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Proactive Caching at the Wireless Edge:A Novel Predictive User Popularity-Aware Approach

computer Modeling in engineering & Sciences 2024年第8期140卷 1997-2017页

作者： Yunye Wan Peng Chen Yunni Xia Yong Ma Dongge Zhu Xu Wang Hui Liu Weiling Li Xianhua Niu Lei Xu Yumin Dong College of Computer Science Chongqing UniversityChongqing400044China School of Computer and Software Engineering Xihua UniversityChengdu610039China School of Computer and Information Engineering Jiangxi Normal UniversityNanchang330022China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd.Yinchuan750002China College of Mechanical and Vehicle Engineering Chongqing UniversityChongqing400030China School of Computer Science and Technology Beijing Institute of TechnologyBeijing100083China School of Computer Science and Technology Dongguan University of TechnologyDongguan523808China School of Emergency Management Xihua UniversityChengdu610039China College of Computer and Information Science Chongqing Normal UniversityChongqing401331China

Mobile Edge Computing(MEC)is a promising technology that provides on-demand computing and efficient storage services as close to end users as *** an MEC environment,servers are deployed closer to mobile terminals to exploit storage infrastructure,improve content delivery efficiency,and enhance user ***,due to the limited capacity of edge servers,it remains a significant challenge to meet the changing,time-varying,and customized needs for highly diversified content of ***,techniques for caching content at the edge are becoming popular for addressing the above *** is capable of filling the communication gap between the users and content providers while relieving pressure on remote cloud ***,existing static caching strategies are still inefficient in handling the dynamics of the time-varying popularity of content and meeting users’demands for highly diversified entity *** address this challenge,we introduce a novel method for content caching over MEC,i.e.,*** synthesizes a content popularity prediction model,which takes users’stay time and their request traces as inputs,and a deep reinforcement learning model for yielding dynamic caching *** results demonstrate that PRIME,when tested upon the MovieLens 1M dataset for user request patterns and the Shanghai Telecom dataset for user mobility,outperforms its peers in terms of cache hit rates,transmission latency,and system cost.

关键词： Mobile edge computing content caching system average cost deep reinforcement learning collaborative mechanism

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Improved Cooperation by Balancing Exploration and Exploitation in Intertemporal Social Dilemma Tasks

Improved Cooperation by Balancing Exploration and Exploita...

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Chinese Intelligent Automation Conference, CIAC 2023

作者： Zhenbo, Cheng Xuesong, Xu Xingguang, Liu Leilei, Zhang Qihou, Chen Yuxin, Chen Xia, Zhang Gang, Xiao College of Computer Science and Technology and College of Software ZheJiang University of Technology Hang zhou China College of Mechanical Engineering ZheJiang University of Technology ZheJiang HangZhou China

ISBN: (纸本)9789819961863

When an individual’s behavior has rational characteristics, this may lead to irrational collective actions for the group. A wide range of organisms from animals to humans often evolve the social attribute of cooperation to meet this challenge. Therefore, cooperation among individuals is of great significance for allowing social organisms to adapt to changes in the natural environment. Based on multi-agent reinforcement learning, we propose a learning strategy for achieving coordination by incorporating a simple learning rate model that can balance exploration and exploitation. We demonstrate that agents that use the strategy improve a relatively collective return in a decision task called the intertemporal social dilemma, where the conflict between the individual and the group is particularly sharp. We also explore the effects of the diversity of learning rates on the population of reinforcement learning agents and show that agents trained in heterogeneous populations develop particularly coordinated policies relative to those trained in homogeneous populations. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： Reinforcement learning

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Enhancing Human Action Recognition with Adaptive Hybrid Deep Attentive Networks and Archerfish Optimization

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computers, Materials & Continua 2024年第9期80卷 4791-4812页

作者： Ahmad Yahiya Ahmad Bani Ahmad Jafar Alzubi Sophers James Vincent Omollo Nyangaresi Chanthirasekaran Kutralakani Anguraju Krishnan Department of Accounting and Finance Faculty of BusinessMiddle East UniversityAmman11831Jordan Faculty of Engineering Al-Balqa Applied UniversitySalt19117Jordan Department of Mathematics Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India Department of Computer Science and Software Engineering Jaramogi Oginga Odinga University of Science and TechnologyBondo210-40601Kenya Department of Electronics and Communication Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesChennai602105India Department of Electronics and Communication Engineering Saveetha Engineering College(Autonomous)Chennai602105India Department of Computer Science and Engineering Kongunadu College of Engineering and Technology(Autonomous)TholurpattiTrichy621215India

In recent years,wearable devices-based Human Activity Recognition(HAR)models have received significant *** developed HAR models use hand-crafted features to recognize human activities,leading to the extraction of basic *** images captured by wearable sensors contain advanced features,allowing them to be analyzed by deep learning algorithms to enhance the detection and recognition of human *** lighting and limited sensor capabilities can impact data quality,making the recognition of human actions a challenging *** unimodal-based HAR approaches are not suitable in a real-time ***,an updated HAR model is developed using multiple types of data and an advanced deep-learning ***,the required signals and sensor data are accumulated from the standard *** these signals,the wave features are *** the extracted wave features and sensor data are given as the input to recognize the human *** Adaptive Hybrid Deep Attentive Network(AHDAN)is developed by incorporating a“1D Convolutional Neural Network(1DCNN)”with a“Gated Recurrent Unit(GRU)”for the human activity recognition ***,the Enhanced Archerfish Hunting Optimizer(EAHO)is suggested to fine-tune the network parameters for enhancing the recognition *** experimental evaluation is performed on various deep learning networks and heuristic algorithms to confirm the effectiveness of the proposed HAR *** EAHO-based HAR model outperforms traditional deep learning networks with an accuracy of 95.36,95.25 for recall,95.48 for specificity,and 95.47 for precision,*** result proved that the developed model is effective in recognizing human action by taking less ***,it reduces the computation complexity and overfitting issue through using an optimization approach.

关键词： Human action recognition multi-modal sensor data and signals adaptive hybrid deep attentive network enhanced archerfish hunting optimizer 1D convolutional neural network gated recurrent units

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Common Problems in software Requirement engineering Process: An Overview of Pakistani software Industry 4

Common Problems in Software Requirement Engineering Process:...

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4th International Conference on Advancements in Computational Sciences, ICACS 2023

作者： Khalid, Sadia Rasheed, Uzair Muneer, Mishal Butt, Wasi Haider Mehmood, Rabia Qamar, Usman College of Electrical and Mechanical Engineering NUST Department of Computer and Software Engineering Islamabad Pakistan University of Lahore Department of Computer Science and IT Lahore Pakistan Lahore College for Women University Department of Computer Science Lahore Pakistan

ISBN: (纸本)9781665463775

Requirement engineering is a major phase of software development process. A project's success mainly depends on an efficient and effective requirement engineering process. Practices have been defined to ensure successful requirement engineering of software projects. Yet the professionals face numerous issues during this phase. This paper explores the software requirement engineering practices from in the software industry of Pakistan. It highlights the common problems faced by the software professionals, as well as commonly deployed solutions and practices. © 2023 IEEE.

关键词： Requirements engineering

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Adaptive event-triggered state estimation for large-scale systems subject to deception attacks

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Science China(Information Sciences) 2022年第2期65卷 139-154页

作者： Hanchen XIAO Derui DING Hongli DONG Guoliang WEI College of Science University of Shanghai for Science and Technology Department of Control Science and Engineering University of Shanghai for Science and Technology School of Software and Electrical Engineering Swinburne University of Technology Institute of Complex Systems and Advanced Control Northeast Petroleum University

This paper addresses state estimation issues of large-scale systems with measurements subject to deception attacks, where the communication topology among sub-estimators is the same as the physical coupling structure of the subsystems. In consideration of the limited channel bandwidth, a novel adaptive event-triggered scheme is proposed for governing the data transmission among sub-estimators. With the help of Lyapunov analysis approaches, sufficient conditions are derived to ensure the input-to-state stability of the dynamics of estimation errors. Meanwhile, the bound of the estimation errors is obtained in the mean-square sense. The desired estimator parameters are presented in an analytical form dependent on the solution of a set of matrix inequalities. The developed scheme is related to the local information of the subsystems and thus satisfies the requirement of scalability. Finally, a simulation example of power systems is given to reveal the usefulness and effectiveness of the developed design scheme.

关键词： large-scale discrete-time systems adaptive event-triggering communication Adaptive event-triggering communication deception attacks input-to-state stability power systems

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MTEE: Multiscale Temporal Entropy Evaluation Paradigm for Heterogeneous Complex Datasets 20th

MTEE: Multiscale Temporal Entropy Evaluation Paradigm for H...

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20th IFIP WG 10.3 International Conference on Network and Parallel Computing, NPC 2024

作者： An, Ledong Wang, Chenyang Huang, Shaoyuan Guo, Yang Zhang, Cheng Qiu, Chao Wang, Xiaofei College of Intelligent and Computing Tianjin University Tianjin China College of Computer Science and Software Engineering Shenzhen University Guangdong Shenzhen518060 China Tianjin University of Finance and Economics Tianjin China Software Technology Company Limited Suzhou215163 China

ISBN: (纸本)9789819628292

The rapid advancements in time-series forecasting have led to the emergence of datasets with increasingly diverse characteristics. Researchers typically focus on designing robust algorithms to handle these datasets. However, model performance can vary across different datasets. Existing studies commonly use Mean Absolute Error (MAE) and Mean Squared Error (MSE) to evaluate model performance. These metrics often need to fully account for the impact of dataset quality on forecast accuracy and reliability, leading to insufficient explanations of model forecasts and even dataset dependence. Furthermore, the lack of dataset evaluation makes it difficult to determine whether forecast results are influenced by data characteristics or model architecture, significantly hindering model improvement. To address this problem, this study proposes a new dataset evaluation paradigm-Multiscale Temporal Entropy Evaluation Paradigm. This paradigm aims to tackle the problem of data feature ambiguity affecting model forecast interpretability. By evaluating nine time-series datasets from domains such as weather, economics, transportation, and networks and comparing these evaluations with the forecast results from six different models, this study demonstrates the effectiveness of this method in explaining the relationship between dataset features and model performance. © IFIP International Federation for Information Processing 2025.

关键词： Spatio-temporal data

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Collaborative Multi-Agent Proximal Policy Based Resource Allocation in Clustered IoV Networks 10

Collaborative Multi-Agent Proximal Policy Based Resource All...

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10th IEEE Smart World Congress, SWC 2024

作者： Raza, Arif Xiang, Junfan Khan, Salabat Shrivastava, Shivanshu Ashraf, M. Wasim Abbas Wang, Lu Shenzhen University College of Computer Science and Software Engineering Shenzhen China Shenzhen University College of Electronics and Information Engineering Shenzhen China Qilu Institute of Technology College of Computer and Information Engineering Jinan China Rajiv Gandhi Institute of Petroleum Technology Department of Electrical and Electronics Engineering Amethi India Hanjiang Normal University School of Mathematics and Computer Science Shiyan China

ISBN: (纸本)9798331520861

The Internet of Vehicles (IoV) necessitates efficient resource management to meet the growing demands for high data rates, low latency, and real-time communication in Intelligent Transportation Systems (ITS). This paper presents a novel multiagent reinforcement learning framework based on the Proximal Policy Optimization (PPO) algorithm for optimizing resource allocation in clusters of cells within IoV networks. The framework dynamically allocates transmission power and bandwidth across base stations, each acting as a collaborative agent. Extensive simulations demonstrate that the multi-agent PPO approach outperforms other reinforcement learning algorithms, including Soft Actor-Critic (SAC) and Advantage Actor-Critic (A2C), regarding throughput, power efficiency, dynamic bandwidth allocation, and overall network performance. © 2024 IEEE.

关键词： Bandwidth

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RGB-T object tracking with adaptive decision fusion 2

RGB-T object tracking with adaptive decision fusion

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2nd International Conference on Algorithms, Microchips, and Network Applications, AMNA 2023

作者： Bai, Yida Yang, Ming Department of Software Engineering College of Computer & Information Science Southwest University Chongqing400715 China Department of Computer Science College of Computer & Information Science Southwest University Chongqing400715 China

ISBN: (纸本)9781510664791

Visual object tracking is a traditional task in computer vision, which has developed with several decades. With the development of machine learning, correlation filter (CF) has been proposed with satisfying performance and very high framerate. Though the CF framework has numerous strengths in this task, the tracker is fragile to miss the target in several scenes, including extreme illumination, target occlusion and deformation. Recently, thermal modality, which detects the target’s temperature, is robust to the night scenes and can provide a precise target contour. In this paper, we propose a CF based tracker with decision fusion strategy for visible-thermal (RGB-T) tracking. First, we introduce multi-modal KCF trackers as our baseline. Then, we design a decision fusion method considering the Peak-to-Side Rate (PSR) of the score maps, thereby achieving an adaptive fusing those modalities and avoiding model’s heterogeneity. In the experiments, our tracker has validated on the public dataset, namely GTOT. Compared with two uni-modality trackers, the proposed tracker with real-time speed has shown superior results on both target localization and scale estimation. © 2023 SPIE.

关键词： Target tracking

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Design and Implementation of Object Detection Model for UAV Aerial images Based on TS-YOLOv8 11

Design and Implementation of Object Detection Model for UAV ...

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11th Joint International Information Technology and Artificial Intelligence Conference, ITAIC 2023

作者： Wang, Shi Li, Jiao Chen, Jiahui Liu, Xinshu Xie, Chenlu Liu, Xiangju College of Computer and Communication Engineering Qilu Institute of Technology Jinan China College of Artificial Intelligence and Software Liaoning Petrochemical University Fushun China

ISBN: (纸本)9798350333664

The main task of UAV aerial object detection is to accurately detect and locate objects using rectangular boxes in aerial images or videos. Due to factors such as shooting height and angle, small objects and overlapping objects account for a large proportion of aerial photography objects. Current UAV aerial photography object detection models may encounter missed detection issues when detecting these two types of objects. In response to the above issues, this paper proposes a TS-YOLOv8 UAV aerial photography object detection model. This model adds a tiny object detection layer on the basis of YOLOv8n to solve the problem of missed detection of small objects. Secondly, the Soft-NMS algorithm is used to optimize the candidate boxes of YOLOv8n to solve the problem of missed detection of mutually occluding objects. The experimental results on the VisDrone2019 dataset show that the proposed TS-YOLO unmanned aerial vehicle aerial photography object detection model can effectively solve the problem of missed detection of small and mutually occluding objects. © 2023 IEEE.

关键词： Object Detection Soft-NMS UAV Aerial Images YOLOv8

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Reinforcement learning-enabled swarm intelligence method for computation task offloading in Internet-of-Things blockchain

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Digital Communications and Networks 2025年第3期11卷 912-924页

作者： Chen, Zhuo Yi, Jiahuan Zhou, Yang Luo, Wei College of Computer Science and Engineering Chongqing University of Technology Chongqing400054 China Department of Computer Science and Software Engineering Auburn University Auburn United States Yunnan Aerospace Engineering Geophysical Detecting Co. Ltd. Kunming650217 China

Blockchain technology, based on decentralized data storage and distributed consensus design, has become a promising solution to address data security risks and provide privacy protection in the Internet-of-Things (IoT) due to its tamper-proof and non-repudiation features. Although blockchain typically does not require the endorsement of third-party trust organizations, it mostly needs to perform necessary mathematical calculations to prevent malicious attacks, which results in stricter requirements for computation resources on the participating devices. By offloading the computation tasks required to support blockchain consensus to edge service nodes or the cloud, while providing data privacy protection for IoT applications, it can effectively address the limitations of computation and energy resources in IoT devices. However, how to make reasonable offloading decisions for IoT devices remains an open issue. Due to the excellent self-learning ability of Reinforcement Learning (RL), this paper proposes a RL enabled Swarm Intelligence Optimization Algorithm (RLSIOA) that aims to improve the quality of initial solutions and achieve efficient optimization of computation task offloading decisions. The algorithm considers various factors that may affect the revenue obtained by IoT devices executing consensus algorithms (e.g., Proof-of-Work), it optimizes the proportion of sub-tasks to be offloaded and the scale of computing resources to be rented from the edge and cloud to maximize the revenue of devices. Experimental results show that RLSIOA can obtain higher-quality offloading decision-making schemes at lower latency costs compared to representative benchmark algorithms. © 2024 Chongqing University of Posts and Telecommunications

关键词： Reinforcement learning

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