uttar Pradesh Prayagraj India Department of History Nizam College Osmania University Hyderabad India Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Campus Chennai India Department of Mathematics Malla Reddy Engineering College Medcha India Department of Computer Science and Engineering School of Computing Mohan Babu University Andhra Pradesh Tirupati India Department of Electronics and Communication Engineering Annamacharaya Institute of Technology and Sciences Andhra Pradesh Tirupati India

ISBN: (纸本)9783031734939

The Internet of Things (IoT) has swept the linked globe. Internet of Things devices are growing exponentially globally due to their great practicality. Securing such gadgets, however, has received surprisingly little attention. Network creators and administrators have no option but to install these devices with inadequate or non-existent security due to their many limitations. 2016, during the notorious Mirai botnet assault, saw similar devices being used to launch distributed denial-of-service attacks. For this reason, provide a simple authentication procedure for these devices. When developing our authentication system, we considered several factors, including portability, user and device registration, scalability, and more. The design was based on a three-tiered paradigm with devices at the cloud, fog, and edge levels. Additionally, suggested other post-quantum cryptography-based cypher suites for review and implementation. Additionally, provide a fail-safe approach to ensure deployed IoT devices may continue delivering services autonomously if an authenticating server fails. When ring learning with mistakes is used, our protocol performs quickly. Using a tool for automated assessment of Internet security protocols and submissions, we demonstrate that our authentication system is secure. Finally, for Internet of Things device authentication in a fog computing setting, provide a secure, hybrid, and speedy protocol. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Authentication Protocol

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Multitask learning multimodal network for chronic disease prediction

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Scientific Reports 2025年第1期15卷 1-12页

作者： Tsai, Hsinhan Yang, Ta-Wei Wu, Tien-Yi Tu, Ya-Chi Chen, Cheng-Lung Chou, Cheng-Fu Department of Computer Science and Information Engineering National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Laboratory Medicine Linkou Main Branch Chang Gung Memorial Hospital Taoyuan Taiwan Taiwan Space Agency Hsinchu Taiwan

Chronic diseases are a critical focus in the management of elderly health. Early disease prediction plays a vital role in achieving disease prevention and reducing the associated burden on individuals and healthcare systems. Traditionally, separate models were required to predict different diseases, a process that demanded significant time and computational resources. In this research, we utilized a nationwide dataset and proposed a multi-task learning approach combined with a multimodal disease prediction model. By leveraging patients’ medical records and personal information as input, the model predicts the risks of diabetes mellitus, heart disease, stroke, and hypertension simultaneously. This approach addresses the limitations of traditional methods by capturing the correlations between these diseases while maintaining strong predictive performance, even with a reduced number of features. Furthermore, our analysis of attention scores identified risk factors that align with previous research, enhancing the model’s interpretability and demonstrating its potential for real-world applications. © The Author(s) 2025.

关键词： Chronic disease Disease incidence prediction ICD code embedding Multi-task learning Multimodal network

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Fairness-Aware Incentive Mechanism for Multi-Server Federated Learning in Edge-Enabled Wireless Networks with Differential Privacy

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IEEE Transactions on Mobile Computing 2025年

作者： Yang, Yu Peng, Kai Wang, Shangguang Xu, Xiaolong Xiao, Peiyun Leung, Victor C.M Huaqiao University College of Engineering Quanzhou362021 China Beijing University of Posts and Telecommunications State Key Laboratory of Networking and Switching Technology Beijing100876 China Nanjing University of Information Science and Technology School of Software Nanjing China Nanjing University State Key Lab. for Novel Software Technology Nanjing210093 China Shenzhen University College of the Computer Science and Software Engineering Shenzhen518060 China University of British Columbia Department of Electrical and Computer Engineering VancouverBCV6T 1Z4 Canada

As a distributed machine learning method, federated learning (FL) can collaboratively train a global model with multiple devices without sharing the original data, thus protecting certain privacy. However, due to the strong heterogeneity of edge nodes (ENs) participating in FL, the quality of data uploaded to the parameter server (PS) varies significantly. Without an appropriate incentive mechanism, low-quality contributors may receive disproportionately high rewards, while high-quality contributors may lack sufficient motivation, leading to inefficient participation and suboptimal global model performance. Consequently, it is critical to develop an effective incentive mechanism to promote fairness for the FL process. To address the issues of existing FL incentive mechanisms lacking privacy protection performance analysis, we propose a fairness-aware incentive mechanism for multi-server FL in edge-enabled wireless differential privacy (DP) networks. Specifically, the wireless channel noise is used to provide DP protection for the local model gradients uploaded by ENs. Next, the interaction between the PSs and ENs is modeled as a Stackelberg game. Furthermore, we solve the Stackelberg game process using backward induction and theoretically propose optimal strategies for both the PSs and ENs. Finally, extensive numerical simulations using real datasets demonstrate the superior performance of our theoretical analysis of the proposed scheme. © 2002-2012 IEEE.

关键词： Federated learning

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User-Centric Data Management in Decentralized Internet of Behaviors System

arXiv

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arXiv 2025年

作者： Zhang, Shiqi Wu, Dapeng Wang, Honggang Wang, Ruyan School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing400065 China Advanced Network and Intelligent Connection Technology Key Laboratory of Chongqing Education Commission of China China Chongqing Key Laboratory of Ubiquitous Sensing and Networking China Department of Graduate Computer Science and Engineering Katz School of Science and Health Yeshiva University in New York City United States

The Internet of Behaviors (IoB) is an emerging concept that utilizes devices to collect human behavior and provide intelligent services. Although some research has focused on human behavior analysis and data collection within IoB, the associated security and privacy challenges remain insufficiently explored. This paper analyzes the security and privacy risks at different stages of behavioral data generating, uploading, and using, while also considering the dynamic characteristics of user activity areas. Then, we propose a blockchain-based distributed IoB data storage and sharing framework, which is categorized into sensing, processing, and management layers based on these stages. To accommodate both identity authentication and behavioral privacy, zero-knowledge proofs are used in the sensing layer to separate the correlation between behavior and identity, which is further extended to a distributed architecture for cross-domain authentication. In the processing layer, an improved consensus protocol is proposed to enhance the decision-making efficiency of distributed IoB by analyzing the geographical and computational capability of the servers. In the management layer, user permission differences and the privacy of access targets are considered. Different types of behavior are modeled as corresponding relationships between keys, and fine-grained secure access is achieved through function secret sharing. Simulation results demonstrate the effectiveness of the proposed framework in multi-scenario IoB, with average consensus and authentication times reduced by 74% and 56%, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Differential privacy

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Autonomous Decision-Making in Cyber-Physical Systems Using Double Deep Q-Learning and Multi-Agent Collaboration

Autonomous Decision-Making in Cyber-Physical Systems Using D...

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Advancement in Computation & computer Technologies (InCACCT), International Conference on

作者： Divyabarathi P Anbarasan R. Vasanthi R B. Leena Akshya J M. Sundarrajan Mani Deepak Choudhry Department of Artificial Intelligence and Data Science Bannari Amman Institute of Technology Erode Tamil Nadu India Department of Information Technology KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computer Science and Engineering Bharathidasan Engineering College Thirupathur Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543891

ISBN: (纸本)9798331543907

The autonomous decision-making capabilities of cyber-physical systems (CPS) establish key elements that optimize the management of resources while distributing tasks and maximizing system operational efficiency. Deep Q-Learning (DQN) represents one approach along with Twin Delayed Deep Deterministic Policy Gradient (TD3) and Multi-Agent Proximal Policy Optimization (PPO) that demonstrates capability in handling decision tasks. The existing models face multiple implementation challenges including inaccurate Q-value estimations weak multi-agent relationships and restricted scalability for dynamic systems. The inadequate performance of present methods for energy efficiency and latency alongside poor collaborative coordination led to the necessity of developing a new approach. The new framework DDQN-MAC takes double-deep Q-Learning and combines it with multi-agent collaboration technology to enhance decision-making abilities specific to challenging environments. The method solves overestimation bias through separate action selection and evaluation processes that use multi-agent collaboration for dynamic task distribution and resource control. The designed model demonstrates enhanced performance in multiple critical evaluation measures. Benchmarks indicate that DDQN-MAC attained a superior completion rate of 95.4% while demonstrating reduced latency performance at 12.5 ms versus the other methods DQN (18.7 ms) and Multi-Agent PPO (92.3%). The research provides fundamental groundwork to guide upcoming investigations in hybrid model development while advancing complex multi-agent system capabilities for enhanced autonomous navigational choices within variable operational environments.

关键词： Q-learning Decision making Collaboration Estimation Cyber-physical systems Dynamic scheduling Energy efficiency Resource management Vehicle dynamics Optimization

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Crow Search Algorithm for Energy-Efficient UAV Path Planning in Large-Scale Aerial Surveys

Crow Search Algorithm for Energy-Efficient UAV Path Planning...

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Device Intelligence, Computing and Communication Technologies, (DICCT), International Conference on

作者： S. Amutha S.V. Ramanan Selvendran Ganesan B. Leena Akshya J Mani Deepak Choudhry M. Sundarrajan Department of Computer Science and Engineering School of Computing Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Chennai Tamil Nadu India Department of Electronics and Communication Engineering PPG Institute of Technology Coimbatore Tamil Nadu India Department of Biomedical Engineering Mahendra Institute of Technology Namakkal Tamil Nadu India Department of Electronics and Communication Engineering KGiSL Institute of Technology Coimbatore Tamil Nadu India Department of Computational Intelligence SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Chennai Tamil Nadu India Department of Networking and Communications SRM Institute of Science and Technology Chennai Tamil Nadu India

ISBN: (数字)9798331543358

ISBN: (纸本)9798331543365

UAVs have been extremely influential for applications such as remote/sensing, disaster response, surveillance, and infrastructure monitoring. They are valuable in modern-day operations because they can conduct high-precision aerial surveys autonomously. Most of the existing trajectory planning approaches are based on metaheuristic algorithms of Particle Swarm Optimization (PSO), Genetic Algorithm (GA), and Ant Colony Optimization (ACO), which are capable of providing satisfactory results. Still, they are computationally expensive with slow convergence and sensitive to local optima. In this paper, the challenges raised above are addressed by an Optimized Trajectory Planning Framework based on the Crow Search Algorithm (CSA). It is based on a nature-inspired optimization technique that mimics the intelligent foraging behavior of crows. The proposed approach dynamically adjusts the UAV flight path by balancing the exploration and exploitation, to compute the optimal trajectory quickly and meanwhile to save energy and avoid the obstacle. Unlike other metaheuristic methods, the Crow Search Algorithm does not confront the premature convergence problem and can guarantee adaptive path optimization even under uncertain environmental conditions. Results show that the energy efficiency performance of the Crow Search Algorithm is 15 percent better, the performance in terms of coverage is 20 percent improved, and the speed of convergence is 30 percent faster compared to various state-of-the-art methods. These experimental findings demonstrate that integrating CSA into UAV trajectory planning offers high computational efficiency, obstacle avoidance, and mission endurance, rendering it a superior UAV trajectory planning algorithm for real-time aerial navigation and autonomous surveillance applications.

关键词： Surveys Trajectory planning Surveillance Heuristic algorithms Metaheuristics Autonomous aerial vehicles Energy efficiency Real-time systems Collision avoidance Convergence

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Trust Online Over-the-air Computation for Wireless Federated Learning

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IEEE Transactions on Mobile Computing 2025年

作者： Sun, Mingjie Zheng, Jie Du, Hongyang Zhang, Haijun Niyato, Dusit Kang, Jiawen Wang, Jiacheng Ren, Jie Gao, Ling Wang, Zheng Northwest University State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services School of Information Science and Technology Shaanxi Xian710127 China University of Hong Kong department of electrical and electronic engineering Hong Kong University of Science and Technology Beijing Institute of Artificial Intelligence Beijing100083 China Nanyang Technological University School of Computer Science and Engineering 639798 Singapore Guangdong University of Technology Automation of School Guangzhou510006 China Shaanxi Normal University School of Computer Science Xi'an710062 China University of Leeds School of Computing LS2 9JT United Kingdom

Using the wireless waveform superposition property, over-the-air computation (OAC) enables federated learning (FL) to achieve fast model aggregation. However, this computing paradigm is vulnerable to poisoning attacks due to the openness of a wireless channel over time, where malicious mobile devices can introduce cumulative errors for the global FL model in a time-varying wireless environment for each communication round. This article presents a trust online OAC (TO-OAC) scheme to minimize impacts on the global model introduced by malicious devices adjusting to dynamic attack and wireless channel fluctuations over time. TO-OAC achieves this by utilizing trustworthy security quantification of OAC for each FL training round. To optimize the cumulative training loss at the aggregation node with the long-term power and trust constraints of mobile devices, we propose a joint trust, power, and channel-aware algorithm to flexibly update local and global models in response to the dynamic changes in the wireless and secure environment. We analyze the performance limits for the aggregation of trust models, considering metrics for computation and communication through time. We then propose another trust online regularization over-the-air computation (TOR-OAC) as an improved version of the TO-OAC scheme to decrease convergence time while ensuring long-term trust and power limitation. Experimental results performed on real-life datasets show that the two proposed schemes (TO-OAC and TOR-OAC) outperform prior works, especially in noisy, time-varying wireless channels and malicious attacks. 2002-2012 IEEE.

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An Overview on Economic Analysis of Internet of Everything

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IEEE Communications Surveys and Tutorials 2025年

作者： Ding, Ningning Ouyang, Xiaomin Gao, Lin Huang, Jianwei Xing, Guoliang Guangzhou511453 China Hong Kong University of Science and Technology Department of Computer Science and Engineering 999077 Hong Kong Harbin Institute of Technology School of Electronics and Information Engineering Guangdong Provincial Key Laboratory of Aerospace Communication and Networking Technology Shenzhen China Chinese University of Hong Kong School of Science and Engineering Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen Key Laboratory of Crowd Intelligence Empowered Low-Carbon Energy Network CSIJRI Joint Research Centre on Smart Energy Storage Guangdong Shenzhen518172 China The Chinese University of Hong Kong Department of Information Engineering 999077 Hong Kong

The Internet of Everything (IoE) integrates people, processes, data, and things into a unified network, enabling highly interconnected systems with enhanced performance in terms of delay, reliability, and connection density. With the advent of next-generation networks, IoE has garnered significant attention for its transformative potential across various domains. However, while most existing studies focus on developing enabling technologies and deployment frameworks, the economic dimensions of IoE systems remain critically underexplored, despite being essential for ensuring their sustainability and scalability. This paper addresses this gap by providing a comprehensive review of the economic analysis of IoE systems. Specifically, we categorize existing research into four key aspects: information collection, information transmission, information fusion, and service application. We identify major economic challenges and propose modeling approaches, including game-theoretic and learning-based methods. Furthermore, we explore diverse IoE application scenarios, such as metaverse solutions and large-scale industrial systems, and summarize prevailing business patterns in the industry. By highlighting the economic dimension as a core component of IoE systems, this survey offers valuable insights into future research directions and outlines critical open challenges. © 1998-2012 IEEE.

关键词： Internet of Everything

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On Cost Aware Cloudlet Placement for Mobile Edge Computing

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IEEE/CAA Journal of Automatica Sinica 2019年第4期6卷 926-937页

作者： Qiang Fan Nirwan Ansari IEEE Advanced Networking Lab Department of Electrical and Computer EngineeringNew Jersey Institute of TechnologyNewarkUSA

As accessing computing resources from the remote cloud inherently incurs high end-to-end(E2E)delay for mobile users,cloudlets,which are deployed at the edge of a network,can potentially mitigate this *** some research works focus on allocating workloads among cloudlets,the cloudlet placement aiming to minimize the deployment cost(i.e.,consisting of both the cloudlet cost and average E2E delay cost)has not been addressed effectively so *** locations and number of cloudlets have a crucial impact on both the cloudlet cost in the network and average E2E delay of ***,in this paper,we propose the Cost Aware cloudlet PlAcement in moBiLe Edge computing(CAPABLE)strategy,where both the cloudlet cost and average E2E delay are considered in the cloudlet *** solve this problem,a Lagrangian heuristic algorithm is developed to achieve the suboptimal *** cloudlets are placed in the network,we also design a workload allocation scheme to minimize the E2E delay between users and their cloudlets by considering the user *** performance of CAPABLE has been validated by extensive simulations.

关键词： Cloudlet placement mobile cloud computing mobile edge computing

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Multi-Task Semantic Communications via Large Models

arXiv

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arXiv 2025年

作者： Ni, Wanli Qin, Zhijin Sun, Haofeng Tao, Xiaoming Han, Zhu Department of Electronic Engineering Tsinghua University Beijing100084 China State Key Laboratory of Space Network and Communications Beijing100084 China Beijing National Research Center for Information Science and Technology Beijing100084 China State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing100876 China Electrical and Computer Engineering University of Houston HoustonTX77004 United States

Artificial intelligence (AI) promises to revolutionize the design, optimization and management of next-generation communication systems. In this article, we explore the integration of large AI models (LAMs) into semantic communications (SemCom) by leveraging their multi-modal data processing and generation capabilities. Although LAMs bring unprecedented abilities to extract semantics from raw data, this integration entails multifaceted challenges including high resource demands, model complexity, and the need for adaptability across diverse modalities and tasks. To overcome these challenges, we propose a LAM-based multi-task SemCom (MTSC) architecture, which includes an adaptive model compression strategy and a federated split fine-tuning approach to facilitate the efficient deployment of LAM-based semantic models in resource-limited networks. Furthermore, a retrieval-augmented generation scheme is implemented to synthesize the most recent local and global knowledge bases to enhance the accuracy of semantic extraction and content generation, thereby improving the inference performance. Finally, simulation results demonstrate the efficacy of the proposed LAM-based MTSC architecture, highlighting the performance enhancements across various downstream tasks under varying channel conditions. Copyright © 2025, The Authors. All rights reserved.

关键词： Data integration

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