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Distributed private preserving learning based chaotic encryption framework for cognitive healthcare IoT systems

informatics in Medicine Unlocked 2024年 49卷

作者： Nabi, Shaik Abdul Kalpana, Ponugoti Chandra, N. Subhash Smitha, L. Naresh, K. Ezugwu, Absalom E. Abualigah, Laith Department of Computer Science and Engineering AVN Institute of Engineering and Technology Department of Computer Science and Engineering CVR College of Engineering Department of Information Technology G Narayanamma Institute of Technology and Science Department of Computer Science and Engineering TKR College of Engineering & Technology Unit for Data Science and Computing North-West University Computer Science Department Al Al-Bayt University MEU Research Unit Middle East University Applied Science Research Center Applied Science Private University Jadara Research Center Jadara University Artificial Intelligence and Sensing Technologies (AIST) Research Center University of Tabuk School of Engineering and Technology Sunway University Malaysia

In the field of cognitive healthcare Internet of Things (CH-IoT), there is a strong demand for reliable and minimally intrusive smart gadgets that consistently acquire, analyse, and obtain the confidential health details of the individual. In fact, CH-IoT is empowered with artificial intelligence (AI) to transmute a fewer operational inputs into actionable, intelligent actions through the digitization of medical healthcare data. However, these systems consume more network complexity, interaction, and overhead costs, while inducing a blend of susceptibility and confidentiality issues. In support of this complexity, these cognitive systems need centralised data collection and to be gathered and analysed, which affects scalability issues and adds fuel to privacy and security breaches. Even though it possesses greater intricacy in its potential application, a substantial factor is maintaining the private preservation of healthcare data against the growing attacks. Thus, this paper presents a distributed privacy-preserving, chaotic encryption-based framework that can be deployed for CH-IoT systems to safeguard sensitive data against message modification, denial of service (DoS), and man-in-the-middle attacks (MIM), guaranteeing privacy and data integrity. The proposed framework integrated the federated learning layered hybrid chaotic encryption strategies by investigating through examination the learning infrastructure of convolutional neural networks (CNN). In the examination, the complete framework was carried out in the Tensorflow Federated Learning Libraries (FLL), and numerous performance metrics such as accuracy, precision, recall, f1-score, transmission efficiency, and overhead ratio were measured and contrasted with the various existing frameworks. For the intensive analysis, formal and informal security experiments were also conducted by NIST (National Institute of Science and Technology). The analytical results illustrate the importance of the proposed framewor

关键词： Cognitive health care Distributed learning Federated learning libraries Internet of things NIST Privacy and security breaches

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Temporal Spectrum Cartography in Low-Altitude Economy Networks: A Generative AI Framework with Multi-Agent Learning

arXiv

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arXiv 2025年

作者： Zhao, Changyuan Zhang, Ruichen Wang, Jiacheng Niyato, Dusit Sun, Geng Du, Hongyang Li, Zan Jamalipour, Abbas Kim, Dong In College of Computing and Data Science Nanyang Technological University Singapore CNRS@CREATE 1 Create Way 08-01 Create Tower 138602 Singapore College of Computer Science and Technology Jilin University 130012 China Department of Electrical and Electronic Engineering University of Hong Kong Hong Kong State Key Laboratory of Integrated Services Networks Xidian University Xian710071 China School of Electrical and Computer Engineering University of Sydney Australia Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of

This paper introduces a two-stage generative AI (GenAI) framework tailored for temporal spectrum cartography in low-altitude economy networks (LAENets). LAENets, characterized by diverse aerial devices such as UAVs, rely heavily on wireless communication technologies while facing challenges, including spectrum congestion and dynamic environmental interference. Traditional spectrum cartography methods have limitations in handling the temporal and spatial complexities inherent to these networks. Addressing these challenges, the proposed framework first employs a Reconstructive Masked Autoencoder (RecMAE) capable of accurately reconstructing spectrum maps from sparse and temporally varying sensor data using a novel dual-mask mechanism. This approach significantly enhances the precision of reconstructed radio frequency (RF) power maps. In the second stage, the Multi-agent Diffusion Policy (MADP) method integrates diffusion-based reinforcement learning to optimize the trajectories of dynamic UAV sensors. By leveraging temporal-attention encoding, this method effectively manages spatial exploration and exploitation to minimize cumulative reconstruction errors. Extensive numerical experiments validate that this integrated GenAI framework outperforms traditional interpolation methods and deep learning baselines by achieving 57.35% and 88.68% reconstruction error reduction, respectively. The proposed trajectory planner substantially improves spectrum map accuracy, reconstruction stability, and sensor deployment efficiency in dynamically evolving low-altitude environments. © 2025, CC BY.

关键词： Deep reinforcement learning

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An Innovative IoT-Driven Smart Streetlight Management System Design

An Innovative IoT-Driven Smart Streetlight Management System...

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International Conference on Computational and Characterization Techniques in engineering & Sciences (CCTES)

作者： Shermina Shekaina Justin P. Thenmozhi Jissy Ann George T. Ananth Kumar Christo Ananth Department of Computing Muscat College Sultanate of Oman Electrical Engineering Department Princess Nourah Bint Abdulrahman University Kingdom of Saudi Arabia Dept. of Electronics and Communication Engineering St.Joseph's College of Engineering (An Autonomous Institution) Chennai India College of ICT British University of Bahrain Kingdom of Bahrain Computer Science and Engineering IFET College of Engineering Viluppuram India Department of Electronics and Communication Engineering Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology Uzbekistan Adjunct Faculty Samarkand State University Chennai India

ISBN: (数字)9798350364699

ISBN: (纸本)9798350364705

This research introduces a cutting-edge approach to optimizing the management of streetlight systems through automation by using IoT. Traditional methods often suffer from inefficiencies, leading to unnecessary energy consumption and maintenance costs. In this study, we propose a novel automated streetlight management system that leverages advanced technologies such as IoT sensors, machine learning algorithms, and smart scheduling mechanisms. By dynamically adjusting lighting levels based on real-time factors such as traffic density, weather conditions, and time of day, our system aims to achieve significant energy savings while ensuring optimal illumination for pedestrians and vehicles. We present the design architecture of our system, along with experimental results demonstrating its effectiveness in various urban environments. Our findings highlight the potential of automated streetlight management systems to enhance sustainability, improve safety, and reduce operational expenses for municipalities and urban planners.

关键词： Machine learning algorithms Urban areas Lighting Energy efficiency Sensor systems Scheduling Safety Internet of Things Sustainable development System analysis and design

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ExClique: An Express Consensus Algorithm for High-Speed Transaction Process in Blockchains

arXiv

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arXiv 2025年

作者： Zhao, Chonghe Zhou, Yipeng Zhang, Shengli Sheng, Quan Z. Zhang, Yang Wen, Shiting College of Electronic and Information Engineering Shenzhen University Shenzhen China School of Computing Macquarie University Sydney Australia Anuradha and Vikas Sinha Department of Data Science University of North Texas DentonTX United States School of Computer and Data Engineering NingboTech University Ningbo China

Proof of Authority (PoA) plays a pivotal role in blockchains for reaching consensus. Clique, which selects consensus nodes to generate blocks with a pre-determined order, is the most popular implementation of PoA due to its low communication overhead and energy consumption. However, our study unveils that the speed to process transactions by Clique is severely restricted by 1) the long communication delay of full blocks (each containing a certain number of transactions) between consensus nodes;and 2) occurrences of no-turn blocks, generated by no-turn nodes if an in-turn block generation fails. Consequently, Clique struggles to support distributed applications requiring a high transaction processing speed, e.g., online gaming. To overcome this deficiency, we propose an Express Clique (ExClique) algorithm by improving Clique from two perspectives: compacting blocks for broadcasting to shorten communication delay and prohibiting the occurrences of no-turn blocks. For performance evaluation, we implement ExClique by modifying Geth of Ethereum, the software implementing Clique, and deploy a permissioned blockchain network by using container technology. The experimental results show that ExClique achieves a substantial enhancement in transactions per second (TPS). Specifically, it boosts TPS by 2.25× in a typical network with 21 consensus nodes and an impressive 7.01× in a large-scale network with 101 consensus nodes when compared to Clique. © 2025, CC BY.

关键词： Blockchain

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Navigating Flood Prediction Complexities: Harnessing Fuzzy Expert Systems and Real-Time Sensor Integration 7

Navigating Flood Prediction Complexities: Harnessing Fuzzy E...

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7th International Conference on Image Information Processing, ICIIP 2023

作者： Madasamy, Sridhar Praveena, K.V.S. Sreedevi, Vaitla Basi Reddy, A. Nirosha, Ch. Naik, S. K. Lokesh Cognizant Technology Solution 500 Frank W. Burr Boulevard TeaneckNJ07666 United States Vignan's Institute of Information Technology Department of Management Studies Andhra Pradesh Visakhapatnam India Department of Cse Maisammaguda Kompally Telagana State Secunderabad500100 India Mohan Babu University School of Computing Department of Computer Science And Engineering Andhra Pradesh Tirupati India St. Martin's Engineering College Department of Eee Secunderabad Telangana Dhulapally India Mlr Institute of Technology Department of Cse Hyderabad Dundigal India

ISBN: (纸本)9798350371406

Fuzzy Expert Systems (FESs) stand at the forefront as a pioneering amalgamation of fuzzy logic and expert systems, constituting a robust framework primed for intricate decision-making processes. Within this context, this work presents an innovative paradigm that harnesses the profound capabilities of FESs to elevate the precision of flood prediction through the seamless integration of real-time sensor data. The domain of flood prediction takes on paramount significance in the realm of disaster management, necessitating the timely and accurate identification of potential risks. By orchestrating the nuanced cognitive processes of fuzzy logic with the specialized knowledge inherent to expert systems, the methodology introduced herein serves as a tangible demonstration of FESs' potential to fundamentally reshape the landscape of flood prediction strategies. This pioneering approach, empowered by the dynamic fusion of disparate fields, stands as a testament to the evolving nature of predictive analytics. By effectively leveraging real-time sensor data, the developed system adeptly maneuvers through the intricate dynamics of flooding events, culminating in predictions that transcend traditional forecasts. The outcomes of these predictions, actionable in nature, engender a profound sense of empowerment in the realm of proactive decision-making, thereby minimizing the potential ramifications of impending flood disasters. Within this work, a thorough examination unfolds, encompassing the foundational underpinnings, the meticulous design, and the execution of the fuzzy expert system. Furthermore, the implications of this approach are rigorously tested through its application in real-world flood scenarios, providing tangible evidence of its efficacy. This holistic exploration thus unravels the transformative potential of FESs in the context of augmenting flood prediction precision, thus forming a cornerstone of disaster management frameworks that embrace resilience and adaptab

关键词： Decision making

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Feasibility Retargeting for Multi-contact Teleoperation and Physical Interaction

arXiv

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arXiv 2023年

作者： Rouxel, Quentin Wen, Ruoshi Li, Zhibin Tiseo, Carlo Mouret, Jean-Baptiste Ivaldi, Serena Inria CNRS Université de Lorraine France Institute for Perception Action and Behaviour School of Informatics University of Edinburgh United Kingdom Department of Computer Science University College London United Kingdom School of Engineering and Informatics University of Sussex United Kingdom

This short paper outlines two recent works [1, 2] on multi-contact teleoperation and the development of the SEIKO (Sequential Equilibrium Inverse Kinematic Optimization) framework. SEIKO adapts commands from the operator in real-time and ensures that the reference configuration sent to the underlying controller is feasible. Additionally, an admittance scheme is used to implement physical interaction, which is then combined with the operator's command and retargeted. SEIKO has been applied in simulations on various robots, including humanoid and quadruped robots designed for loco-manipulation. Furthermore, SEIKO has been tested on real hardware for bimanual heavy object carrying tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Inverse kinematics

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Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

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Computational Intelligence and Neuroscience 2022年第0期2022卷 4690936页

作者： Veeramakali, T. Shobanadevi, A. Nayak, Nihar Ranjan Kumar, Sumit Singhal, Sunita Subramanian, Manoharan Department of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Kattankulathur Chennai India Sri Venkateswara College of Engineering Technology Chittoor India Indian Institute of Management Kozhikode India Department of Computer Science and Engineering Manipal University Jaipur Jaipur303007 India Department of Computer Science School of Informatics and Electrical Engineering Hachalu Hundesa Campus Ambo University Ambo Ethiopia

A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person's abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area. © 2022 T. Veeramakali et al.

关键词： Diseases

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BSH-Det3D: Improving 3D Object Detection with BEV Shape Heatmap

arXiv

引用

arXiv 2023年

作者： Shen, You Zhang, Yunzhou Wu, Yanmin Wang, Zhenyu Yang, Linghao Coleman, Sonya Kerr, Dermot College of Information Science and Engineering Northeastern University Shenyang110819 China School of Electronic and Computer Engineering Peking University Shenzhen China Faculty of Robotics and Engineering Northeastern University Shenyang China The Department of Electronic and Computer Engineering Technical University in Munich Germany School of Computing Engineering and Intelligent Systems Ulster University United Kingdom

The progress of LiDAR-based 3D object detection has significantly enhanced developments in autonomous driving and robotics. However, due to the limitations of LiDAR sensors, object shapes suffer from deterioration in occluded and distant areas, which creates a fundamental challenge to 3D perception. Existing methods estimate specific 3D shapes and achieve remarkable performance. However, these methods rely on extensive computation and memory, causing imbalances between accuracy and real-time performance. To tackle this challenge, we propose a novel LiDAR-based 3D object detection model named BSH-Det3D, which applies an effective way to enhance spatial features by estimating complete shapes from a bird's eye view (BEV). Specifically, we design the Pillar-based Shape Completion (PSC) module to predict the probability of occupancy whether a pillar contains object shapes. The PSC module generates a BEV shape heatmap for each scene. After integrating with heatmaps, BSH-Det3D can provide additional information in shape deterioration areas and generate high-quality 3D proposals. We also design an attention-based densification fusion module (ADF) to adaptively associate the sparse features with heatmaps and raw points. The ADF module integrates the advantages of points and shapes knowledge with negligible overheads. Extensive experiments on the KITTI benchmark achieve state-of-the-art (SOTA) performance in terms of accuracy and speed, demonstrating the efficiency and flexibility of BSH-Det3D. The source code is available on https://***/mystorm16/BSH-Det3D. Copyright © 2023, The Authors. All rights reserved.

关键词： Object detection

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Multiuser Resource Allocation for Semantic-Relay-Aided Text Transmissions

Multiuser Resource Allocation for Semantic-Relay-Aided Text ...

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IEEE Globecom Workshops

作者： Zeyang Hu Tianyu Liu Changsheng You Zhaohui Yang Mingzhe Chen Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China College of Information Science and Electronic Engineering ZheJiang University Hangzhou China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA

Semantic communication (SemCom) is an emerging technology that extracts useful meaning from data and sends only relevant semantic information. Thus, it has the great potential to improve the spectrum efficiency of conventional wireless systems with bit transmissions, especially in low signal-to-noise ratio (SNR) and small bandwidth regions. However, the existing works have mostly overlooked the constraints of mobile devices, which may not have sufficient capabilities to implement resource-demanding semantic encoder/decoder based on deep learning. To address this issue, we propose in this paper a new semantic relay (SemRelay), which is equipped with a semantic receiver to assist multiuser text transmissions. Specifically, the SemRelay decodes semantic information from a base station and forwards it to the users using conventional bit transmission, hence effectively improving text transmission efficiency. To study the multiuser resource allocation, we formulate an optimization problem to maximize the multiuser weighted sum-rate by jointly designing the SemRelay transmit power allocation and system bandwidth allocation. Although this problem is non-convex and hence challenging to solve, we propose an efficient algorithm to obtain its high-quality suboptimal solution by using the block coordinate descent method. Last, numerical results show the effectiveness of the proposed algorithm as well as superior performance of the proposed SemRelay over the conventional decode-and-forward (DF) relay, especially in small bandwidth region.

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Physics-informed Cyber-Attack Detection in Wind Farms

Physics-informed Cyber-Attack Detection in Wind Farms

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GLOBECOM 2022 - 2022 IEEE Global Communications Conference

作者： Faris Alotibi David Tipper Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh PA USA School of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (纸本)9781665435413

The growth of wind power generation is accelerating worldwide. However, wind turbines need to be carefully controlled to maintain structural stability and optimal power generation. The state of the turbine and power generated is digitized and transmitted over communication networks for physical component monitoring, control, and data management. Motivated by creating power outages, financial loss, and physical wind turbine damage, attackers could craft and inject falsified network traffic to maliciously influence control actions. In this paper, we propose a physics-informed machine learning anomaly detection approach. The mechanism is utilized at each wind turbine, where generated power signals are validated based on laws of physics and incorporated into machine learning algorithm to detect anomalies. We show that the proposed combined physics and machine learning approach improves the accuracy of detecting falsification attacks compared with using machine learning alone.

关键词： Machine learning algorithms Machine learning Telecommunication traffic Wind farms Wind power generation Wind turbines Structural engineering

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