Pervasive and Mobile Computing最新文献_第9页

HearDrinking: Drunkenness detection and BACs predictions based on acoustic signal 听觉饮酒：基于声信号的醉酒检测和BACs预测

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

Pervasive and Mobile Computing

Pub Date : 2025-03-01 Epub Date: 2025-02-10 DOI: 10.1016/j.pmcj.2025.102020

Yuan Wu , Gaorong Zhao , Likairui Zhang , Xinrong Hu , Lei Ding

Alcohol poisoning is a severe health concern resulting from excessive drinking and can be life-threatening. By utilizing home monitoring, individuals can quickly determine their blood alcohol content, thus preventing it from reaching hazardous levels. However, most existing systems for drunkenness detection require extra hardware or much effort from the user, making these systems impractical for detecting drunkenness in real life. Motivated by this, we present a device-free, noise-resistant drunkenness detection system named HearDrinking based on smartphone, which utilizes microphone of smartphone to record human’s voice activity, then mine drunkenness related features to yield accurate drunkenness detection. However, using acoustic signal to detect drunkenness is non-trivial since voice activities are prone to be interfered by ambient noise, and extracting fine-grained representations related to drunkenness from voice activities remains unresolved. On one hand, HearDrinking employs a multi-modal fusion method to realize noise-resistant voice activity detection. On the other hand, HearDrinking initially calculates the log-Mel spectrograms from the speech signal. The log-Mel spectrograms contain temporal and spectral information absent in image data. Therefore, conventional convolutions designed for images often have limited effectiveness in extracting features from log-Mel spectrograms. To overcome this limitation, we integrate Omni-dimensional Dynamic Convolution (ODConv) with ShuffleNetV2, creating OD-ShuffleNetV2. ODConv replaces certain conventional convolutions in the ShuffleNetV2 network. Multiple convolution cores are fused based on the log-Mel spectrogram, taking into account multi-dimensional attention, thereby optimizing the network structure. Comprehensive experiments with 15 participants reveal drunkenness detection accuracy of 96.08% and Blood Alcohol Content (BAC) predictions with an average error of 5 mg/dl.

酒精中毒是由过量饮酒引起的严重健康问题，可能危及生命。通过使用家庭监控，个人可以快速确定他们的血液酒精含量，从而防止其达到危险水平。然而，大多数现有的醉酒检测系统需要额外的硬件或用户的大量努力，使得这些系统在现实生活中检测醉酒不现实。基于此，我们提出了一种基于智能手机的无设备、抗噪声的醉酒检测系统——HearDrinking。该系统利用智能手机的麦克风记录人的语音活动，进而挖掘醉酒相关特征，实现准确的醉酒检测。然而，由于语音活动容易受到环境噪声的干扰，并且从语音活动中提取与醉酒相关的细粒度表示仍然没有解决，因此使用声学信号来检测醉酒是非常重要的。一方面，HearDrinking采用多模态融合方法实现抗噪声的语音活动检测。另一方面，HearDrinking首先从语音信号中计算log-Mel谱图。对数mel谱图包含了图像数据中没有的时间和光谱信息。因此，为图像设计的传统卷积在从对数-梅尔谱图中提取特征方面往往效果有限。为了克服这一限制，我们将全维动态卷积（ODConv）与ShuffleNetV2集成，创建了OD-ShuffleNetV2。ODConv取代了ShuffleNetV2网络中的某些传统卷积。基于log-Mel谱图融合多个卷积核，考虑到多维关注，从而优化网络结构。15名参与者的综合实验表明，醉酒检测准确率为96.08%，血液酒精含量（BAC）预测平均误差为5 mg/dl。

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引用次数: 0

EncCluster: Scalable functional encryption in federated learning through weight clustering and probabilistic filters EncCluster：通过权重聚类和概率过滤器在联合学习中进行可扩展的功能加密

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

Pervasive and Mobile Computing

Pub Date : 2025-03-01 Epub Date: 2025-02-21 DOI: 10.1016/j.pmcj.2025.102021

Vasileios Tsouvalas , Samaneh Mohammadi , Ali Balador , Tanir Ozcelebi , Francesco Flammini , Nirvana Meratnia

Federated Learning (FL) enables model training across decentralized devices by communicating solely local model updates to an aggregation server. Although such limited data sharing makes FL more secure than centralized approached, FL remains vulnerable to inference attacks during model update transmissions. Existing secure aggregation approaches rely on differential privacy or cryptographic schemes like Functional Encryption (FE) to safeguard individual client data. However, such strategies can reduce performance or introduce unacceptable computational and communication overheads on clients running on edge devices with limited resources. In this work, we present EncCluster, a novel method that integrates model compression through weight clustering with recent decentralized FE and privacy-enhancing data encoding using probabilistic filters to deliver strong privacy guarantees in FL without affecting model performance or adding unnecessary burdens to clients. We performed a comprehensive evaluation, spanning various datasets and architectures, to demonstrate EncCluster scalability across encryption levels. Our findings reveal that EncCluster significantly reduces communication costs — below even conventional FedAvg — and accelerates encryption by more than four times over all baselines; at the same time, it maintains high model accuracy and enhanced privacy assurances.

联邦学习（FL）通过与聚合服务器单独通信本地模型更新，支持跨分散设备的模型训练。尽管这种有限的数据共享使FL比集中式方法更安全，但FL在模型更新传输过程中仍然容易受到推理攻击。现有的安全聚合方法依赖于差分隐私或像功能加密（Functional Encryption， FE）这样的加密方案来保护单个客户端数据。然而，这种策略可能会降低性能，或者在资源有限的边缘设备上运行的客户端上引入不可接受的计算和通信开销。在这项工作中，我们提出了EncCluster，这是一种新颖的方法，它通过权重聚类集成模型压缩与最近的分散FE和使用概率过滤器增强隐私的数据编码，在FL中提供强大的隐私保证，而不会影响模型性能或给客户端增加不必要的负担。我们进行了全面的评估，涵盖了各种数据集和架构，以展示EncCluster跨加密级别的可扩展性。我们的研究结果表明，EncCluster显著降低了通信成本——甚至低于传统的fedag——并将加密速度提高了四倍以上；同时保持了较高的模型精度，增强了隐私保障。

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引用次数: 0

Climate smart computing: A perspective 气候智能计算：一个视角

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

Pervasive and Mobile Computing

Pub Date : 2025-03-01 Epub Date: 2025-02-07 DOI: 10.1016/j.pmcj.2025.102019

Mingzhou Yang, Bharat Jayaprakash, Subhankar Ghosh, Hyeonjung Tari Jung, Matthew Eagon, William F. Northrop, Shashi Shekhar

Climate change is a societal grand challenge and many nations have signed the Paris Agreement (2015) aiming for net-zero emissions. The computing community has an opportunity to contribute significantly to addressing climate change across all its dimensions, including understanding, resilience, mitigation, and adaptation. Traditional computing methods face major challenges. For example, machine learning is overwhelmed due to non-stationarity (e.g., climate change), data paucity (e.g., rare climate events), the high cost of ground truth collection, and the need to observe natural laws (e.g., conservation of mass). This paper shares a perspective on a range of climate-smart computing challenges and opportunities based on multi-decade scholarly activities and acknowledges the broader societal debate on climate solutions. Moreover, it envisions advancements in computing methods specifically designed to tackle the challenges posed by climate change. It calls for a broad array of computer science strategies and innovations to be developed to address the multifaceted challenges of climate change.

气候变化是一个巨大的社会挑战，许多国家签署了旨在实现净零排放的《巴黎协定》（2015年）。计算界有机会为解决气候变化的所有方面作出重大贡献，包括了解、复原力、缓解和适应。传统的计算方法面临重大挑战。例如，由于非平稳性（如气候变化）、数据缺乏（如罕见的气候事件）、地面真相收集的高成本以及需要遵守自然规律（如质量守恒），机器学习不堪重负。本文分享了基于数十年学术活动的一系列气候智能计算挑战和机遇的观点，并承认关于气候解决方案的更广泛的社会辩论。此外，它还设想了专门为应对气候变化带来的挑战而设计的计算方法的进步。它要求开发一系列广泛的计算机科学战略和创新，以应对气候变化带来的多方面挑战。

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引用次数: 0

A novel middleware for adaptive and efficient split computing for real-time object detection 一种用于实时目标检测的自适应高效分割计算中间件

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

Pervasive and Mobile Computing

Pub Date : 2025-03-01 Epub Date: 2025-02-22 DOI: 10.1016/j.pmcj.2025.102028

Matteo Mendula , Paolo Bellavista , Marco Levorato , Sharon Ladron de Guevara Contreras

Real-world applications requiring real-time responsiveness frequently rely on energy-intensive and compute-heavy neural network algorithms. Strategies include deploying distributed and optimized Deep Neural Networks on mobile devices, which can lead to considerable energy consumption and degraded performance, or offloading larger models to edge servers, which requires low-latency wireless channels. Here we present Furcifer, a novel middleware that autonomously adjusts the computing strategy (i.e., local computing, edge computing, or split computing) based on context conditions. Utilizing container-based services and low-complexity predictors that generalize across environments, Furcifer supports supervised compression as a viable alternative to pure local or remote processing in real-time environments. An extensive set of experiments coversdiverse scenarios, including both stable and highly dynamic channel environments with unpredictable changes in connection quality and load. In moderate-varying scenarios, Furcifer demonstrates significant benefits: achieving a 2x reduction in energy consumption, a 30% higher mean Average Precision score compared to local computing, and a three-fold FPS increase over static offloading. In highly dynamic environments with unreliable connectivity and rapid increases in concurrent clients, Furcifer’s predictive capabilities preserves up to 30% energy, achieving a 16% higher accuracy rate, and completing 80% more frame inferences compared to pure local computing and approaches without trend forecasting, respectively.

现实世界中需要实时响应的应用经常依赖于能源密集型和计算量大的神经网络算法。策略包括在移动设备上部署分布式优化深度神经网络，这可能会导致相当大的能耗和性能下降；或者将大型模型卸载到边缘服务器，这需要低延迟无线信道。我们在此介绍一种新型中间件 Furcifer，它能根据上下文条件自主调整计算策略（即本地计算、边缘计算或分离计算）。利用基于容器的服务和可跨环境通用的低复杂度预测器，Furcifer 支持将监督压缩作为实时环境中纯本地或远程处理的可行替代方案。大量实验涵盖了各种不同的场景，包括连接质量和负载发生不可预测变化的稳定和高度动态信道环境。在中度变化的场景中，Furcifer 显示了显著的优势：与本地计算相比，能耗降低了 2 倍，平均精度分数提高了 30%，FPS 提高了三倍。在连接不可靠、并发客户端迅速增加的高动态环境中，Furcifer 的预测能力可节省多达 30% 的能源，准确率提高了 16%，与纯本地计算和无趋势预测的方法相比，完成的帧推理分别增加了 80%。

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引用次数: 0

A Real-time skeleton-based fall detection algorithm based on temporal convolutional networks and transformer encoder 一种基于时间卷积网络和变压器编码器的基于骨骼的实时跌倒检测算法

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 Epub Date: 2025-01-20 DOI: 10.1016/j.pmcj.2025.102016

Xiaoqun Yu , Chenfeng Wang , Wenyu Wu , Shuping Xiong

As the population of older individuals living independently rises, coupled with the heightened risk of falls among this demographic, the need for automatic fall detection systems becomes increasingly urgent to ensure timely medical intervention. Computer vision (CV)-based methodologies have emerged as a preferred approach among researchers due to their contactless and pervasive nature. However, existing CV-based solutions often suffer from either poor robustness or prohibitively high computational requirements, impeding their practical implementation in elderly living environments. To address these challenges, we introduce TCNTE, a real-time skeleton-based fall detection algorithm that combines Temporal Convolutional Network (TCN) with Transformer Encoder (TE). We also successfully mitigate the severe class imbalance issue by implementing weighted focal loss. Cross-validation on multiple publicly available vision-based fall datasets demonstrates TCNTE's superiority over individual models (TCN and TE) and existing state-of-the-art fall detection algorithms, achieving remarkable accuracies (front view of UP-Fall: 99.58 %; side view of UP-Fall: 98.75 %; Le2i: 97.01 %; GMDCSA-24: 92.99 %) alongside practical viability. Visualizations using t-distributed stochastic neighbor embedding (t-SNE) reveal TCNTE's superior separation margin and cohesive clustering between fall and non-fall classes compared to TCN and TE. Crucially, TCNTE is designed for pervasive deployment in mobile and resource-constrained environments. Integrated with YOLOv8 pose estimation and BoT-SORT human tracking, the algorithm operates on NVIDIA Jetson Orin NX edge device, achieving an average frame rate of 19 fps for single-person and 17 fps for two-person scenarios. With its validated accuracy and impressive real-time performance, TCNTE holds significant promise for practical fall detection applications in older adult care settings.

随着独立生活的老年人人口的增加，加上这一人口中跌倒风险的增加，对自动跌倒检测系统的需求变得越来越迫切，以确保及时的医疗干预。基于计算机视觉（CV）的方法由于其非接触式和普及性而成为研究人员的首选方法。然而，现有的基于cv的解决方案往往存在鲁棒性差或计算需求过高的问题，阻碍了它们在老年人生活环境中的实际实现。为了解决这些挑战，我们引入了TCNTE，这是一种结合了时间卷积网络（TCN）和变压器编码器（TE）的基于骨骼的实时跌倒检测算法。我们还成功地缓解了严重的类不平衡问题，通过实现加权焦点损失。在多个公开可用的基于视觉的跌倒数据集上的交叉验证表明，TCNTE优于单个模型（TCN和TE）和现有的最先进的跌倒检测算法，实现了显着的准确性(UP-Fall的前视图：99.58%；上下侧视图：98.75%；Le2i: 97.01%；gmdsa -24: 92.99%)和实际可行性。使用t分布随机邻居嵌入（t-SNE）的可视化显示，与TCN和TE相比，TCNTE在跌倒类和非跌倒类之间具有更好的分离裕度和内聚性。至关重要的是，TCNTE是为移动和资源受限环境中的普遍部署而设计的。该算法集成了YOLOv8姿态估计和BoT-SORT人体跟踪，在NVIDIA Jetson Orin NX边缘设备上运行，单人场景平均帧率为19 fps，两人场景平均帧率为17 fps。凭借其经过验证的准确性和令人印象深刻的实时性能，TCNTE在老年人护理环境中的实际跌倒检测应用中具有重要的前景。

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引用次数: 0

Machinery detection by impulsive noise recognition using WiFi sensing 基于WiFi传感的脉冲噪声识别机械检测

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 Epub Date: 2025-02-04 DOI: 10.1016/j.pmcj.2025.102018

Iratxe Landa , Guillermo Diaz , Iker Sobron , Iñaki Eizmendi , Manuel Velez

Engines and electrical devices in operation generate electromagnetic pulses, also called impulsive noise (IN), that interfere with wireless signals. The IN shall affect the channel estimation process and is, therefore, present in the channel state information (CSI) provided by wireless receivers. In this paper, impulsive noise (IN) is used as a fingerprint of electrical devices to identify the IN sources that interfere with a WiFi signal, taking into account that each individual machine has a unique pattern of impulsive noise. In this sense, the WiFi CSI provides valuable information to recognize the IN sources through deep learning (DL) strategies. Two DL models have been proposed and tested on two experimental data sets for multiclass and multilabel analysis; in multiclass, devices can operate alone during the measurement, and in multilabel, multiple devices can work simultaneously in a more realistic scenario. The model transferability between location and days has also been evaluated by analyzing two different IN feature sets for device classification with the Few-shot-learning (FSL) model. Results show that the proposed DL models can recognize multiple devices working simultaneously through the IN and also offer an acceptable transferability performance (

\sim

80% accuracy for a five-class problem).

发动机和运行中的电气设备产生电磁脉冲，也称为脉冲噪声（in），干扰无线信号。IN将影响信道估计过程，因此，它存在于无线接收器提供的信道状态信息（CSI）中。在本文中，考虑到每台机器都有独特的脉冲噪声模式，脉冲噪声（In）被用作电子设备的指纹来识别干扰WiFi信号的In源。从这个意义上说，WiFi CSI通过深度学习（DL）策略为识别In源提供了有价值的信息。提出了两个深度学习模型，并在两个实验数据集上进行了多类和多标签分析的测试；在多类别中，设备可以在测量过程中单独运行，而在多标签中，多个设备可以在更现实的场景中同时工作。通过使用Few-shot-learning （FSL）模型分析两种不同的设备分类IN特征集，还评估了模型在位置和日期之间的可转移性。结果表明，所提出的深度学习模型可以识别通过IN同时工作的多个设备，并且还提供了可接受的可转移性性能（对于五类问题的准确率为80%）。

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引用次数: 0

Advancing user-space networking for DDS message-oriented middleware: Further extensions 为DDS面向消息的中间件推进用户空间网络：进一步扩展

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 Epub Date: 2025-01-27 DOI: 10.1016/j.pmcj.2025.102013

Vincent Bode , Carsten Trinitis , Martin Schulz , David Buettner , Tobias Preclik

Due to the flexibility it offers, publish–subscribe messaging middleware is a popular choice in Industrial IoT (IIoT) applications. The Data Distribution Service (DDS) is a widely used industry standard for these systems with a focus on versatility and extensibility, implemented by multiple vendors and present in myriad deployments across industries like aerospace, healthcare and industrial automation. However, many IoT scenarios require real-time capabilities for deployments with rigid timing, reliability and resource constraints, while publish–subscribe mechanisms currently rely on components that are not strictly real-time capable, such as the Linux networking stack, making it hard to provide robust performance guarantees without large safety margins.

In order to make publish–subscribe approaches viable and efficient also in such real-time scenarios, we introduce user-space DDS networking transport extensions, allowing us to fast-track the communication hot path by bypassing the Linux kernel. For this purpose, we extend the best-performing vendor implementation from a previous study, CycloneDDS, to include modules for two widespread user-space networking technologies, the Data Plane Development Kit (DPDK) and the eXpress Data Path (XDP). Building on this, we additionally offer two more extensions to the second most performant implementation FastDDS, also based on DPDK and XDP, and realize novel optimizations not present in the original extension implementations. We evaluate each extension’s performance benefits against four existing DDS implementations (OpenDDS, RTI Connext, FastDDS and CycloneDDS). The DPDK-based and XDP-based extensions offer a performance benefit of 31%–38% and 18%–22% reduced mean latency, respectively, as well as an increase in bandwidth and sample rate throughput of at least 160%, while reducing the latency bound by at least 93%, demonstrating the performance and dependability advantages of circumventing the kernel for real-time communications.

由于其提供的灵活性，发布-订阅消息传递中间件是工业物联网（IIoT）应用程序中的流行选择。数据分发服务（DDS）是这些系统广泛使用的行业标准，其重点是多功能性和可扩展性，由多个供应商实现，并在航空航天、医疗保健和工业自动化等行业的无数部署中出现。然而，许多物联网场景需要具有严格定时、可靠性和资源约束的部署的实时能力，而发布-订阅机制目前依赖于不具有严格实时能力的组件，例如Linux网络堆栈，因此在没有大安全裕度的情况下很难提供强大的性能保证。为了使发布-订阅方法在这种实时场景中也可行且高效，我们引入了用户空间DDS网络传输扩展，允许我们绕过Linux内核快速跟踪通信热路径。为此，我们从之前的研究中扩展了性能最好的供应商实现，CycloneDDS，包括两种广泛的用户空间网络技术的模块，数据平面开发工具包（DPDK）和快速数据路径（XDP）。在此基础上，我们还为性能第二高的实现FastDDS（同样基于DPDK和XDP）提供了另外两个扩展，并实现了原始扩展实现中没有的新优化。我们针对四种现有的DDS实现（OpenDDS、RTI Connext、FastDDS和CycloneDDS）评估了每个扩展的性能优势。基于dpdk和基于xdp的扩展分别提供了31%-38%和18%-22%的性能优势，平均延迟分别减少了31%-38%和18%-22%，带宽和采样率吞吐量增加了至少160%，同时延迟限制减少了至少93%，证明了绕过内核进行实时通信的性能和可靠性优势。

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引用次数: 0

FastPlan: A three-step framework for accelerating drone-centric search operations in post-disaster relief FastPlan：在灾后救援中加速以无人机为中心的搜索行动的三步框架

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 Epub Date: 2025-01-31 DOI: 10.1016/j.pmcj.2025.102017

Sunho Lim , Ingyu Lee , Gyu Sang Choi , Jinseok Chae , Ellora Ashish , Eric Ward , Cong Pu

Commercially well-known drones are increasingly popular and have been deployed in post-disaster relief to support traditional search and rescue operations. However, there are still barriers to conducting a drone-centric search operation, including but not limited to operational difficulty for non-professional drone pilots, inefficient pre-planned path, and more importantly initial setup delay. In this paper, we propose a small-scale prototype of a mobile framework, called FastPlan, to facilitate the development for expediting drone-centric search decisions and operations. The basic idea is to automate the required search operations with no or minimized user intervention. The framework consists of three major operations: map extraction, clustering, and path planning. We extract a set of target POIs and metadata from a public map (i.e., Google Maps) integrated with a customized local database to decide where and what to search. Then we deploy simple density-based clustering and search priority-based path planning strategies to efficiently group and cover the POIs. The framework can be extended flexibly by replacing individual operations with an alternative depending on the search priority, like a Lego block. We implement the framework as an Android-based mobile software (App) for a proof-of-the-concept and conduct extensive simulation experiments for performance evaluation. We analyze the different performance behavior and their implication and applicability. The results indicate that the proposed framework can be a viable approach to post-disaster relief.

商业上知名的无人机越来越受欢迎，并已部署在灾后救援中，以支持传统的搜救行动。然而，进行以无人机为中心的搜索操作仍然存在障碍，包括但不限于非专业无人机飞行员的操作难度，低效的预先规划路径，更重要的是初始设置延迟。在本文中，我们提出了一个名为FastPlan的小型移动框架原型，以促进以无人机为中心的搜索决策和操作的快速发展。其基本思想是在没有或最小化用户干预的情况下自动执行所需的搜索操作。该框架包括三个主要操作：地图提取、聚类和路径规划。我们从公共地图（即谷歌Maps）中提取一组目标poi和元数据，并与定制的本地数据库集成，以决定搜索的位置和内容。然后，我们部署简单的基于密度的聚类和基于搜索优先级的路径规划策略来有效地分组和覆盖poi。通过根据搜索优先级（如乐高积木）替换单个操作，可以灵活地扩展框架。我们将该框架作为基于android的移动软件（App）实现，以进行概念验证，并进行广泛的模拟实验以进行性能评估。分析了不同的性能行为及其含义和适用性。结果表明，该框架是一种可行的灾后救援方法。

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引用次数: 0

TrustMD — A multi-layer framework for domain, edge and D2D caching based on trust dissemination and blockchain TrustMD——基于信任传播和区块链的域、边缘和D2D缓存的多层框架

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 Epub Date: 2025-01-27 DOI: 10.1016/j.pmcj.2025.102015

Acquila Santos Rocha , Billy Anderson Pinheiro , Weverton Cordeiro , Vinicius C.M. Borges

Device-to-Device communication (D2D), combined with edge caching and distinct domains, is a promising approach for offloading data from wireless mobile networks. However, user security is still an open issue in D2D communication. Security vulnerabilities remain possible as a side effect of enabling straightforward, direct, and spontaneous interactions between untrustworthy users. To address this issue, this work involves designing TrustMD (Trust Multiple Domain), a multi-layer framework combining diverse technologies inspired by blockchain and trust management to develop a secure and scalable framework for multi-domain, edge, and D2D caching layers. Specifically, TrustMD combines edge trust storage with blockchain for distributed storage management in a multi-layer architecture designed to store trust control data in edge efficiently and D2D networks across different domains. Our experiments with TrustMD showed a significant improvement in data goodput, reaching as high as 95% of the total network throughput. In contrast, state-of-the-art approaches without trust control dissemination achieved at most 80%. Even though we observed a 7% increase in D2D overhead, TrustMD can effectively control latency levels. TrustMD managed security effectively without compromising network performance, reducing false negative rates up to 31% in the best-case scenario. TrustMD offers a scalable and effective security solution that boosts network performance and ensures robust protection.

设备到设备通信（D2D）结合边缘缓存和不同域，是一种很有前途的从无线移动网络中卸载数据的方法。然而，在D2D通信中，用户安全仍然是一个悬而未决的问题。在不可信的用户之间支持直接、直接和自发交互的副作用仍然存在安全漏洞。为了解决这个问题，这项工作涉及设计TrustMD（信任多域），这是一个多层框架，结合了受区块链和信任管理启发的各种技术，为多域、边缘和D2D缓存层开发一个安全且可扩展的框架。具体来说，TrustMD将边缘信任存储与区块链相结合，在多层架构中进行分布式存储管理，旨在有效地将信任控制数据存储在边缘和跨不同域的D2D网络中。我们对TrustMD的实验表明，在数据传输方面有了显著的改善，达到了总网络吞吐量的95%。相比之下，没有信任控制传播的最先进方法最多达到80%。尽管我们观察到D2D开销增加了7%，但TrustMD可以有效地控制延迟水平。TrustMD在不影响网络性能的情况下有效地管理安全性，在最佳情况下可将误报率降低31%。TrustMD提供了一个可扩展和有效的安全解决方案，提高网络性能，并确保强大的保护。

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引用次数: 0

Vspp: Verifiable, shareable, and privacy-preserving access control scheme for IoV Vspp：可验证、可共享、保护隐私的IoV访问控制方案

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 Epub Date: 2025-01-26 DOI: 10.1016/j.pmcj.2025.102014

Youwang Sun , Chunhua Jin , Xinying Liu , Lingwen Kong , Changhui Yu , Guanhua Chen , Liqing Chen

Internet of Vehicles (IoV) is a specialized application of Internet of Things (IoT), which interconnects vehicles and cloud platforms by using various communication devices and computing technologies to realize the transmission and sharing of vehicle information and enhance the driving experience. However, vehicle users face challenges in identity, data, and inside security when using IoV. In order to solve these problems, we propose a verifiable, shareable, and privacy-preserving access control scheme for IoV. In our scheme, we use zero-knowledge proof (ZKP) to ensure the security of user identity. More specifically, it can enable user anonymity and authenticity without revealing any additional information associated with the user. Meanwhile, we employ proxy re-encryption (PRE) to provide confidential sharing and secure the operation of data. In addition, we use the cryptographic reverse firewall (CRF) to ensure users’ internal security. It can prevent algorithm substitution attacks while ensuring chosen plaintext attack security. Finally, compared to other schemes, our scheme not only enables anonymity, traceability, unlinkability, and confidentiality but is also resistant to insider attacks. Performance evaluation shows that our scheme surpasses the other schemes in terms of time and storage costs.

车联网（IoV）是物联网（IoT）的专门应用，通过各种通信设备和计算技术将车辆与云平台互联，实现车辆信息的传输和共享，提升驾驶体验。然而，车辆用户在使用车联网时面临着身份、数据和内部安全方面的挑战。为了解决这些问题，我们提出了一种可验证、可共享、保护隐私的车联网访问控制方案。在我们的方案中，我们使用零知识证明（ZKP）来确保用户身份的安全性。更具体地说，它可以实现用户匿名性和真实性，而不会泄露与用户相关的任何附加信息。同时，采用代理再加密（PRE）技术实现数据的保密共享，保证数据的安全运行。此外，我们使用加密反向防火墙（CRF）来确保用户的内部安全。在保证所选明文攻击安全性的同时，可以防止算法替代攻击。最后，与其他方案相比，我们的方案不仅具有匿名性、可追溯性、不可链接性和保密性，而且还具有抗内部攻击的能力。性能评估表明，我们的方案在时间和存储成本方面优于其他方案。

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引用次数: 0