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A Survey of Edge Caching:Key Issues and Challenges

Tsinghua Science and technology 2024年第3期29卷 818-842页

作者： Hanwen Li Mingtao Sun Fan Xia Xiaolong Xu Muhammad Bilal School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China Reading Academy Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si 17035Republic of Korea

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

关键词： edge caching edge computing caching location caching object caching strategy 5G networkarchitecture Internet of Things(IoT)

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Comparison of Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) Models on the Poverty Levels in Central Java in 2023

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IAENG International Journal of Applied Mathematics 2024年第12期54卷 2746-2757页

作者： Alya, Najma Attaqiya Almaulidiyah, Qothrotunnidha Farouk, Bailey Reshad Rantini, Dwi Ramadan, Arip Othman, Fazidah Engineering Department Data Science Technology Study Program Universitas Airlangga Indonesia Engineering Department Data Science Technology Study Program Faculty of Advanced Technology and Multi-discipline Universitas Airlangga Indonesia Information System Study Program Department of Industrial and System Engineering Telkom University Surabaya Campus Indonesia Department of Computer System and Technology Faculty of Computer Science and Information Technology University of Malaya Malaysia

Poverty is considered a serious global issue that must be immediately eradicated by Sustainable Development Goals (SDGs) 1, namely ending poverty anywhere and in any form. As a developing country, poverty is a complex issue faced by Indonesia. Tackling poverty in Indonesia has become a key focus of the government’s strategic priorities. This issue of poverty is experienced by all provinces in Indonesia, including Central Java. The poverty rate in Central Java stands at 10.77% or 3.79 million people. The causes of poverty are explained by several factors. Using these various factors, a mapping model can be conducted to determine the number of poor populations in Central Java. This research employs Geographically Weighted Regression (GWR) and Mixed Geographically Weighted Regression (MGWR) approaches to compare the effectiveness of these models in analyzing the number of poor populations in Central Java in 2023. The kernel function weighting used in this study is Fixed Gaussian. The results showed that the MGWR model outperformed both the OLS regression and GWR models, achieving an Akaike information Criterion (AIC) of 62.766, an R2 of 82.3%, and a Mean Squared Error (MSE) of 0.177. Consequently, it can be inferred that the MGWR model was more suitable for explaining the poverty levels in Central Java. © (2024), (International Association of Engineers). All Rights Reserved.

关键词： Logistic regression

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Oppositional Red Fox Optimization Based Task Scheduling Scheme for Cloud Environment

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computer Systems Science & engineering 2023年第4期45卷 483-495页

作者： B.Chellapraba D.Manohari K.Periyakaruppan M.S.Kavitha Department of Information Technology Karpagam Institute of TechnologyCoimbatore641032TamilnaduIndia Department of Computer Science and Engineering St.Joseph’s Institute of TechnologyChennai600119India Department of Computer Science&Engineering SNS College of EngineeringCoimbatore641107India Department of Computer Science&Engineering SNS College of TechnologyCoimbatore641035India

Owing to massive technological developments in Internet of Things(IoT)and cloud environment,cloud computing(CC)offers a highly flexible heterogeneous resource pool over the network,and clients could exploit various resources on *** IoT-enabled models are restricted to resources and require crisp response,minimum latency,and maximum bandwidth,which are outside the *** was handled as a resource-rich solution to aforementioned *** high delay reduces the performance of the IoT enabled cloud platform,efficient utilization of task scheduling(TS)reduces the energy usage of the cloud infrastructure and increases the income of service provider via minimizing processing time of user ***,this article concentration on the design of an oppositional red fox optimization based task scheduling scheme(ORFOTSS)for IoT enabled cloud *** presented ORFO-TSS model resolves the problem of allocating resources from the IoT based cloud *** achieves the makespan by performing optimum TS procedures with various aspects of incoming *** designing of ORFO-TSS method includes the idea of oppositional based learning(OBL)as to traditional RFO approach in enhancing their efficiency.A wide-ranging experimental analysis was applied on the CloudSim *** experimental outcome highlighted the efficacy of the ORFO-TSS technique over existing approaches.

关键词： Metaheuristics task scheduling cloud computing internet of things makespan red fox optimizer

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DyPSS: Dynamic Priority Signalling System for Emergency Vehicles using Fog and YOLOv8 17

DyPSS: Dynamic Priority Signalling System for Emergency Vehi...

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17th International Conference on COMmunication Systems and NETworkS, COMSNETS 2025

作者： Kakde, Anuj Nath, Shubha Brata Ghosh, Soumya K. Indian Institute of Technology Department of Computer Science and Engineering Kharagpur India Indian Institute of Information Technology Department of Computer Science and Engineering Guwahati India

ISBN: (纸本)9798331531195

Efficient navigation of emergency response vehicles (ERVs) through urban congestion is crucial to life-saving efforts, yet traditional traffic systems often slow down their swift passage. In this work, we introduce Dynamic Priority Signalling System (DyPSS), an intelligent, multi-layered traffic management solution designed to minimize ERV delays by creating "green corridors"at traffic signals. Using technologies like fog computing, cloud computing, global positioning system and deep learning, DyPSS adjusts traffic lights instantly to clear congestion as an ERV approaches. Simulation testing was conducted using SUMO and it demonstrated DyPSS's potential to significantly reduce ERV travel time in dense urban settings with minimal disruption to general traffic flow. This system could drastically improve emergency response by reducing delays and helping responders reach their destinations faster, potentially saving more lives. © 2025 IEEE.

关键词： Emergency vehicles

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Salp swarm algorithm using lens opposition-based learning and local search for constrained optimization problems

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Iran Journal of computer Science 2025年第2期8卷 659-686页

作者： Majumdar, Parijata Mitra, Sanjoy Department of Computer Science and Engineering Indian Institute of Information Technology Agartala Jirania 799046 India Department of Computer Science and Engineering Tripura Institute of Technology Agartala Narsingarh 799009 India

The Salp swarm algorithm (SSA) simulates how salps forage and travel in the ocean. SSA suffers from low initial population diversity, improper balancing of exploration and exploitation, and slow convergence speed. Thus, we have proposed a novel variant of SSA (LOLSSA) by integrating lens opposition learning with the fundamental evolutionary techniques of SSA to increase the initial population variety. During exploration, the information about the local best position for each individual is effectively shared with the follower, using the salp position update process to avoid getting stuck at the local optimum. At the end of each iteration, the local search algorithm is utilized to increase exploitation. Additionally, the leader’s salp position incorporates inertia weight to speed up the convergence speed. Swarm intelligence algorithms have proven their efficacy in feature selection, since they search for an optimal set of features in a large search area that will have the best impact on the accuracy of the training system. LOLSSA is thus employed for feature selection to select informative features in 11 high-dimensional datasets, where it outperforms well-known metaheuristics in average fitness, accuracy, and average number of feature reductions. In the software-as-a-service cloud computing model, a task scheduling problem is solved to satisfy the QoS requirement of both the service provider and the user. Experiments show that LOLSSA optimizes this problem in terms of different performance metrics. LOLSSA also outperforms other metaheuristics regarding 23 well-known benchmark functions, IEEE CEC-C06 2019, and CEC-06 2020 benchmark functions, and in solving engineering design problems. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Convergence speed Feature selection Global optimization Lens opposition learning Salp swarm algorithm Task scheduling problem

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Neuroadaptive Containment Control for Nonlinear Multiagent Systems With Input Saturation: An Event-Triggered Communication Approach

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3163-3173页

作者： Wang, Xin Zhang, Sen Li, Huaqing Zhang, Wei Li, Hongyi Huang, Tingwen Southwest University College of Electronic and Information Engineering Chongqing400715 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this article, a neuroadaptive event-triggered containment control strategy combined with the dynamic surface control (DSC) approach is proposed for nonlinear multiagent systems (MASs) with input saturation. Based on the event-triggered communication mechanisms, the updates of neural network weight and controllers are implemented solely under triggering conditions of violation, which markedly reduces unnecessary communication resources and minimizes inefficient control costs compared with the traditional control method. Radial basis function neural networks (RBF NNs) are employed to handle the nonlinear uncertainties of MASs. Simultaneously, an adaptive compensatory mechanism is incorporated within the backstepping process to address the nonlinear effect posed by input saturation. Additionally, we demonstrate the system stability by extending the Lyapunov theorem to jump and continuous scenarios while excluding Zeno behavior, realizing that all followers can enter the convex hull constructed by leaders. Finally, the effectiveness of the proposed methodology is verified through application simulations. © 2013 IEEE.

关键词： Radial basis function networks

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Deep Subspace K-Means Clustering for Syringe Defect Detection Without Defective Samples

Deep Subspace K-Means Clustering for Syringe Defect Detectio...

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2023 International Symposium on Design Studies and Intelligence engineering, DSIE 2023

作者： Yan, Fei Wang, Xiaodong Hong, Longfu Xie, Zhiyao College of Computer and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (纸本)9781643684888

Modern clustering techniques are becoming more popular, but traditional clustering methods often heavily rely on artificial feature extraction, costing too much human labor resources. This task is especially important when it comes to finding surface defects on medical syringes. To solve this problem, our study comes up with a new way to detect syringe defects without defective samples using deep fuzzy subspace clustering in a smooth way. In particular, our method combines conventional fuzzy c-means and deep autoencoder into a joint framework. During the clustering process, the autoencoder part automatically extract features from dataset itself and generates the representative feature embeddings. After the feature extraction, the fuzzy clustering is performed to find the optimal data structure. This "feature learning, fuzzy clustering" process is iteratively performed until the objective function converges. This new method improves the accuracy and reliability of clustering by a lot, which makes it a great choice for finding scale defects on medical syringes. The efficacy of our method is evaluated on four publicly accessible datasets and one medical syringe dataset. The results demonstrate its potential to revolutionize the field of surface defect detection in this critical domain. © 2024 The Authors.

关键词： Extraction

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Multi Pedestrian Tracking Algorithm Based on Improved YOLOv5 and DeepSORT 9

Multi Pedestrian Tracking Algorithm Based on Improved YOLOv5...

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9th International Conference on Intelligent Informatics and BioMedical Sciences, ICIIBMS 2024

作者： Wan, Hong Zhang, Yu Yang, Delong Shanghai Institute of Technology School of Computer and Information Engineering Shanghai China

ISBN: (纸本)9798350363043

A pedestrian detection and tracking algorithm based on improved YOLOv5s and DeepSORT is proposed to address the issues of pedestrian identity jumps and tracking failures caused by mutual occlusion, similar appearance features, target deformation, and motion blur on traffic roads. In the detection stage, the fusion of attention module ECA and YOLOv5 neck network enhances the extraction of pedestrian features. Replacing the CIoU bounding box loss function with the EIoU bounding box loss function accelerates bounding box regression while improving accurate localization. During the tracking phase, DeepSORT is improved by utilizing an extended Kalman filter to predict pedestrian positions in nonlinear environments. The Hungarian algorithm is used to match and detect trajectories, optimizing the problem of frequent identity switching in complex environments. Finally, the improved YOLOv5 and DeepSORT algorithms were connected to detect and track the MOT-16 dataset. The experimental results show that the improved YOLOv5 algorithm has an average accuracy increase of 3.5% compared to the original algorithm. When combined with DeepSORT tracking, the average tracking accuracy is 60.5%, which is 3.1% higher than the original *** number of pedestrian identity switches decreased by 46 times, which is 5.2% lower than the original algorithm. © 2024 IEEE.

关键词： Extended Kalman filters

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A Bilateral Assessment of Human Activities Using PSO-Based Feature Optimization and Non-linear Multi-task Least Squares Twin Support Vector Machine

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SN computer Science 2024年第3期5卷 315页

作者： Thakur, Ujwala Vidyarthi, Ankit Prajapati, Amarjeet Department of Computer Science Engineering and Information Technology Jaypee Institute of Information Technology Noida Sector-62 Uttar Pradesh Noida 201309 India

Human activity recognition (HAR) is an essential part of many applications, including smart surroundings, sports analysis, and healthcare. Accurately categorizing intricate actions from sensor data is still difficult, though. This method infers an individual’s actions using a variety of sensor data, including magnetometers, gyroscopes, and accelerometers. This work suggests a unique method for classifying human activities by combining a non-linear multi-task least squares twin support vector machine (NMtLSSVM) with particle swarm optimization (PSO) for feature optimization. Utilizing the advantages of both approaches, the suggested strategy achieves excellent resilience and accuracy in activity identification. The suggested method reduces dimensionality and computing costs while maintaining pertinent information using PSO to extract the most important features from the raw sensor data. On the other hand, NMtLSSVM is used to construct a multi-task learning framework that learns several related tasks at the same time and shares information among them. As a result, generalization and resilience can be enhanced beyond single-task models. Two datasets that are available to the public, WISDM, and UCI-HAR, were used to assess the suggested method. The testing findings show that the suggested approach works noticeably better than the most advanced techniques already in use. Overall activities, the outcome achieves an average classification accuracy of 97.8% (UCI-HAR dataset) and 98.5% (WISDM dataset). Furthermore, the PSO-based feature optimization decreased the feature count by 60% without sacrificing efficiency. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Human action recognition Intelligent computing Machine learning classification Performance comparison Sensory data analysis

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Employing Machine Learning Algorithms to Detect Phishing URL Websites 5

Employing Machine Learning Algorithms to Detect Phishing URL...

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Charishma, T.N.S. Koushik, A Sai Reddy, G Sai Ashmit Himabindu, M. Institute of Aeronautical Engineering Computer Science and Information Technology Hyderabad India

ISBN: (纸本)9798331518097

Phishing attacks have risen like wildfire in the era of new digital technology in the wake of exploiting holes in the electronic communication process while cheating a user to seek private information. This work provides machine learning-based resistance against phishing attacks by differentiating phishing websites using recognition of patterns and analysis on data feature. By training several machine learning models, including Random Forest, XGBoost, and Support Vector Machine (SVM), on a dataset that included characteristics frequently found on phishing websites, we developed a system that could distinguish accurately between genuine websites and phishing attempts. This project brought in a nice innovation through the incorporation of these predictive models into a Flask-developed web application, thus allowing for an interactive, real-time phishing detection. The project shall ensure protection of financial security and personal information by giving users an easy interface to input website links so they can get instant feedback about whether the link is likely to be legitimate. This hybrid technique improves accessibility to non-technical users, brings better detection accuracy, and makes phishing detection more proactive, approachable, and successful in reality. © 2024 IEEE.

关键词： Phishing

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