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The Confluence of WSNs and 5G Technologies: A Game-Changing Synergy

The Confluence of WSNs and 5G Technologies: A Game-Changing ...

2024 International Conference on Sustainable Computing and Integrated Communication in Changing Landscape of AI, ICSCAI 2024

作者： Veeresh, G. Sreevani, N. Lakhanpal, Sorabh Dhasmana, Gulshan Nagini, Rallabandi Venkata Santoshi Saraswati Swetha Jabbar, Ahmed sabah Abed AL-Zahra New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Dundigal Hyderabad500043 India Lovely Professional University Phagwara India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun India Department of Information Technology GRIET Bachupally Telangana Hyderabad India Medical Laboratory Technology Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (纸本)9798350355338

5G and WSNs improve wireless communication infrastructure greatly. Wireless sensor networks (WSNs) gather and transfer data from many sources using small, self-sufficient sensor nodes. The next generation of mobile networks is 5G. This service lets you connect additional devices, speed up the internet, and reduce latency. This article investigates the complicated links between technologies to highlight their effects. This research examines how WSNs operate with 5G and if their unique characteristics are beneficial. WSNs' huge data collection and 5G's lightning-fast data transmission are benefits. The combination may benefit smart cities, healthcare, industrial robotics, environmental monitoring, and more. This article discusses WSNs and 5G integration origins, causes, research concerns, and remedies. Researchers are investigating Dynamic Spectrum Fusion and Traffic-Aware Data management (DSFTDR) to improve spectrum usage, traffic management, and energy-efficient data collecting. In-depth analysis of WSNs and 5G indicates their potential. It examines how this may make radio transmission speedier, more connected, and data driven. Understanding and employing this combination is crucial for satisfying current application demands and advancing wireless communications. WSNs and 5G advance wireless communication technologies for a connected future. © 2024 IEEE.

关键词： 5G mobile communication systems

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Large sequence models for sequential decision-making:a survey

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Frontiers of computer science 2023年第6期17卷 25-42页

作者： Muning WEN Runji LIN Hanjing WANG Yaodong YANG Ying WEN Luo MAI Jun WANG Haifeng ZHANG Weinan ZHANG School of Electronics Information and Electrical Engineering Shanghai Jiao Tong UniversityShanghai 200241China Digital Brain Lab Shanghai 201306China Institute of Automation Chinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Institute for Artificial Intelligence Peking UniversityBeijing 100091China School of Informatics The University of EdinburghEdinburgh EH89JUUK Department of Computer Science University College LondonLondon WC1E 6BTUK

Transformer architectures have facilitated the development of large-scale and general-purpose sequence models for prediction tasks in natural language processing and computer vision,e.g.,GPT-3 and Swin *** originally designed for prediction problems,it is natural to inquire about their suitability for sequential decision-making and reinforcement learning problems,which are typically beset by long-standing issues involving sample efficiency,credit assignment,and partial *** recent years,sequence models,especially the Transformer,have attracted increasing interest in the RL communities,spawning numerous approaches with notable effectiveness and *** survey presents a comprehensive overview of recent works aimed at solving sequential decision-making tasks with sequence models such as the Transformer,by discussing the connection between sequential decision-making and sequence modeling,and categorizing them based on the way they utilize the ***,this paper puts forth various potential avenues for future research intending to improve the effectiveness of large sequence models for sequential decision-making,encompassing theoretical foundations,network architectures,algorithms,and efficient training systems.

关键词： sequential decision-making sequence modeling the Transformer training system

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Research on Coverless Image Steganography

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International Journal of Network Security 2023年第1期25卷 25-31页

作者： Anggriani, Kurnia Wu, Nan-I Hwang, Min-Shiang Department of Computer Science & Information Engineering Asia University 500Lioufeng Rd.Wufeng Taichung41354 Taiwan Faculty of Engineering University of Bengkulu Indonesia Department of Digital Multimedia Lee-Ming Institute of Technology No.2-2Lijhuan Rd.Taishan TownshipCounty 243 Taipei Taiwan Department of Medical Research China Medical University Hospital China Medical University Taiwan

In recent years, Coverless Image Steganography (CIS) has become an essential research topic for many scholars. This research topic was initiated in 2015 under steganography without embedding. Accordingly, CIS was presented to withstand the inadequacies of the general steganography method against the risks of steganalysis tools. This is because information concealed in CIS has no modification left on the stego image. Conversely, CIS performs a mapping operation to select a stego image containing secret information. This paper attempts to examine the development of CIS over the past five years, summarizing and analyzing the benefits and challenges of the current methods used by each survey paper. Moreover, we present the experimental results in tables and graphs to provide a precise performance comparison. This is done to outline future research requirements and opportunities in the CIS research topic. For example, in the future, developing a CIS method that is resistant to steganalysis tools, has a large hiding capacity, and can be used in real-time applications would be ideal. © 2022,International Journal of Network Security. All Rights Reserved.

关键词： Mapping

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Distributed Deep Reinforcement Learning for Autonomous Iot Healthcare Devices in the Cloud 1

Distributed Deep Reinforcement Learning for Autonomous Iot H...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Shukla, Aasheesh Pokhariya, Hemant Singh Michaelson, Jacob Laxminarayanamma, K. Kumar, Mukesh Krishna, Om Gla University Department of Electronics and Communication Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

The ethical and philosophical problems concerning the cooperation of AI systems and human artists are also examined in this study. In addressing authorship, agency, and the very essence of creation, the changing position of artists as co-creators with intelligent algorithms is explored. It also looks at how AI can question and change conventional ideas of creative competence. Additionally, this study looks into how AI affects the promotion and distribution of art. AI-driven marketing tactics provide improved targeting of customers, personalized experiences, and optimal promotional efforts by utilizing insights based on data and predictive analytics. The study focuses on how these developments transform the relationships between galleries and artists and their patrons, ultimately fostering a more varied and inclusive art scene. The system's potential in many healthcare scenarios has been validated through simulations and practical experiments, which have received excellent feedback from healthcare providers. A critical review emphasizes the need to address practical deployment issues and data security concerns, while also highlighting the exciting convergence of IoT healthcare devices and DDRL. The paper ends with suggestions for more research, highlighting the significance of ethical issues, user interface improvements, and real-world validation. Through the smooth integration of DDRL-enhanced Internet of Things (IoT) medical equipment into clinical practice, our research eventually improves patient care and transforms the delivery of healthcare. © 2023 IEEE.

关键词： data processing healthcare Internet of Things learning process technology

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Editorial:Special Topic on Federated Learning for IoT and Edge Computing

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ZTE Communications 2022年第3期20卷 1-2页

作者： PAN Yi CUI Laizhong CAI Zhipeng LI Wei Faculty of Computer Science and Control Engineering at Shenzhen Institute of Advanced Technology Chinese Academy of SciencesChina the Academy of the United Nations Sciences and Technology Organization Ukrainian Academy of Engineering Sciences American Institute for Medical and Biological Engineering Institute of Engineering Technology the College of Computer Science and Software Engineering at Shenzhen University China Department of Computer Science and the College of Business at Georgia State University USA the Center for Distributed and High Performance Computing School of Computer ScienceThe University of Sydney the Australia-China Joint Research Center on Energy Informatics and Demand Response Technologies

Recent years have witnessed the proliferation of Internet of Things(IoT),in which billions of devices are connected to the Internet,generating an overwhelming amount of *** is challenging and infeasible to transfer an... 详细信息

关键词： cloud generating data.

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Improving Data Integrity for Gray Hole Attack Detection by using a Hash Signature Algorithm in WSN 5

Improving Data Integrity for Gray Hole Attack Detection by u...

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5th International Conference on Smart Systems and Inventive Technology, ICSSIT 2023

作者： Radhika, S. Srikanth, M.S. Anand, K. Saravanan, K. Southry, S. Sree Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India Reva University School Computer Science and Engineering Karnataka Bangalore India Rajalakshmi Engineering College Department of Computer Science and Engineering Rajalakshmi NagarThandalam Chennai India R.M.D Engineering College Department of Information Technology Kavaraipettai Chennai India Sona College of Technology Department of Electronics and Communication Engineering Tamil Nadu Salem India

ISBN: (纸本)9781665474672

Nowadays, Sensor nodes are utilized in widespread distribution. Thus, security issues are a significant apprehension in the Wireless Sensor Networks (WSN). The gray hole attack is a particularly damaging kind of assault that, in addition to bringing down WSNs, brings down whole networks and interferes with the flow of data. A hash signature algorithm (IGDH) is proposed to solve these problems to improve data integrity for Gray hole attack detection. During the process of route discovery in the network, this strategy makes use of an algorithm that generates a hash signature to detect the Gray hole attack. The recipient node gets the message from any sensor and verifies the sensor node hash signature. During data transmission, the recipient node checks the principal communication count (PCC) and size of the data packet;if the packet size is equal Accept the message;otherwise, the recipient node decides some packets are dropped and isolates that node is a grayhole attacker. The simulation results explain the proposed method has better packet delivery and minimizes the packet loss and miss detection ratio in the WSN. © 2023 IEEE.

关键词： Sensor nodes

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Advancing Urban Traffic Control with IoT and Deep Learning: A YOLOv8 and LSTM-based Adaptive Signal System

Advancing Urban Traffic Control with IoT and Deep Learning: ...

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2025 International Conference on Intelligent Computing and Control Systems, ICICCS 2025

作者： Priya, K. Priyadharshini, K. Krishnan, R. Santhana Raj, J. Relin Francis Settu, Imran Javeed Srinivasan, A. Ramco Institute of Technology Department of Computer Science and Engineering India SRM Institute of Science and Technology School of Computing College of Engineering and Technology Faculty of Engineering and Technology Department of Computing Technologies Chennai India SCAD College of Engineering and Technology Department of Electronics and Communication Engineering India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Electronics and Communication Engineering Chennai India Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Department of Electronics and Communication Engineering Chennai India Velammal College of Engineering and Technology Department of Information Technology Madurai India

ISBN: (纸本)9798331512088

Urban traffic congestion remains a significant challenge due to increasing vehicle density, inefficient signal control, and unpredictable traffic patterns. Traditional fixed-time signal systems fail to adapt dynamically to varying congestion levels, leading to increased delays, fuel consumption, and environmental pollution. To address these limitations, this research proposes an IoT and deep learning-based adaptive traffic signal system integrating YOLOv8 for real-time vehicle detection and LSTM for congestion prediction. Traffic data is collected using IoT sensors, including cameras, ultrasonic sensors, RFID modules, GPS trackers, and air quality sensors. YOLOv8 processes real-time camera feeds to detect vehicles, classify them based on type, and estimate traffic density. Concurrently, an LSTM-based predictive model analyzes historical traffic patterns and external factors such as peak hours and weather conditions to forecast congestion trends. The system dynamically adjusts traffic signal durations based on congestion probability scores, optimizing road utilization and minimizing vehicle waiting times. Extensive training and evaluation using real-world traffic datasets demonstrate significant improvements in congestion reduction compared to conventional traffic control methods. Performance metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) validate the reliability of the predictive model. Additionally, air quality monitoring highlights the environmental benefits of reduced emissions. This intelligent traffic management framework enhances urban mobility, improves emergency vehicle response times, and contributes to sustainable smart city development. © 2025 IEEE.

关键词： Adaptive Signal Control Air Quality Monitoring Congestion Prediction Deep Learning IoT LSTM Smart Cities Traffic Management YOLOv8

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Assessing the Robustness of nnU-Net in the Detection of Prostate Lesions via Bi-Parametric MRI

Assessing the Robustness of nnU-Net in the Detection of Pros...

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2023 IEEE EMBS Special Topic Conference on Data science and engineering in Healthcare, Medicine and Biology, IEEECONF 2023

作者： Zaridis, Dimitrios I. Mylona, Eugenia Tachos, Nikolaos Kalantzopoulos, Charalampos Pezoulas, Vasileios C. Koutsouris, Dimitrios D. Matsopoulos, George K. Marias, Kostas Tsiknakis, Manolis Fotiadis, Dimitrios I. National Technical University of Athens School of Electrical and Computer Engineering Biomedical Engineering Laboratory AthensGR15773 Greece Forth Biomedical Research Institute Ioannina Greece University of Ioannina Ioannina Greece Forth Institute of Computer Science Heraklion Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering Crete Heraklion Greece FORTH-IMBB Ioannina Greece University of Ioannina Unit of Medical Technology and Intelligent Information Systems Materials Science and Engineering Department IoanninaGR 45110 Greece

ISBN: (纸本)9798350383386

Within the scope of prostate cancer diagnostic imaging, distinguishing lesions is challenging due to their subtle appearance and the prostate gland's complexity. This study employed the nnU-Net, a state-of-the-art medical image segmentation model, on a dataset of 301 patients from 2 openly available datasets to identify whether lesion sizes are affecting the model's performance. By applying t-SNE dimensionality reduction algorithm among dice score and respective lesion sizes, we found that nnU-Net behaves differently for lesions smaller than 9mm compared to lesions larger than 15mm. These insights can inform specialized training approaches for future deep learning models in prostate lesion *** Relevance: The main contributions of this study is, to investigate whether lesion sizes affects nnU-Net's deficiency leading to the development of more specialized models in respect to these factors. © 2023 IEEE.

关键词： Urology

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Spatiotemporal Analysis of Health Data using Graph Convolutional Networks in the Cloud 1

Spatiotemporal Analysis of Health Data using Graph Convoluti...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Shukla, Aasheesh Pokhariya, Hemant Singh Michaelson, Jacob Laxminarayanamma, K. Yadav, Dinesh Kumar Kumar, Navneet Gla University Department of Electronics and Communication Mathura India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Institute of Aeronautical Engineering Department of Information Technology Telangana Hyderabad India Lloyd Institute of Engineering and Technology Greater Noida India Lloyd Law College Greater Noida India

ISBN: (纸本)9798350330915

This study combines cloud computing infrastructure with graph convolutional networks (GCNs) to come up with a unique method for analyzing spatiotemporal health data. Based on an interpretive mindset, this study uses secondary data sources, including anonymized electronic health records geographic information, and a deductive method with a descriptive research design. The GCN model, which treats patients as nodes and their interactions as edges in a graph, is designed to capture complicated relationships within the spatiotemporal data. The model exhibits greater ability in spotting complex patterns, which include trends in patient migration and dynamics of illness propagation. The parallel processing capabilities of the cloud computing environment speed up the model training and assessment operations. The GCN outperforms conventional ways in handling complicated dependencies, as shown by comparison with existing techniques. Additionally, the implementation of cloud computing greatly improves computational effectiveness, allowing for the real-time analysis of massive datasets. The results of the study provided healthcare professionals and policymakers with insightful information. Planning for targeted interventions as well as resource allocation are influenced by changes in patient migration and disease transmission. The research emphasizes the significance of further investigation in this multidisciplinary domain by highlighting the potential for revolutionary influence on healthcare decision-making including resource allocation. The integration of cutting-edge analytical methods and cloud computing for better healthcare outcomes is emphasized in this study, which provides the groundwork for future research in spatiotemporal health data analysis. © 2023 IEEE.

关键词： Cloud computing Integration in health data analysis Graph Convolutional Networks Healthcare research Spatiotemporal Analysis

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Human-Spa: An Online Platform Based on Spatial Transcriptome Data for Diseases of Human Systems

Human-Spa: An Online Platform Based on Spatial Transcriptome...

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Su, Yunyun Cui, Feifei Yan, Shiyu Zou, Quan Cao, Chen Zhang, Zilong Hainan University School of Computer Science and Technology Haikou China Hainan University School of Life Sciences Haikou China University of Electronic Science and Technology of China Institute of Fundamental and Frontier Sciences Chengdu China Nanjing Medical University School of Biomedical Engineering and Informatics Nanjing China

ISBN: (纸本)9798350337488

Spatial transcriptomics has become a major method for high-throughput analysis of gene expression at the current level of cells, which can directly study gene expression changes in disease cells, reveal the occurrence and development mechanism of diseases, identify potential therapeutic targets related to diseases, and provide new clues for disease diagnosis and treatment. Although there have been major breakthroughs in the analysis and acquisition of transcriptome data, there are still many challenges in how to effectively manage, share and utilize these valuable data resources in the study of human systemic diseases. To solve this problem, we are committed to building a spatial transcriptome database website related to human systemic diseases, aiming to provide reliable datasets related to multiple diseases, and provide certain data information and analysis results to accelerate the progress of human systemic diseases research. This paper will introduce the construction process of the database website in detail, and discuss its application prospects in disease research, with the aim of promoting further development and innovation in the field of human health. Here, Human-spa mainly includes 12 Human systems, 38 disease types, 55 datasets, and Human-Spa provides a very friendly web interface for visualization and dataset parsing. In conclusion, the construction of Human-Spa will provide a powerful tool and resource platform for Human disease research. Human-Spa is available for free at http://***/ © 2023 IEEE.

关键词： data analysis database website diseases of human system spatial transcriptome

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