Pervasive and Mobile Computing最新文献_第6页

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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-06-05 DOI: 10.1016/j.pmcj.2025.102077

Liliana Martirano , Lucio La Cava , Andrea Tagarelli

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.

社交媒体被广泛认为是调查用户意见的一个有价值的代理，它通过模仿虚拟场所，让个人每天就各种话题进行讨论。其中，气候变化因其大规模影响、对社会的切实后果和持久性而势头日益强劲。在这项工作中，我们调查了围绕气候紧急情况的社会辩论，旨在揭示气候辩论的基本模式，从而为战略和业务决策提供有价值的支持。为此，我们利用图挖掘和自然语言处理技术，基于我们提出的名为NATMAC的框架，分析了与缔约方会议（COP）有关的长达七年的大量推文语料库，COP是气候相关问题多边讨论的主要全球论坛，该框架由三个主要模块组成，旨在执行网络分析，主题建模和情感计算任务。我们在这项工作中的贡献是多方面的：(i)我们提供了对参与气候辩论的关键社会行动者及其关系的见解，（ii）我们揭示了缔约方会议期间在社会景观中讨论的主要主题，（iii）我们评估了用户情绪和情绪随时间的演变，以及（iv）我们基于多个维度确定用户社区。此外，我们提出的方法显示出扩展到其他紧急问题的潜力，突出了其通用性和在分析和理解日益引起争议的紧急现象方面的更广泛应用潜力。

{"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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-06-04 DOI: 10.1016/j.pmcj.2025.102075

Gyujeong Lim , Joon-Min Gil , Heonchang Yu

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).

边缘计算是云基础设施中的一种新模式，它分散了计算和存储，使数据和服务更接近用户。这种接近性允许用户以更低的延迟访问高质量或大容量的数据。但是，边缘服务器通常比云服务器拥有更少的资源，因此需要有效的资源管理。新兴研究的重点是提高用户请求到边缘服务器的缓存命中率，从而减少响应延迟并提高效率。尽管如此，如果不考虑可用带宽，那么在边缘环境中保持速度和质量就变得具有挑战性。本文提出了一种自主带宽高效边缘编播（A-BEE-C）方法，以提高边缘服务区内每个设备的有效带宽。本文介绍的编解码是一种将多个文件编码成一个文件再发送给用户的传输方法。a - bee - c引入了一种基于实时带宽评估在单播和编播模式之间切换的动态机制。该方法在边缘服务器带宽有限的情况下，通过将多个用户请求编码为单个编码传输，提高了每个设备的有效带宽。实验结果表明，A-BEE-C将每个设备的平均延迟减少了9.89% (Zipf模式数据最多减少18.45%)，并将每个用户的有效带宽增加了10.15% （Zipf模式数据最多减少18.11%）。

{"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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-06-04 DOI: 10.1016/j.pmcj.2025.102076

Jun Ma , Dimitrije Panic , Roberto Yus , Georgios Bouloukakis

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.

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.

为个体居住者提供适当的热舒适对于提高幸福感和工作效率至关重要。然而，供暖、通风和空调（HVAC）系统占建筑物能源消耗和二氧化碳排放的很大一部分。为了应对当前的能源危机和气候变化，人们提出了创新的方法，利用配备传感器和智能设备的普适和移动计算系统来满足居住者的热舒适和高效的暖通空调管理。然而，评估这些热舒适提供解决方案存在相当大的困难。在现实世界中进行实验会带来一些挑战，比如隐私问题，以及安装和维护传感器基础设施的高成本。另一方面，模拟实验需要准确地模拟真实情况，保证模拟数据的可靠性。为了应对这些挑战，我们提出了Co-zyBench，这是一种利用数字孪生（DT）技术评估个性化热舒适供应系统的创新基准工具。我们的基准测试采用了一个基于模拟的DT来模拟建筑及其HVAC系统，另一个用于模拟居住者动态行为的DT，以及一个联合仿真中间件来实现DT的无缝连接。我们的基准包括基于建筑物的建筑模型、传感器读数和居住者热感觉数据等数据生成dt的机制。它还包括基于标准建筑、暖通空调配置和各种乘员热概况的参考dt。作为评估的结果，基准会根据预期的能源消耗、碳排放、热舒适和居住者公平指标生成报告。我们介绍了基于真实建筑和几个参考DT的DT内最先进的热舒适供应系统的评估结果。

{"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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-06-01 DOI: 10.1016/j.pmcj.2025.102066

Agnaldo de Souza Batista , Aldri Luiz dos Santos

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.

无人驾驶飞行器（UAV）已经被认为是一个用于各种服务的通用平台。在飞行过程中，这些飞行器必须避免碰撞才能安全运行。通过这种方式，他们需要保持空间意识，即了解其覆盖区域内的其他人。然而，机动性和定位阻碍了建立无人机网络基础设施来支持可靠的基本服务。因此，此类车辆需要具有最新信息的位置服务，以抵御虚假位置注入威胁。这项工作提出了FlySafe，这是一种弹性无人机位置共享服务，采用机会主义方法提供无人机的位置。FlySafe考虑了无人机位置的新鲜度，以保持其空间感知。此外，它依靠无人机的位置信息的年龄来触发设备发现。仿真结果表明，FlySafe在无人机操作中实现了高达94.15%的空间感知，对网络中注入的错误位置具有弹性。发现精度达到94.53%，定位误差小于2 m。

{"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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

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

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.

物联网设备的快速增长给传统的以云为中心的架构带来了压力，暴露出延迟、带宽和可靠性方面的局限性。雾计算通过分散离数据源更近的资源来解决这些问题，但是由于动态工作负载、异构资源和严格的QoS要求，任务卸载和资源分配仍然具有挑战性。本研究将任务卸载建模为一个多目标优化问题，考虑了任务优先级、能效、延迟和截止日期。使用马尔可夫决策过程（MDP），它在多代理雾计算设置中应用了三种深度强化学习（DRL）算法- DQN， DDPG和SAC。与之前关注单个代理或孤立度量的工作不同，该方法捕获节点间依赖关系，以提高整体资源使用。仿真结果表明，SAC算法的任务期限成功率为97.3%，资源效率提高了10.1%，在管理动态雾环境方面具有较好的效果。这些结果为未来的物联网系统提供了可扩展的、自适应的卸载策略。

{"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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

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

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.

移动自组织网络（MANET）代表一组无线网络，这些网络不需要集中控制就可以创建网络。此外，MANET作为一个有效的通信网络，但受到安全问题的影响。MANET入侵检测不断监控网络流量以发现潜在的入侵。然而，它需要网络节点来分析和处理数据，这导致了最高的处理费用。为了解决这一难题，本文设计了基于EIK Herd Anaconda Optimization （EHAO）的路由算法和EHAO训练的深度Kronecker网络（EHAO- dkn）进行入侵检测。MANET仿真是实现路由的首要步骤。在路由中考虑了带适应度因子的EHGAO。在基站（BS）中检测到MANET中的入侵存在，其中Z-score归一化应用于规范化日志数据。Wave Hedges度量有效地选择相关特征，EHAO-DKN检测入侵。此外，基于ehao的路由获得了85.30、2.905 J和0.608 mS的最优信任、能量和延迟，基于ehao - dkn的入侵检测的准确率、灵敏度和特异性分别达到92.40%、91.50%和91.50%。

{"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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-05-23 DOI: 10.1016/j.pmcj.2025.102065

Shuvodeep Saha , Chelsea Dobbins , Anubha Gupta , Arindam Dey

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 (N=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.

可穿戴技术的进步使得使用生理信号，如皮电活动（EDA）和心率变异性（HRV），在虚拟现实（VR）中更普遍地用于检测自主神经系统的变化。然而，挑战在于利用这些信号来客观地检测VR中的存在，这通常依赖于可能存在固有偏见的自我报告。本文解决了这一问题，并提出了一项研究（N=26），该研究调查了不同程度的存在对VR生理反应的影响。我们创造了一个中性的VR环境，其中包含了通过调整不同参数调用的三个存在级别（高、中、低）。参与者戴着一个手腕上的可穿戴设备，当他们经历这些环境时，该设备会捕捉他们的生理信号。结果表明，EDA信号的强直和相位成分在不同水平间具有显著的差异。使用EDA的相位和张力成分构建的两个新特征成功地区分了存在水平。对HRV数据的分析表明，使用低频与高频之间的比率，低、中水平之间存在显著差异。

{"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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-05-22 DOI: 10.1016/j.pmcj.2025.102064

Peng Tang, Yong Zhang

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.

随着海上活动的频繁，海上落水事故增多，救援延误往往导致人员死亡。无人机在救援中具有快速定位和实时监控的优点，但无人机拍摄的图像中小目标较多，检测精度不足；同时，由于无人机计算资源的限制，目标检测算法难以部署。为此，本文提出了一种基于YOLOv8s改进的轻型目标检测模型LiteFlex-YOLO，旨在提高无人机海上救援目标检测性能。首先，通过引入P2小目标检测层，增强了模型的小目标感知能力；其次，用轻量化的C2fCIB模块取代C2f模块，降低了计算复杂度，使模型更加轻量化；进一步，利用ODConv（全维动态卷积）增强了主干的特征提取能力；最后，引入SimAM （Simple attention Module）的注意机制，增强对关键特征信息的注意。最终实验结果表明，LiteFlex-YOLO在SeaDronesSee数据集上达到了69.5%的[email protected]，比YOLOv8s提高了18.2%，模型参数降低到YOLOv8s的71.2%。此外，与其他SOTA算法相比，LiteFlex-YOLO在小目标检测精度、模型轻量化和鲁棒性方面表现优异。

{"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 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

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

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 Octopus, which consists of two components: i) an importance sampling-based local loss computation 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 knapsack model-based federated learning client selection 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 Octopus improved the model accuracy by 2.64%

\sim

32.61% with heterogeneous data, and by 1.97%

\sim

11.74% with device heterogeneity, compared to eight state-of-the-art baselines.

联邦学习（FL）作为一种分布式的实时车辆数据处理方式，在车联网（IoV）中被广泛用于改善驾驶体验和提高服务质量。但考虑到车辆节点数据和设备的异构性，随机选择参与模型训练的车辆存在数据偏倚、资源消耗大、收敛速度低等问题。为此，我们提出了Octopus，它由两个部分组成：1)设计了一种基于重要性抽样的局部损失计算方法，为每个客户端请求资源信息，并应用重要性抽样技术评估每个客户端对全局模型收敛的贡献，然后利用以每个客户端的局部损失作为项目值，以系统总训练时间作为背包容量的背包模型加速客户端收敛；Ii)设计基于背包模型的联邦学习客户端选择方法，选择局部损失最优、模型上传速度最大的客户端参与训练。在每一轮训练中，这些客户端在预定义的时间内下载和更新模型，然后使所选的客户端能够继续上传更新的模型参数，以帮助服务器有效地完成模型聚合。实验结果表明，与8个最先进的基线相比，Octopus在异构数据下将模型精度提高了2.64% ~ 32.61%，在设备异构数据下将模型精度提高了1.97% ~ 11.74%。

{"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

Would you mind hiding my malware? Building malicious Android apps with StegoPack 你介意把我的恶意软件藏起来吗？使用StegoPack构建恶意Android应用程序

IF 3 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2025-05-09 DOI: 10.1016/j.pmcj.2025.102060

Danilo Dell’Orco , Giorgio Bernardinetti , Giuseppe Bianchi , Alessio Merlo , Alessandro Pellegrini

This paper empirically explores the resilience of the current Android ecosystem against stegomalware, which involves both Java/Kotlin and native code. To this aim, we rely on a methodology that goes beyond traditional approaches by hiding malicious Java code and extending it to encoding and dynamically loading native libraries at runtime. By merging app resources, steganography, and repackaging, the methodology seamlessly embeds malware samples into the assets of a host app, making detection significantly more challenging. We implemented the methodology in a tool, StegoPack, which allows the extraction and execution of the payload at runtime through reverse steganography. We used StegoPack to embed well-known DEX and native malware samples over 14 years into real Android host apps. We then challenged top-notch antivirus engines, which previously had high detection rates on the original malware, to detect the embedded samples. Our results reveal a significant reduction in the number of detections (up to zero in most cases), indicating that current detection techniques, while thorough in analyzing app code, largely disregard app assets, leading us to believe that steganographic adversaries are not even included in the adversary models of most deployed defensive analysis systems. Thus, we propose potential countermeasures for StegoPack to detect steganographic data in the app assets and the dynamic loader used to execute malware.

本文从经验上探讨了当前Android生态系统对隐恶意软件（涉及Java/Kotlin和本地代码）的弹性。为了实现这一目标，我们依赖于一种超越传统方法的方法，通过隐藏恶意Java代码并将其扩展为在运行时编码和动态加载本机库。通过合并应用程序资源，隐写和重新包装，该方法无缝地将恶意软件样本嵌入到主机应用程序的资产中，使检测更具挑战性。我们在工具StegoPack中实现了该方法，该工具允许通过反向隐写术在运行时提取和执行有效负载。我们使用StegoPack将知名的DEX和本地恶意软件样本嵌入到真正的Android主机应用程序中。然后，我们挑战了顶尖的反病毒引擎，这些引擎以前对原始恶意软件的检测率很高，以检测嵌入的样本。我们的研究结果显示，检测数量显著减少（大多数情况下为零），这表明当前的检测技术虽然在分析应用程序代码时非常彻底，但在很大程度上忽略了应用程序资产，这使我们相信，大多数部署的防御分析系统的对手模型中甚至不包括隐写术对手。因此，我们提出了StegoPack检测应用程序资产中的隐写数据和用于执行恶意软件的动态加载程序的潜在对策。

{"title":"Would you mind hiding my malware? Building malicious Android apps with StegoPack","authors":"Danilo Dell’Orco , Giorgio Bernardinetti , Giuseppe Bianchi , Alessio Merlo , Alessandro Pellegrini","doi":"10.1016/j.pmcj.2025.102060","DOIUrl":"10.1016/j.pmcj.2025.102060","url":null,"abstract":"<div><div>This paper empirically explores the resilience of the current Android ecosystem against stegomalware, which involves both Java/Kotlin and native code. To this aim, we rely on a methodology that goes beyond traditional approaches by hiding malicious Java code and extending it to encoding and dynamically loading native libraries at runtime. By merging app resources, steganography, and repackaging, the methodology seamlessly embeds malware samples into the assets of a host app, making detection significantly more challenging. We implemented the methodology in a tool, StegoPack, which allows the extraction and execution of the payload at runtime through reverse steganography. We used StegoPack to embed well-known DEX and native malware samples over 14 years into real Android host apps. We then challenged top-notch antivirus engines, which previously had high detection rates on the original malware, to detect the embedded samples. Our results reveal a significant reduction in the number of detections (up to zero in most cases), indicating that current detection techniques, while thorough in analyzing app code, largely disregard app assets, leading us to believe that steganographic adversaries are not even included in the adversary models of most deployed defensive analysis systems. Thus, we propose potential countermeasures for StegoPack to detect steganographic data in the app assets and the dynamic loader used to execute malware.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"111 ","pages":"Article 102060"},"PeriodicalIF":3.0,"publicationDate":"2025-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144068520","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