Pervasive and Mobile Computing最新文献_第3页

Lightweight secure key establishment to create a secure channel between entities in a crowdsourcing environment 轻量级安全密钥建立，在众包环境中创建实体之间的安全通道

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-09 DOI: 10.1016/j.pmcj.2025.102078

Mahdi Nikooghadam, Hamid Reza Shahriari

{"title":"Lightweight secure key establishment to create a secure channel between entities in a crowdsourcing environment","authors":"Mahdi Nikooghadam, Hamid Reza Shahriari","doi":"10.1016/j.pmcj.2025.102078","DOIUrl":"10.1016/j.pmcj.2025.102078","url":null,"abstract":"<div><div>The concept of crowdsourcing uses shared intelligence to solve complex tasks through group collaboration. Crowdsourcing involves gathering information and opinions from participants who submit their data, or solutions, over the Internet using a specific program. Given that the communication environment for crowdsourcing platforms is the Internet, there is a significant opportunity for attackers to compromise the confidentiality and integrity of information and violate participants’ privacy. Despite the great benefits of crowdsourcing, concerns about security and privacy are growing and require attention. Unfortunately based on our knowledge, the schemes presented to preserve security and privacy in crowdsourcing are susceptible to security and privacy attack and have a high computational and communication overhead. Therefore, they are not appropriate for crowdsourcing environments. This paper presents an ultra-lightweight authentication and key establishment protocol based on hash functions. This protocol meets all security requirements, is invulnerable to known attacks, and imposes a very low network overhead. The security of the proposed scheme has been formally proved, depicting the resistance of the proposed scheme to different types of possible attacks. In addition, the robustness of the proposed scheme against potential attacks has been proven through Scyther’s automatic software validation tool. The performance evaluation ultimately demonstrated that the proposed protocol incurs significantly reduced computational and communication costs compared to previous schemes and is very suitable for the crowdsourcing environment.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102078"},"PeriodicalIF":3.0,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262812","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Unveiling user dynamics in the evolving social debate on climate crisis during the conferences of the parties 在缔约方会议期间，在不断发展的气候危机社会辩论中揭示用户动态

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-05 DOI: 10.1016/j.pmcj.2025.102077

Liliana Martirano , Lucio La Cava , Andrea Tagarelli

{"title":"Unveiling user dynamics in the evolving social debate on climate crisis during the conferences of the parties","authors":"Liliana Martirano , Lucio La Cava , Andrea Tagarelli","doi":"10.1016/j.pmcj.2025.102077","DOIUrl":"10.1016/j.pmcj.2025.102077","url":null,"abstract":"<div><div>Social media have widely been recognized as a valuable proxy for investigating users’ opinions by echoing virtual venues where individuals engage in daily discussions on a wide range of topics. Among them, climate change is gaining momentum due to its large-scale impact, tangible consequences for society, and enduring nature. In this work, we investigate the social debate surrounding climate emergency, aiming to uncover the fundamental patterns that underlie the climate debate, thus providing valuable support for strategic and operational decision-making. To this purpose, we leverage Graph Mining and NLP techniques to analyze a large corpus of tweets spanning seven years pertaining to the Conference of the Parties (COP), the leading global forum for multilateral discussion on climate-related matters, based on our proposed framework, named NATMAC, which consists of three main modules designed to perform network analysis, topic modeling and affective computing tasks. Our contribution in this work is manifold: (i) we provide insights into the key social actors involved in the climate debate and their relationships, (ii) we unveil the main topics discussed during COPs within the social landscape, (iii) we assess the evolution of users’ sentiment and emotions across time, and (iv) we identify users’ communities based on multiple dimensions. Furthermore, our proposed approach exhibits the potential to scale up to other emergency issues, highlighting its versatility and potential for broader use in analyzing and understanding the increasingly debated emergent phenomena.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102077"},"PeriodicalIF":3.0,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144364551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A-BEE-C: Autonomous Bandwidth-Efficient Edge Codecast A-BEE-C：自主带宽高效边缘编解码器

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-04 DOI: 10.1016/j.pmcj.2025.102075

Gyujeong Lim , Joon-Min Gil , Heonchang Yu

{"title":"A-BEE-C: Autonomous Bandwidth-Efficient Edge Codecast","authors":"Gyujeong Lim , Joon-Min Gil , Heonchang Yu","doi":"10.1016/j.pmcj.2025.102075","DOIUrl":"10.1016/j.pmcj.2025.102075","url":null,"abstract":"<div><div>Edge computing is a new paradigm in cloud infrastructure that decentralizes computing and storage, bringing data and services closer to the users. This proximity allows users to access high quality or large sized data with lower latency. However, edge servers typically have fewer resources than cloud servers, necessitating efficient resource management. Emerging research focuses on increasing the cache hit rate of user requests to edge servers, which reduces response latency and improves efficiency. Nonetheless, if available bandwidth is not considered, it becomes challenging to maintain both speed and quality in edge environments. This paper proposes an Autonomous Bandwidth-Efficient Edge Codecast (A-BEE-C) method to enhance the effective bandwidth per device within an edge service area. Codecast, introduced in this paper, is a transmission method that encodes multiple files into a single file before sending it to users. A-BEE-C introduces a dynamic mechanism that switches between unicast and codecast modes based on real-time bandwidth assessment. Our proposed method increases the effective bandwidth per device by encoding multiple user requests into a single coded transmission when the bandwidth of the edge server is limited. Experimental results demonstrate that A-BEE-C reduces average latency per device by up to 9.89% (and up to 18.45% with Zipf pattern data) and increases effective bandwidth per user by up to 10.15% (up to 18.11% with Zipf pattern).</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102075"},"PeriodicalIF":3.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144221106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A customizable benchmarking tool for evaluating personalized thermal comfort provisioning in smart spaces using Digital Twins 一个可定制的基准工具，用于使用Digital Twins评估智能空间的个性化热舒适配置

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-04 DOI: 10.1016/j.pmcj.2025.102076

Jun Ma , Dimitrije Panic , Roberto Yus , Georgios Bouloukakis

{"title":"A customizable benchmarking tool for evaluating personalized thermal comfort provisioning in smart spaces using Digital Twins","authors":"Jun Ma , Dimitrije Panic , Roberto Yus , Georgios Bouloukakis","doi":"10.1016/j.pmcj.2025.102076","DOIUrl":"10.1016/j.pmcj.2025.102076","url":null,"abstract":"<div><div>Providing proper thermal comfort to individual occupants is crucial to improve well-being and work efficiency. However, Heating, Ventilation, and Air Conditioning (HVAC) systems are responsible for a large portion of energy consumption and CO2 emissions in buildings. To combat the current energy crisis and climate change, innovative ways have been proposed to leverage pervasive and mobile computing systems equipped with sensors and smart devices for occupant thermal comfort satisfaction and efficient HVAC management. However, evaluating these thermal comfort provision solutions presents considerable difficulties. Conducting experiments in the real world poses challenges such as privacy concerns and the high costs of installing and maintaining sensor infrastructure. On the other hand, experiments with simulations need to accurately model real-world conditions and ensure the reliability of the simulated data.</div><div>To address these challenges, we present Co-zyBench, an innovative benchmarking tool that leverages Digital Twin (DT) technology to assess personalized thermal comfort provision systems. Our benchmark employs a simulation-based DT for the building and its HVAC system, another DT for simulating the dynamic behavior of its occupants, and a co-simulation middleware to achieve a seamless connection of the DTs. Our benchmark includes mechanisms to generate DTs based on data such as architectural models of buildings, sensor readings, and occupant thermal sensation data. It also includes reference DTs based on standard buildings, HVAC configurations, and various occupant thermal profiles. As a result of the evaluation, the benchmark generates a report based on expected energy consumption, carbon emission, thermal comfort, and occupant equity metrics. We present the evaluation results of state-of-the-art thermal comfort provisioning systems within a DT based on a real building and several reference DTs.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102076"},"PeriodicalIF":3.0,"publicationDate":"2025-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Resilient UAVs location sharing service based on information freshness and opportunistic deliveries 基于信息新鲜度和机会交付的弹性无人机位置共享服务

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-06-01 DOI: 10.1016/j.pmcj.2025.102066

Agnaldo de Souza Batista , Aldri Luiz dos Santos

{"title":"Resilient UAVs location sharing service based on information freshness and opportunistic deliveries","authors":"Agnaldo de Souza Batista , Aldri Luiz dos Santos","doi":"10.1016/j.pmcj.2025.102066","DOIUrl":"10.1016/j.pmcj.2025.102066","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAV) have been recognized as a versatile platform for various services. During the flight, these vehicles must avoid collisions to operate safely. In this way, they demand to keep spatial awareness, i.e., to know others in their coverage area. However, mobility and positioning hamper building UAV network infrastructure to support reliable basic services. Thus, such vehicles call for a location service with up-to-date information resilient to false location injection threats. This work proposes FlySafe, a resilient UAV location-sharing service that employs opportunistic approaches to deliver UAVs’ location. FlySafe takes into account the freshness of UAVs’ location to maintain their spatial awareness. Further, it counts on the age of the UAV’s location information to trigger device discovery. Simulation results showed that FlySafe achieved spatial awareness up to 94.15% of UAV operations, being resilient to false locations injected in the network. Moreover, the accuracy in device discovery achieved 94.53% with a location error of less than 2 m.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102066"},"PeriodicalIF":3.0,"publicationDate":"2025-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144184361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Task offloading of IOT device in fog-enabled architecture using deep reinforcement learning approach 使用深度强化学习方法在雾支持架构中卸载物联网设备的任务

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-31 DOI: 10.1016/j.pmcj.2025.102067

Abhinav Tomar, Megha Sharma, Ashwarya Agarwal, Aditya Nath Jha, Jai Jaiswal

{"title":"Task offloading of IOT device in fog-enabled architecture using deep reinforcement learning approach","authors":"Abhinav Tomar, Megha Sharma, Ashwarya Agarwal, Aditya Nath Jha, Jai Jaiswal","doi":"10.1016/j.pmcj.2025.102067","DOIUrl":"10.1016/j.pmcj.2025.102067","url":null,"abstract":"<div><div>The rapid growth of IoT devices has strained traditional cloud-centric architectures, revealing limitations in latency, bandwidth, and reliability. Fog computing addresses these issues by decentralizing resources closer to data sources, but task offloading and resource allocation remain challenging due to dynamic workloads, heterogeneous resources, and strict QoS requirements. This study models task offloading as a multi-objective optimization problem, considering task priority, energy efficiency, latency, and deadlines. Using a Markov Decision Process (MDP), it applies three Deep Reinforcement Learning (DRL) algorithms — DQN, DDPG, and SAC — in a multi-agent fog computing setup. Unlike prior work focused on single-agent or isolated metrics, this approach captures inter-node dependencies to improve overall resource use. Simulations show SAC achieves a 97.3% task deadline success rate and improves resource efficiency by 10.1%, highlighting its effectiveness in managing dynamic fog environments. These results advance scalable, adaptive offloading strategies for future IoT systems.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102067"},"PeriodicalIF":3.0,"publicationDate":"2025-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144194944","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Hybrid elk herd green anaconda-based multipath routing and deep learning-based intrusion detection In MANET 基于混合麋鹿群绿水蟒的多路径路由和基于深度学习的MANET入侵检测

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-23 DOI: 10.1016/j.pmcj.2025.102079

Dr M. Anugraha , Dr S. Selvin Ebenezer , Dr S. Maheswari

{"title":"Hybrid elk herd green anaconda-based multipath routing and deep learning-based intrusion detection In MANET","authors":"Dr M. Anugraha , Dr S. Selvin Ebenezer , Dr S. Maheswari","doi":"10.1016/j.pmcj.2025.102079","DOIUrl":"10.1016/j.pmcj.2025.102079","url":null,"abstract":"<div><div>A Mobile Ad-Hoc Network (MANET) represents a set of wireless networks that create the network without requiring centralized control. Moreover, the MANET serves as an effectual communication network but is impacted by security issues. MANET intrusion detection constantly monitors network traffic for potential intrusions. Still, it requires network nodes for analyzing, and processing the data, which leads to the highest processing charge. For solving such difficulties, the EIK Herd Anaconda Optimization (EHAO)-based routing, and EHAO-trained Deep Kronecker Network (EHAO-DKN) for intrusion detection is devised in this paper. The MANET simulation is the prime step for attaining the routing. The proposed EHGAO with the fitness factors are considered in the routing. The intrusion presence in the MANET is detected at the Base Station (BS), where the Z-score normalization is applied to normalize the log data. The Wave Hedges metric effectively selects the relevant features, and the EHAO-DKN detects the intrusion. Furthermore, the EHAO-based routing obtained the optimal trust, energy, and delay of 85.30, 2.905 J, and 0.608 mS as well as the accuracy, sensitivity, and specificity of 92.40 %, 91.50 %, and 91.50 % are achieved by the EHAO-DKN-based intrusion detection.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"112 ","pages":"Article 102079"},"PeriodicalIF":3.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144241479","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Differentiating presence in virtual reality using physiological signals 利用生理信号区分虚拟现实中的存在感

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-23 DOI: 10.1016/j.pmcj.2025.102065

Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey

{"title":"Differentiating presence in virtual reality using physiological signals","authors":"Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey","doi":"10.1016/j.pmcj.2025.102065","DOIUrl":"10.1016/j.pmcj.2025.102065","url":null,"abstract":"<div><div>Advancements in wearable technologies have made the use of physiological signals, such as Electrodermal Activity (EDA) and Heart Rate Variability (HRV), more prevalent for detecting changes in the autonomic nervous system within virtual reality (VR). However, the challenge lies in utilizing these signals to objectively detect presence in VR, which typically relies on self-reports that can be inherently biased. This paper addresses this issue and presents a study (<em>N</em>=26) that investigates the effect that different levels of presence has on physiological responses in VR. A neutral VR environment was created that incorporated three levels of presence (high, medium and low) that were invoked by tuning different parameters. Participants wore a wrist-worn wearable device that captured their physiological signals whilst they experienced each of these environments. Results indicated that tonic and phasic components of the EDA signal were significant in differentiating between the levels. Two novel features, constructed using both the phasic and tonic components of EDA, successfully differentiated between presence levels. Analysis of the HRV data illustrated a significant difference between the low and medium levels using the ratio between low frequency to high frequency.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102065"},"PeriodicalIF":3.0,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

LiteFlex-YOLO:A lightweight small target detection network for maritime unmanned aerial vehicles LiteFlex-YOLO：用于海上无人机的轻型小型目标探测网络

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-22 DOI: 10.1016/j.pmcj.2025.102064

Peng Tang, Yong Zhang

{"title":"LiteFlex-YOLO:A lightweight small target detection network for maritime unmanned aerial vehicles","authors":"Peng Tang, Yong Zhang","doi":"10.1016/j.pmcj.2025.102064","DOIUrl":"10.1016/j.pmcj.2025.102064","url":null,"abstract":"<div><div>With frequent maritime activities, the number of overboard accidents at sea has increased, and rescue delays often lead to people being killed. Unmanned Aerial Vehicles (UAVs) have the advantages of fast localization and real-time monitoring in rescue, but the images taken by UAVs have many small targets, and the detection accuracy is insufficient; at the same time, target detection algorithms are difficult to be deployed due to the limitation of computational resources of UAVs. For this reason, this paper proposes a lightweight target detection model based on YOLOv8s improvement, LiteFlex-YOLO, which aims to improve the performance of target detection in UAVs sea rescue. Firstly, the small target sensing ability of the model is enhanced by introducing the P2 small target detection layer, secondly, replacing the C2f module with the lightweight C2fCIB module reduces the computational complexity to make the model more lightweight, furthermore, the feature extraction ability of the backbone is enhanced by using the ODConv (Omni-Dimensional Dynamic Convolution); Lastly, the attention mechanism of SimAM (Simple Attention Module) is introduced to enhance the attention of the key feature information. The final experimental results showed that, LiteFlex-YOLO achieves a [email protected] of 69.5% on the SeaDronesSee dataset, which is 18.2% improvement compared to YOLOv8s, and the model parameters are reduced to 71.2% of YOLOv8s. Moreover, compared with other SOTA algorithms, LiteFlex-YOLO performs excellently in small object detection accuracy, model lightweighting, and robustness.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102064"},"PeriodicalIF":3.0,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144125069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Octopus: Knapsack model-driven federated learning client selection in internet of vehicles Octopus：车联网中背包模型驱动的联合学习客户端选择

IF 3 3区计算机科学

Pervasive and Mobile Computing Pub Date : 2025-05-16 DOI: 10.1016/j.pmcj.2025.102063

Ling Xing , Jingjing Cui , Jianping Gao , Kaikai Deng , Honghai Wu , Huahong Ma

{"title":"Octopus: Knapsack model-driven federated learning client selection in internet of vehicles","authors":"Ling Xing , Jingjing Cui , Jianping Gao , Kaikai Deng , Honghai Wu , Huahong Ma","doi":"10.1016/j.pmcj.2025.102063","DOIUrl":"10.1016/j.pmcj.2025.102063","url":null,"abstract":"<div><div>Federated learning (FL), as a distributed way for processing real-time vehicle data, is widely used to improve driving experience and enhance service quality in Internet of Vehicles (IoV). However, considering the data and devices heterogeneity of vehicle nodes, randomly selecting vehicles that are involved in model training would suffer from data skewness, high resource consumption, and low convergence speed. To this end, we propose <span>Octopus</span>, which consists of two components: i) an <em>importance sampling-based local loss computation</em> method is designed to request resource information for each client and apply the importance sampling technique to assess each client’s contribution to the global model’s convergence, followed by utilizing a knapsack model that treats the local loss of each client as the item value, while treating the total system training time as the knapsack capacity to accelerate the client convergence; ii) a <em>knapsack model-based federated learning client selection</em> method is designed to select the client with optimal local loss and maximum model uploading speed to participate in training. In each training round, these clients download and update the model within a predefined time, followed by enabling the selected clients to continue uploading the updated model parameters for assisting the server to efficiently complete the model aggregation. Experimental results show that <span>Octopus</span> improved the model accuracy by 2.64% <span><math><mo>∼</mo></math></span>32.61% with heterogeneous data, and by 1.97% <span><math><mo>∼</mo></math></span>11.74% with device heterogeneity, compared to eight state-of-the-art baselines.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102063"},"PeriodicalIF":3.0,"publicationDate":"2025-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144071054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0