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IoT-Aware Real-Time Healthcare Diagnostic Framework For Diabetes Using Wearable Sensors through Deep Reinforcement Learning

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IEEE Internet of Things Journal 2025年第11期12卷 18183-18194页

作者： Farman, Haleem Shahzad, Yasir Jan, Bilal Nasralla, Moustafa M. Sallam, Karam M. Munasinghe, Kumudu Jamalipour, Abbas Prince Sultan University Smart Systems Engineering Lab Department of Communications and Networks Engineering College of Engineering Riyadh Saudi Arabia University of Peshawar Department of Computer Science Pakistan FATA University Department of Computer Science Kohat Pakistan University of Sharjah Computer Science Department Sharjah United Arab Emirates University of Canberra School of Information Technology and Systems ACT2617 Australia The University of Sydney School of Electrical and Information Engineering NSW2006 Australia

Machine Learning (ML) with 5G technology has revolutionized smart healthcare. It has helped improve the quality of care, such as real-time analysis, decision-making, patient monitoring, and personalized treatments. In this paper, a 5G aware real-time diabetes prediction framework is proposed using optimized Bidirectional Long Short-Term Memory (Bi-LSTM) with Deep Reinforcement Learning (DRL). Bi-LSTM can analyze time-series data in forward and backward passes on patient health metrics, such as Blood Glucose (BG) levels of a diabetic patient, to identify patterns and trends and make predictions about future health outcomes. A local dataset using a wearable sensor is collected of 10 Type-2 diabetic patients, encompassing daily BG levels at different times, alongside additional parameters such as blood pressure and body weight. The proposed framework leverages a dataset of 1,830 data points to forecast glucose levels for the following day. It also harnesses DRL to improve and optimize the model's future predictive performance. The obtained results are evaluated with other ML algorithms to validate the effectiveness of the proposed framework, which shows an improvement from 93.1% to 98.6% accuracy. The patient's diabetic condition is categorized using the Surveillance Error Grid (SEG) to increase the clinical impact of glucose prediction and make informed decisions. The results show that Bi-LSTM-DRL is an effective approach to predicting glucose levels in real-time and can adapt to health-related changes and optimize its predictions accordingly. © 2014 IEEE.

关键词： Deep reinforcement learning

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Lip-Audio Modality Fusion for Deep Forgery Video Detection

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computers, Materials & Continua 2025年第2期82卷 3499-3515页

作者： Yong Liu Zhiyu Wang Shouling Ji Daofu Gong Lanxin Cheng Ruosi Cheng College of Cyberspace Security Information Engineering UniversityZhengzhou450001China Research Institute of Intelligent Networks Zhejiang LabHangzhou311121China College of Computer Science and Technology Zhejiang UniversityHangzhou310027China Henan Key Laboratory of Cyberspace Situation Awareness Zhengzhou450001China Key Laboratory of Cyberspace Security Ministry of EducationZhengzhou450001China

In response to the problem of traditional methods ignoring audio modality tampering, this study aims to explore an effective deep forgery video detection technique that improves detection precision and reliability by fusing lip images and audio signals. The main method used is lip-audio matching detection technology based on the Siamese neural network, combined with MFCC (Mel Frequency Cepstrum Coefficient) feature extraction of band-pass filters, an improved dual-branch Siamese network structure, and a two-stream network structure design. Firstly, the video stream is preprocessed to extract lip images, and the audio stream is preprocessed to extract MFCC features. Then, these features are processed separately through the two branches of the Siamese network. Finally, the model is trained and optimized through fully connected layers and loss functions. The experimental results show that the testing accuracy of the model in this study on the LRW (Lip Reading in the Wild) dataset reaches 92.3%;the recall rate is 94.3%;the F1 score is 93.3%, significantly better than the results of CNN (Convolutional Neural networks) and LSTM (Long Short-Term Memory) models. In the validation of multi-resolution image streams, the highest accuracy of dual-resolution image streams reaches 94%. Band-pass filters can effectively improve the signal-to-noise ratio of deep forgery video detection when processing different types of audio signals. The real-time processing performance of the model is also excellent, and it achieves an average score of up to 5 in user research. These data demonstrate that the method proposed in this study can effectively fuse visual and audio information in deep forgery video detection, accurately identify inconsistencies between video and audio, and thus verify the effectiveness of lip-audio modality fusion technology in improving detection performance.

关键词： Deep forgery video detection lip-audio modality fusion mel frequency cepstrum coefficient siamese neural network band-pass filter

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Reliable and Efficient Data Collection in UAV based IoT networks

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IEEE Communications Surveys and Tutorials 2025年

作者： Poorvi, Joshi Kalita, Alakesh Gurusamy, Mohan National University of Singapore Communications and Networks Lab Department of Electrical and Computer Engineering Singapore Dhanbad Mathematics and Computing Department India

IoT involves sensors for monitoring and wireless networks for efficient communication. However, resource-constrained IoT devices and limitations in existing wireless technologies hinder its full potential. Integrating UAVs into IoT networks can address some challenges by expanding its coverage, providing security, and bringing computing closer to IoT devices. Nevertheless, effective data collection in UAV-assisted IoT networks is hampered by factors, including dynamic UAV behaviour, environmental variables, connectivity instability, and security considerations. In this survey, we first explore various communication technologies for UAV-IoT networks, performance metrics, and simulation tools, followed by a categorization of the UAV-IoT system based on the service model of UAV, mode of operation of UAV and UE during data collection, and network topology. As this article primarily emphasizes reliable and efficient data collection in UAV-assisted IoT networks, we briefly discuss existing research on data accuracy and consistency, network connectivity, and data security and privacy to provide insights into reliable data collection. Additionally, we discuss efficient data collection strategies in UAV-based IoT networks, covering trajectory planning and constraint management, sensor network clustering, data aggregation, and UAV swarm formations. Further, advanced AI techniques are discussed, which enable efficient and reliable data transfer in UAV-IoT systems. Finally, we discuss future challenges and their possible solutions using different approaches such as Decentralized Reinforcement and Federated Learning, Generative AI, and hybrid optical-RF communication links with integration of blockchain for reliable, efficient, and secure data collection in UAV-assisted IoT networks. © 2025 IEEE. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Automated Traffic Light Control System for Ambulance Using Laser Signals and Raspberry Pi

Automated Traffic Light Control System for Ambulance Using L...

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Anusha M S Uttam Upendra Hegde N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Networks & IoT Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Networks & IoT Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

Urban traffic congestion poses a major challenge, particularly for emergency services like ambulances, where delays can have life-threatening consequences. This paper proposes a novel, cost-efficient Automated Traffic Light Control System aimed at prioritizing ambulances at busy intersections, thereby reducing delays and ensuring faster response times. The system utilizes Raspberry Pi Zero 2W, Laser Diodes (KY-008), and Light Dependent Resistors (LDRs) to detect ambulances and dynamically control traffic lights. Unlike traditional systems relying on GPS or RFID technologies, this laser-based approach offers greater precision and affordability through the use of modulated laser signals. Experimental results reveal that this system reduces ambulance response times by 35%. The tests were conducted under simulated urban conditions, highlighting the system's capability to outperform conventional solutions. The simplicity of integration and real-time responsiveness underscore its potential for widespread urban adoption.

关键词： Resistors Semiconductor lasers Traffic control Emergency services Road safety Real-time systems Delays Time factors Traffic congestion Radiofrequency identification

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Wireless Communications for Concentrated Solar Power Fields

IEEE Transactions on Green Communications and Networking

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IEEE Transactions on Green Communications and Networking 2025年

作者： Rahman, Aisha B Siraj, Md Sadman Tsiropoulou, Eirini Eleni Arizona State University Performance and Resource Optimization in Networks - PROTON Lab School of Electrical Computer and Energy Engineering TempeAZ United States

The control of heliostats in existing Concentrated Solar Power (CSP) fields is performed based on wired communications, resulting in high installation, maintenance, and operation cost. This paper introduces a wireless communication system for CSP fields based on the Integrated Access and Backhaul (IAB) technology, a distributed resource management mechanism, and an artificial intelligent or AI-based clustering mechanism for the heliostats. The proposed system ensures efficient communication between the heliostats and their cluster-heads as well as among the cluster-heads themselves in order to communicate with the Central Station (CS) residing in the CSP field. The clustering is optimized using the Optimistic Q-learning with Upper Confidence Bound (OQ-UCB) algorithm, accounting for the CSP fields' topology and channel gain characteristics. Furthermore, a game-theoretic approach is employed to optimize the transmission power and bandwidth allocation, aiming to enhance the energy efficiency while meeting the stringent latency demands of the heliostat autocalibration. Experiments and numerical results from a CSP field with 7,683 heliostats validate the system's efficacy in maintaining robust wireless communication and energy efficiency and highlight the proposed model's capability to support the wireless communication needs of CSP fields. © 2017 IEEE.

关键词： Concentrated solar power

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Symbiotic Resource Pricing in the Computing Continuum Era

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IEEE Transactions on Mobile Computing 2025年

作者： Rahman, Aisha B Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Arizona State University Performance and Resource Optimization in Networks - Proton Lab School of Electrical Computer and Energy Engineering TempeAZ85287 United States National Technical University of Athens School of Electrical and Computer Engineering Athens15780 Greece

Though extensive research efforts have been devoted to the problem of computing resource pricing, they mainly focus on single computing paradigms. In this paper, we provide a holistic approach to this problem, by treating the whole computing continuum, consisting of cloud, edge, and fog computing providers, simultaneously offering their resources to the users. Within such a complex setting, we establish the concept of symbiotic computing resource pricing and sharing, where the computing providers and the users coexist within a mutually beneficial ecosystem, sharing services and resources as a means of ensuring their business survival and service satisfaction. Under this prism, we introduce two key pricing families, namely the non-cooperative one which involves competition and is treated through game theoretic approaches, and the cooperative resource pricing (full or partial), which addresses complex scenarios through optimization and coalition. A thorough performance assessment is provided, through modeling and simulation, in order to highlight and quantify the key characteristics and tradeoffs of the various resource pricing approaches introduced. © 2002-2012 IEEE.

关键词： Fog computing

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Use a Little Force to Move a Great Mass: A Jamming Leverage Strategy for Covert Communications

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IEEE Transactions on Information Forensics and Security 2025年 20卷 5015-5024页

作者： Wang, Tingting Li, Zan Si, Jiangbo Cheng, Zihao Gao, Yang Al-Dhahir, Naofal Integrated Service Networks Lab of Xidian University Xi’an China Air Force Engineering University Information and Navigation College Xi’an China The University of Texas at Dallas Department of Electrical and Computer Engineering RichardsonTX75080 United States

We propose a joint covert beamforming design and jamming strategy to protect the communication process between Alice and Bob from being discovered by Willie with the help of another pair of neutral nodes. Specifically, with the help of irrelevant communication parties that commonly exist in practical communication scenarios, Jammer increases his transmission power by interfering with the neutral receiver, thus indirectly increasing the interference to Willie, which can be viewed as leveraging the force to make a big impact with a small effect. In our designed beamformer, we jointly optimize the beam power allocation factor, Alice’s transmission power, and Jammer’s transmission power when Alice transmits, to maximize the covert rate, which also maximizes Alice’s transmission power. In the perfect channel state information (CSI) scenario, the transformed optimization problem is solved via a one-dimensional search method and CVX solver. Due to the solution’s high complexity, we further propose a method to determine the optimal power allocation factor. For the imperfect Willie’s CSI scenario, three cases are investigated: Alice to Willie imperfect CSI, Transmitter to Willie imperfect CSI, and Jammer to Willie imperfect CSI. We utilize the S-procedure to tackle the optimization problem. Simulation results demonstrate the effectiveness of our proposed strategy. © 2005-2012 IEEE.

关键词： Beamforming

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Detection of Brain Tumor Using a Deep Learning Approach

Detection of Brain Tumor Using a Deep Learning Approach

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Artificial Intelligence and Data engineering (AIDE), International Conference on

作者： Arun Kumar S Anand Kumar M Robin Rohit Vincent Anusha M S Uttam Upendra Hegde N Nauman Pasha Dept. of Computer Studies Symbiosis Institute of Computer Studies and Research (SICSR) Pune India Dept. of Information Science and Engineering Networks & IoT Lab Presidency University Bangaluru India Dept. of Computer Science and Engineering Bio-Intelligence Lab Presidency University Bangaluru India

ISBN: (数字)9798331527518

ISBN: (纸本)9798331527525

This study introduces a deep learning-based method for classifying brain tumors using a pre-trained VGG19 convolutional neural network (CNN). By leveraging transfer learning, we adapted the VGG19 model with custom fully connected layers and trained it on MRI images to differentiate between tumor and non-tumor cases. The model’s architecture includes dropout layers to minimize overfitting and enhance generalization while maintaining the robust feature extraction capabilities of the VGG19 base. We developed a Flask web application interface to make the model accessible via web-based user input, allowing users to upload MRI images and receive immediate predictions on tumor presence. Preliminary results show that proposed model can accurately classify brain tumor images with high accuracy, highlighting the potential of deep learning in automating brain tumor diagnostics. This tool could significantly aid clinicians in making rapid and accurate diagnoses, ultimately improving patient outcomes.

关键词： Deep learning Adaptation models Accuracy Magnetic resonance imaging Transfer learning Brain tumors Predictive models Brain modeling Convolutional neural networks Reliability

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SAFLA: Semantic-Aware Full Lifecycle Assurance for Intent-Driven networks

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IEEE Transactions on Cognitive Communications and Networking 2025年

作者： Kou, Shiwen Yang, Chungang Gurusamy, Mohan Xidian University The State Key Laboratory on Integrated Services Networks Xi’an710071 China Communication & Networks Lab Department of Electrical and Computer Engineering National University of Singapore SG117583 Singapore

Intent-Driven networks (IDNs) are designed to improve network management efficiency by transforming high-level intents into actionable configurations. Due to evolving user requirements and network dynamics, a semantic gap has emerged between declarative intents and underlying configurations, posing significant challenges to the assurance and reliability of IDNs. Despite promising preliminaries, existing intent assurance approaches, which mainly focus on monitoring intents’ Key Performance Indicators (KPIs), fail to bridge the semantic gap. This gap can result in configurations that technically meet KPIs from a top-down perspective but fail to accurately reflect the original network intent from a bottom-up view, leading to serious security risks and undermining overall objectives. To address this issue, we introduce SAFLA, a semantic-aware framework designed to ensure the intent’s full lifecycle. By seamlessly integrating top-down and bottom-up approaches, SAFLA not only enhances intent assurance by monitoring intent performance but also bridges the semantic gap between high-level intents and low-level configurations. This integration facilitates a self-healing mechanism for IDNs, reducing the need for manual intervention even under evolving requirements and network reconfigurations. Experimental results on software-defined Networking demonstrate the SAFLA’s feasibility and efficiency in intent assurance. © 2015 IEEE.

关键词： Network management

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Integrated Robotic Aerial Base Stations Deployment and Backhaul Design in 6G Multihop networks

Integrated Robotic Aerial Base Stations Deployment and Backh...

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IEEE Conference on Wireless Communications and Networking

作者： Wen Shang Yuan Liao Vasilis Friderikos Halim Yanikomeroglu Center of Telecommunication Research King's College London London U.K. Department of Systems and Computer Engineering Non-Terrestrial Networks Lab Carleton University Ottawa Canada

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

To overcome the limited endurance of traditional unmanned aerial vehicles (UAVs), we propose a network of robotic aerial base stations (RABSs) that can energy-efficiently anchor into tall urban landforms, such as lampposts. This approach enables the creation of a hyper-flexible wireless multi-hop network, designed to support green, densified, and dynamic network requirements, thereby ensuring reliable long-term coverage for the whole observed region. The proposed network infrastructure can concurrently address the backhaul link capacity bottleneck and support access link traffic demand in the millimeter-wave (mmWave) frequency band. Specifically, the RABSs grasping locations, resource blocks (RBs) assignment, and route flow control are simultaneously optimized to maximize the served traffic demands. The group of RABSs capitalizes on the fact that traffic distribution varies considerably across both time and space within a given geographical area. Hence, they are able to relocate to suitable locations, i.e., ‘follow’ the traffic demand as it unfolds to increase the overall network efficiency. To tackle the curse of dimensionality of the proposed mixed-integer problem, we propose a greedy algorithm to obtain a competitive solution with low computational complexity. A wide set of numerical investigations reveals that RABSs could improve the served traffic demand. For instance, compared to networks with randomly deployed fixed small cells, the proposed mode serves at most 65% more traffic demand.

关键词： 6G mobile communication Base stations Wireless networks Spread spectrum communication Pressing Reliability engineering Resource management Vehicle dynamics Robots Backhaul networks

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