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SSRN 2024年

作者： Morgan, Ahmed A. Department of Computer and Network Engineering College of Computing Umm Al-Qura University Makkah21955 Saudi Arabia Department of Computer Engineering Faculty of Engineering Cairo University Giza12613 Egypt

Bluetooth Low Energy (BLE) is one of the most widely used technologies to carry out the essential task of indoor localization. Hundreds of studies are presented to use, analyze, and improve the performance of BLE-based localization systems. These studies include a multitude of localization techniques, application domains, and evaluation metrics. This paper presents a state-of-the-art survey of these studies, taking the accuracy metric into main consideration. Using a majority analysis of reported values in these studies, the presented survey has four key contributions. First, it quantifies the level of accuracy reached by previous localization techniques. Throughout the presented survey, it becomes evident that sub meter accuracy is currently achievable using advanced localization techniques, whereas sub decimeter counterpart is still an open area that requires further research efforts. Second, different domains of BLE-based localization applications are classified into three groups of high, moderate, and low accuracy. Localization techniques are similarly summarized and categorized into the same three groups, such that future researchers could easily find the suitable technique for each domain of applications. Third, categories of techniques that are capable of realizing sub meter accuracy are enumerated. These are the ones that primarily use Machine Learning (ML), filtration, fusion, or the Angle of Arrival (AoA) feature of BLE v5.1 and above. Individual techniques within these categories are further investigated and assessed. Fourth, evaluation metrics, which are targeted by these highly accurate categories of techniques, are reviewed. This review reflects that some important metrics, such as security, still require much attention from the BLE localization community. © 2024, The Authors. All rights reserved.

关键词： Machine learning

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Effective Preprocessing and Explainability for Better Lung Cancer Prediction

Effective Preprocessing and Explainability for Better Lung C...

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International CSI computer Conference, CSICC

作者： Parsa Azimi Sadegh Sulaimany Department of Computer Engineering Social & Biological Network Laboraroty (SBNA) University of Kurdistan Sanandaj Iran

ISBN: (数字)9798331523114

ISBN: (纸本)9798331523121

The application of artificial intelligence (AI) in the medical field has expanded significantly in recent years. Among its various subsets, machine learning (ML) demonstrates immense potential for enhancing the diagnosis and treatment of diseases. In this study, we aim to enhance the classification outcomes of recent lung cancer research by effective preprocessing techniques and emphasizing the impact of features derived from the ‘Survey Lung Cancer’ dataset. To improve classifier performance, different tasks such as SMOTE (Synthetic Minority Over-sampling Technique) for handling class imbalance and MinMaxScaler for feature scaling, were applied. These preprocessing steps significantly enhanced model accuracy. Specifically, the SVM classifier achieved a PR AUC of 99%, while the Random Forest (RF) classifier demonstrated the best performance across other evaluation metrics, achieving an accuracy of 97.91 %. Additionally, SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) were employed to identify the most influential features contributing to each model's predictions. These techniques helped identify the most influential features contributing to each model's predictions, enhancing interpretability and understanding of their outputs.

关键词： Measurement Surveys Support vector machines Visualization Accuracy Explainable AI Lung cancer Predictive models Feature extraction Classification tree analysis

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Design and FPGA Implementation of the Henon Chaotic Map for Image Encryption 14

Design and FPGA Implementation of the Henon Chaotic Map for ...

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14th International Conference on Information and Communication Systems, ICICS 2023

作者： Taqieddin, Eyad Mustafa, Dheya Shatnawi, Ahmed Al-Khaleel, Osama Papachristou, Christos Jordan University of Science and Tech. Network Eng. and Security Department Irbid22110 Jordan The Hashemite University Faculty of Engineering Dept. of Computer Engineering Zarqa13133 Jordan Jordan University of Science and Tech. Software Engineering Department Irbid22110 Jordan Jordan University of Science and Tech. Computer Engineering Department Irbid22110 Jordan Case Western Reserve University Ecse Department ClevelandOH44106 United States

ISBN: (纸本)9798350307870

Due to the ever increasing usage of multimedia content sharing and the inherent nature of the insecure links used for transmission, it is quite necessary to provide a fast and secure encryption algorithm. Current symmetric ciphers possess properties that may reveal some content of the plaintext images, especially when a block cipher is used without any chaining. Chaotic encryption has been proposed to allow for hiding inherent properties of the images and to generate almost uniform histograms of the cipher image (i.e.;hiding any visual properties). To that end, we propose the use of hardware to implement chaos-based ciphers for faster execution time. The results of this work show that such an implementation is feasible, and even favorable, when compared to software-based image encryption. © 2023 IEEE.

关键词： Field programmable gate arrays (FPGA)

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Image Recognition Technology Supported Environment Monitoring Smart Fan

Image Recognition Technology Supported Environment Monitorin...

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Educational Innovation (ECEI), IEEE Eurasian Conference on

作者： Sheng-Yuan Yang Hsiu-Chia Lee Department of Computer Information and Network Engineering Lunghwa University of Science and Technology Taoyuan Taiwan

Owing to the development of the Internet of Things (IoT), smart electrical appliances bring convenience to life. However, smart home appliances are too cumbersome as their interface operations are mostly not intuitive with less practicability than expected. Taiwan has an island climate, and the summer weather is scorching. An easy-to-use and practical electric fan is indispensable for cooling off the heat, which is the research object of this study to make users feel comfortable. In this study, image recognition technology was introduced. The user turns on the fan manually or through the mobile APP, and selects the tracking mode through the mode function. The camera with the fan detects the user’s position and then starts to track the user. The device is equipped with a smoke sensor and a camera to monitor the user’s home. The fan proves the feasibility of the conceptual architecture.

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Towards Cache-Assisted Hierarchical Detection for Real-Time Health Data Monitoring in IoHT

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computers, Materials & Continua 2023年第11期77卷 2529-2544页

作者： Muhammad Tahir Mingchu Li Irfan Khan Salman AAl Qahtani Rubia Fatima Javed Ali Khan Muhammad Shahid Anwar School of Software Technology Dalian University of Technology(DUT)Dalian116621China School of Software Technology Key Laboratory for Ubiquitous Network and Service Software of Liaoning ProvinceDalian116650China Department of Computer Engineering King Saud UniversityRiyadh11543Saudi Arabia School of Software Tsinghua UniversityBeijing100084China Department of Software Engineering University of Science and TechnologyBanu28100Pakistan Department of AI and Software Gachon UniversitySeongnamsi13120Korea

Real-time health data monitoring is pivotal for bolstering road services’safety,intelligence,and efficiency within the Internet of Health Things(IoHT)***,delays in data retrieval can markedly hinder the efficacy of big data awareness detection *** advocate for a collaborative caching approach involving edge devices and cloud networks to combat *** strategy is devised to streamline the data retrieval path,subsequently diminishing network *** an adept cache processing scheme poses its own set of challenges,especially given the transient nature of monitoring data and the imperative for swift data transmission,intertwined with resource allocation *** paper unveils a novel mobile healthcare solution that harnesses the power of our collaborative caching approach,facilitating nuanced health monitoring via edge *** system capitalizes on cloud computing for intricate health data analytics,especially in pinpointing health *** the dynamic locational shifts and possible connection disruptions,we have architected a hierarchical detection system,particularly during *** system caches data efficiently and incorporates a detection utility to assess data freshness and potential lag in response ***,we introduce the Cache-Assisted Real-Time Detection(CARD)model,crafted to optimize *** the inherent complexity of the NP-hard CARD model,we have championed a greedy algorithm as a *** reveal that our collaborative caching technique markedly elevates the Cache Hit Ratio(CHR)and data freshness,outshining its contemporaneous benchmark *** empirical results underscore the strength and efficiency of our innovative IoHT-based health monitoring *** encapsulate,this paper tackles the nuances of real-time health data monitoring in the IoHT landscape,presenting a joint edge-cloud caching strategy paired with a hierarchical detection *** methodology yields enh

关键词： Real-time health data monitoring Cache-Assisted Real-Time Detection(CARD) edge-cloud collaborative caching scheme hierarchical detection Internet of Health Things(IoHT)

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A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G networks

A Standard-compliant Assessment of Beyond-eMBB QoS/QoE in 5G...

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2024 IEEE Conference on Standards for Communications and networking, CSCN 2024

作者： Caso, Giuseppe Rajiullah, Mohammad Brunstrom, Anna De Nardis, Luca Alay, Özgü Neri, Marco Karlstad University Department of Mathematics and Computer Science Karlstad Sweden Sapienza University of Rome Department of Information Engineering Electronics and Telecommunications Rome Italy University of Oslo Department of Informatics Oslo Norway Mobile Network Testing Rohde & Schwarz Rome Italy

ISBN: (纸本)9798331507428

5th Generation (5G) mobile systems are being deployed to address the Quality of Service and Experience (QoS/QoE) requirements of several use cases, including enhanced Mobile Broadband (eMBB) and Ultra-Reliable Low Latency Communication (URLLC). While eMBB performance testing inherits well-established methodologies and Key Performance Indicators (KPIs), beyond-eMBB services (i.e., URLLC and eMBBURLLC real-time applications) are often tested by adopting simplistic or in-house methodologies, which do not help towards accurate assessment and comparison. In this paper, we fill this gap by providing a detailed analysis of a methodology, recently standardized by the International Telecommunication Union Telecommunication Standardization Sector (ITU-T), that targets systematic performance evaluations of beyond-eMBB services. The methodology relies on the definition of a QoE KPI, i.e., the interactivity score (i-score), on top of three QoS KPIs measuring service latency, stability, and continuity. To this aim, we perform a multi-service measurement campaign on two 5G networks across two cities in Sweden, during which we run a large number of tests compliant with the ITU-T methodology, and analyze the collected data. Our results empirically validate the methodology, showcasing its ability of capturing heterogeneous service characteristics and requirements, as well as the interdependencies between i-score and QoS KPIs, and the impact of different factors on QoS/QoE performance, including user mobility, connection capability, and server location. © 2024 IEEE.

关键词： 5G mobile communication systems

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Research and Innovation in Next Generation Security and Privacy in Industry 5.0 Iot 6

Research and Innovation in Next Generation Security and Priv...

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6th International Conference on Contemporary Computing and Informatics, IC3I 2023

作者： Navale, Geeta S. Madala, Ravikiran Managuli, Manjunath Jayalakshmi, N. Kadiravan, G. Rawat, Ruchira Sinhgad Institute of Technology and Science Department of Computer Engineering Maharashtra Pune India Ph.D Information Technology Network and Data Center Engineer University of Cumberlands DurhamNC United States KLS Gogte Institute of Technology Department of Electronics and Communication Engineering Karnataka Belagavi India PSNA College of Engineering and Technology Master of Computer Applications Tamilnadu Dindigul India KL Deemed to Be University Department of Computer Science and Information Technology Andhra Pradsh India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttrakhand Dehradun India

ISBN: (纸本)9798350304480

The objective of the newest manufacturing paradigm, known as 'Industry 5.0,'is to combine human intelligence with cutting-edge technology like the Internet of Things (IoT), artificial intelligence (AI), and robots to develop production systems that are incredibly effective and adaptable. But as we rely more on these technologies, we face additional privacy and security issues that might affect how dependable and trustworthy these systems are. In this report, we examine the most recent advancements in security and privacy for Industry 5.0 IoT and pinpoint the newest trends, obstacles, and possibilities. We also cover how these changes may affect manufacturing in the future and provide advice to scholars, practitioners, and policymakers. © 2023 IEEE.

关键词： artificial intelligence cyber-physical systems human-machine collaboration Industry 5.0 Internet of Things IoT manufacturing privacy robotics security

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GRDNet: A Lightweight Railway Foreign Object Intrusion Detection Algorithm 7

GRDNet: A Lightweight Railway Foreign Object Intrusion Detec...

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7th International Conference on Advanced Algorithms and Control engineering, ICAACE 2024

作者： Wang, Zhaonan Wei, Lin Meng, Caixia Shi, Lei Gao, Yufei Wang, Qingxian School Of Cyber Science And Engineering Zhengzhou University Zhengzhou China School Of Computer And Artificial Intelligence Zhengzhou University Zhengzhou China Zhengzhou Police College Department Of Image And Network Investigation Technology Zhengzhou China

ISBN: (纸本)9798350361445

Railway intrusion detection using convolutional neural networks has significant results, but it requires a large amount of data and arithmetic power to train and deploy neural network models, and it is still a challenge to improve detection accuracy under limited computational resources. Based on this problem, this paper proposes a lightweight railway foreign object intrusion detection algorithm: GRDNet, which firstly adopts a lightweight backbone network GhostNetV2, and makes use of the decoupled fully connected attention mechanism to aggregate local and long-distance information, which significantly reduces the number of parameters and computation while maintaining the detection performance. Next, a deep feature extractor, RSPPFCSPC, is proposed to reduce the computational effort by decreasing the number of channels in the feature map while maintaining the diversity and effectiveness of the features. Finally, a knowledge distillation algorithm is used to improve the accuracy of foreign object localization and classification and reduce the leakage rate of the model. Experiments show that the proposed method is superior to the state-of-the-art work of each comparison. © 2024 IEEE.

关键词： Object detection

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A Critical Review on Flexibility Quantification and Evaluation Methods in Medium and Low Voltage networks

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IEEE Access 2025年 13卷 92622-92640页

作者： Georgia Eirini Lazaridou Efstratia Sarafoglou Nikolaos Fesakis Vasileios Boglou Athanasios Karlis Department of Electrical and Computer Engineering Democritus University of Thrace Xanthi Greece Hellenic Electricity Distribution Network Operator (HEDNO) Athens Greece

With the increasing integration of Distributed Energy Resources (DERs), effective methods for flexibility quantification and evaluation become necessary to ensure grid stability and reliability. This review systematically explores two complementary approaches to flexibility quantification in medium voltage (MV) and low voltage (LV) networks: metric-based (MB) and region-based (RB) methodologies. MB methods provide concise evaluations of system-wide flexibility, facilitating benchmarking and operational insights. However, they often fail to capture the spatiotemporal complexities and interdependencies inherent in modern grids. RB methods, on the other hand, leverage Feasible Operating Regions (FOR) to map flexibility spatially and temporally but are hindered by low observability characterizing distributions systems and limited uncertainty modelling. Compared to previous studies that analyze MB and RB approaches separately, this review uniquely synthesizes both methodologies and proposes their combination as a more comprehensive tool for dynamic flexibility management in modern distribution networks. By enhancing FOR with flexibility metrics, the approach bridges gaps in uncertainty and time modelling, addressing key challenges posed by the increased penetration of DERs. This integration offers a practical and scalable solution for system operators to achieve a more comprehensive and dynamic assessment of flexibility, paving the way for enhanced grid stability and operational efficiency.

关键词： Measurement Reviews Power system stability Distributed power generation Uncertainty Power system reliability Renewable energy sources Indexes Reactive power Power measurement

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Fine-Grained Multivariate Time Series Anomaly Detection in IoT

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computers, Materials & Continua 2023年第6期75卷 5027-5047页

作者： Shiming He Meng Guo Bo Yang Osama Alfarraj Amr Tolba Pradip Kumar Sharma Xi’ai Yan School of Computer&Communication Engineering Changsha University of Science&TechnologyChangsha410114China Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Department of Computing Science University of AberdeenAberdeenAB243FXUK Hunan Provincial Key Laboratory of Network Investigational Technology Hunan Police AcademyChangsha410138China

Sensors produce a large amount of multivariate time series data to record the states of Internet of Things(IoT)*** time series timestamp anomaly detection(TSAD)can identify timestamps of attacks and ***,it is necessary to determine which sensor or indicator is abnormal to facilitate a more detailed diagnosis,a process referred to as fine-grained anomaly detection(FGAD).Although further FGAD can be extended based on TSAD methods,existing works do not provide a quantitative evaluation,and the performance is ***,to tackle the FGAD problem,this paper first verifies that the TSAD methods achieve low performance when applied to the FGAD task directly because of the excessive fusion of features and the ignoring of the relationship’s dynamic changes between ***,this paper proposes a mul-tivariate time series fine-grained anomaly detection(MFGAD)*** avoid excessive fusion of features,MFGAD constructs two sub-models to independently identify the abnormal timestamp and abnormal indicator instead of a single model and then combines the two kinds of abnormal results to detect the fine-grained *** on this framework,an algorithm based on Graph Attention Neural network(GAT)and Attention Convolutional Long-Short Term Memory(A-ConvLSTM)is proposed,in which GAT learns temporal features of multiple indicators to detect abnormal timestamps and A-ConvLSTM captures the dynamic relationship between indicators to identify abnormal *** simulations on a real-world dataset demonstrate that the proposed algorithm can achieve a higher F1 score and hit rate than the extension of existing TSAD methods with the benefit of two independent sub-models for timestamp and indicator detection.

关键词： Multivariate time series graph attention neural network fine-grained anomaly detection

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