Pervasive and Mobile Computing最新文献_第3页

Edge computing and 5G network integration for mobility-aware service deployments 边缘计算和5G网络集成，用于移动感知服务部署

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-11-24 DOI: 10.1016/j.pmcj.2025.102134

João Gameiro , Rodrigo Rosmaninho , Gonçalo Perna , Pedro Rito , Susana Sargento , Carlos Marques , Filipe Pinto

The growing scale of smart city sensing devices and infrastructure entails a wide variety of available sensing information that can provide valuable insights into user mobility and traffic congestion. This information can be used to optimize service delivery through the development of mobility-aware services. 5G systems and their associated technologies provide an ideal environment with capabilities to efficiently support edge computing and bring the processing and storage resources closer to the end users, which results in a latency and backhaul usage reduction.

This article proposes the integration of edge computing in 5G operator network and a mobility/road-side infrastructure with edge orchestration to provide mobility-aware services to the end-users on demand. With this approach, a service instantiation can be translated into resource allocation both on the 5G platform through multi-slicing and the edge infrastructure. Resource management is then optimized for the users on the move by continuously allocating the necessary virtual network slices, processing, and storage resources in the appropriate locations for the user to consume its services while maintaining the appropriate QoS levels and optimized resource distribution in the edge platform. This approach is evaluated in a real mobile 5G network with emulated Radio Access Network (RAN) resources through two use cases based on infotainment and emergency services. The results show that the approach is efficient in using mobility, service requirements, and platform’s resources information to enable a proactive resource reservation both in the 5G base stations and edge computing nodes throughout the path traversed by the users.

智能城市传感设备和基础设施的规模不断扩大，需要各种各样的可用传感信息，这些信息可以为用户移动性和交通拥堵提供有价值的见解。这些信息可用于通过开发移动感知服务来优化服务交付。5G系统及其相关技术提供了一个理想的环境，能够有效地支持边缘计算，并使处理和存储资源更接近最终用户，从而减少延迟和回程使用。本文提出在5G运营商网络中集成边缘计算和具有边缘编排的移动/道路侧基础设施，以按需为最终用户提供移动感知服务。通过这种方法，可以通过多切片和边缘基础设施将服务实例化转换为5G平台上的资源分配。然后，通过在适当的位置持续分配必要的虚拟网络切片、处理和存储资源，以便用户使用其服务，同时在边缘平台中保持适当的QoS级别和优化的资源分配，从而为移动中的用户优化资源管理。通过基于信息娱乐和应急服务的两个用例，在具有模拟无线接入网（RAN）资源的真实移动5G网络中对该方法进行了评估。结果表明，该方法能够有效地利用移动性、业务需求和平台的资源信息，在用户走过的整个路径上实现5G基站和边缘计算节点的主动资源预留。

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引用次数: 0

Supervised momentum contrastive learning for mmWave-based human action recognition 基于毫米波的人类动作识别的监督动量对比学习

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-11-10 DOI: 10.1016/j.pmcj.2025.102131

Huimin Yao , Dengao Li , Jumin Zhao

Accurate human action recognition (HAR) using sparse millimeter-wave (mmWave) radar point clouds faces significant challenges. Existing approaches suffer from ineffective feature extraction in sparse point clouds, vulnerability to radar noise and multi-path interference, and significant intra-class variance induced by distance–angle variations. To overcome these limitations, we propose SMC-HAR, a novel Supervised Momentum Contrast framework for HAR. SMC-HAR leverages contrastive learning with a joint loss function that integrates supervised contrastive loss and cross-entropy loss. This design enhances feature discriminability, mitigates intra-class dispersion, and promotes feature aggregation within classes while improving separation between classes. Our momentum mechanism dynamically optimizes the feature distribution reference space and bolsters robustness against noise and multi-path interference. Furthermore, we design a domain-specific augmentation optimization strategy tailored for mmWave radar point clouds in HAR, which explores optimal synergistic combinations of augmentations to better adapt to point cloud sparsity and action pattern characteristics. Experimental results on the widely used MM-Fi dataset show that SMC-HAR achieves a classification accuracy of 88.40%, marking a substantial 8.40% improvement over the baseline cross-entropy model. This demonstrates the effectiveness of our framework in enhancing feature discriminability and robustness for mmWave point cloud-based HAR.

利用稀疏毫米波（mmWave）雷达点云进行准确的人体动作识别（HAR）面临着重大挑战。现有方法存在稀疏点云特征提取效果不佳、易受雷达噪声和多径干扰以及距离-角度变化引起的类内方差较大等问题。为了克服这些限制，我们提出了SMC-HAR，一种新的监督动量对比框架。SMC-HAR利用对比学习和一个联合损失函数，该函数集成了监督对比损失和交叉熵损失。这种设计增强了特征的可辨别性，减轻了类内部的分散，促进了类内部的特征聚合，同时改善了类之间的分离。我们的动量机制动态优化了特征分布参考空间，增强了对噪声和多径干扰的鲁棒性。此外，我们为HAR中的毫米波雷达点云设计了一个特定领域的增强优化策略，该策略探索了增强的最佳协同组合，以更好地适应点云稀疏性和行动模式特征。在广泛使用的MM-Fi数据集上的实验结果表明，SMC-HAR的分类准确率达到了88.40%，比基线交叉熵模型提高了8.40%。这证明了我们的框架在增强基于毫米波点云的HAR的特征可辨别性和鲁棒性方面的有效性。

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引用次数: 0

Lyapunov-based queue stability optimization for task offloading in UAV-assisted VEC 基于lyapunov的无人机辅助VEC任务卸载队列稳定性优化

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-10-30 DOI: 10.1016/j.pmcj.2025.102126

Xin He, Yuanzhi Ni, Hongfeng Tao

Due to the presence of increasing computation demands in telematics, RSUs are proposed to play a critical role in Vehicular Edge Computing (VEC). However, how to simultaneously improve the communication quality and reduce the service latency becomes a severe challenge due to the resource shortage. To tackle these issues, we explore how to utilize Unmanned Aerial Vehicles (UAVs) in VEC to facilitate the task offloading performance, i.e., the latency of the service and the stability of the task queues. A Genetic Algorithm (GA)-based Lyapunov optimization framework is proposed for task scheduling optimization. It aims to minimize system cost and stabilize edge server task queues by obtaining the optimal decision. The proposed algorithm optimizes the Lyapunov drift plus penalty function in each time slot. Finally, simulations verify that proposed LyGA scheme is able to achieve the trade-off between minimizing the system cost and maintaining queue stability compared with the benchmark methods.

由于远程信息处理对计算量的需求越来越大，rsu在车辆边缘计算（VEC）中发挥着至关重要的作用。然而，由于资源短缺，如何在提高通信质量的同时降低业务延迟成为一个严峻的挑战。为了解决这些问题，我们探索了如何在VEC中利用无人机来促进任务卸载性能，即服务的延迟和任务队列的稳定性。针对任务调度优化问题，提出了一种基于遗传算法的Lyapunov优化框架。它的目标是通过获得最优决策来最小化系统成本和稳定边缘服务器任务队列。该算法对每个时隙的李雅普诺夫漂移加惩罚函数进行了优化。最后，通过仿真验证了与基准方法相比，所提出的LyGA方案能够在最小化系统成本和保持队列稳定性之间实现折衷。

引用次数: 0

A blockchain-assisted privacy-preserving framework for Mobile CrowdSensing 一个区块链辅助的移动众测隐私保护框架

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-11-05 DOI: 10.1016/j.pmcj.2025.102125

Nitish Andola , Vijay Kumar Yadav

Mobile Crowdsensing (MCS) has emerged as a powerful paradigm for large-scale data collection using mobile devices. However, traditional MCS frameworks pose significant privacy risks, particularly concerning worker identity and location disclosure during task execution and payment processing. Existing privacy-preserving approaches, such as dummy location insertion, k-anonymity, and differential privacy, either compromise efficiency or fail to address all privacy leakage vectors. To overcome these challenges, we propose a Zero-Knowledge Proof (ZKP)-based blockchain framework that ensures robust privacy protection while maintaining system efficiency. Our protocol leverages zk-SNARKs to protect worker identity and location during task submission and payment transactions. By integrating Ethereum smart contracts, we eliminate reliance on a centralized Crowdsensing Service Provider (CSP), mitigating single point of failure and Denial-of-Service (DoS) risks. We have provided formal security proofs for the transaction privacy. Through detailed experimentation on the Sepolia test network, we analyze gas costs and transaction finalization times, demonstrating that proof verification, despite its computational complexity, remains practical due to off-chain proof generation. The proposed framework optimizes on-chain and off-chain interactions, enhancing scalability while preserving user privacy. A comparative analysis against existing frameworks highlights our model’s superior privacy guarantees and computational efficiency.

移动群体感知（MCS）已经成为使用移动设备进行大规模数据收集的一个强大范例。然而，传统的MCS框架带来了重大的隐私风险，特别是在任务执行和支付处理期间工人身份和位置的披露。现有的隐私保护方法，如虚拟位置插入、k-匿名和差分隐私，要么降低效率，要么无法解决所有隐私泄露向量。为了克服这些挑战，我们提出了一个基于零知识证明（ZKP）的区块链框架，在保持系统效率的同时确保强大的隐私保护。我们的协议利用zk- snark在任务提交和支付交易期间保护工人的身份和位置。通过集成以太坊智能合约，我们消除了对集中式众感服务提供商（CSP）的依赖，减轻了单点故障和拒绝服务（DoS）风险。我们为交易隐私提供了形式化的安全证明。通过在Sepolia测试网络上进行详细的实验，我们分析了gas成本和交易完成时间，表明尽管计算复杂，但由于链下证明生成，证明验证仍然是实用的。该框架优化了链上和链下交互，增强了可扩展性，同时保护了用户隐私。与现有框架的比较分析表明，我们的模型具有更好的隐私保证和计算效率。

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引用次数: 0

Blockchain-enabled dynamic formation control and reorganization for intelligent UAV swarms 基于区块链的智能无人机群动态编队控制与重组

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-11-04 DOI: 10.1016/j.pmcj.2025.102129

Huayu Li , Peiyan Li , Wei Zhang , Huiling Shi , Lizhuang Tan , Peiying Zhang

The rapid advancement of unmanned aerial vehicle (UAV) swarm technology has led to growing demands for secure, autonomous, and adaptive coordination mechanisms. However, conventional centralized swarm control systems suffer from single points of failure and limited adaptability in dynamic environments. To address these challenges, this paper presents a blockchain-based decentralized architecture for dynamic formation control and reorganization of intelligent UAV swarms. The proposed system utilizes smart contracts to handle UAV registration, task configuration, data submission, slot allocation, and leader election. An event-driven mechanism is incorporated, allowing UAVs to react in real time to blockchain-triggered events such as leader changes and formation updates. Moreover, UAVs continuously monitor QGroundControl (QGC) heartbeat signals to detect disconnections and autonomously initiate reorganization via on-chain logic. Experimental results demonstrate that the proposed architecture enhances swarm flexibility, reduces control latency, and ensures reliable coordination under dynamic and fault-prone conditions. These findings highlight the potential of blockchain technology in enabling secure and autonomous swarm management within future space-air-ground integrated network.

随着无人机（UAV）群技术的快速发展，对安全、自主和自适应协调机制的需求日益增长。然而，传统的集中式群控制系统在动态环境中存在单点故障和适应性有限的问题。为了解决这些挑战，本文提出了一种基于区块链的分布式架构，用于智能无人机群的动态编队控制和重组。该系统利用智能合约来处理无人机注册、任务配置、数据提交、插槽分配和领导者选举。集成了事件驱动机制，允许无人机实时响应区块链触发的事件，如领导者变化和编队更新。此外，无人机持续监测QGroundControl （QGC）心跳信号，以检测断开并通过链上逻辑自主启动重组。实验结果表明，该架构提高了集群的灵活性，降低了控制延迟，保证了动态和易发故障条件下的可靠协调。这些发现突出了区块链技术在未来空间-空气-地面综合网络中实现安全和自主群管理的潜力。

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引用次数: 0

Listen to the road: acoustic traffic monitoring on edge platforms via Lightweight Noise Spectrogram Transformer (LNST) 聆听道路：通过轻型噪声频谱转换器（LNST）在边缘平台上进行声学交通监测

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-11-06 DOI: 10.1016/j.pmcj.2025.102132

Guowen Li , Zihang Huang , Teng Fei , Dunxin Jia , Meng Bian

Accurate real-time traffic flow monitoring is crucial for intelligent transportation systems (ITS), enabling optimized traffic management, urban planning, and policy-making. However, conventional methods face cost, deployment, weather, and privacy challenges. Addressing these shortcomings, this study investigates the potential of utilizing ubiquitous traffic noise, an inherently accessible, cost-efficient, non-intrusive, and privacy-preserving signal, as a viable data source. We propose the Lightweight Noise Spectrogram Transformer (LNST), a novel deep learning model for analyzing traffic noise spectrograms as a Proof of Concept. LNST leverages the Transformer architecture's self-attention mechanism to effectively capture long-range temporal and spectral dependencies crucial for interpreting complex traffic acoustics. Trained and evaluated on diverse urban traffic scenarios, LNST demonstrates significant advantages. Experimental results show it consistently outperforms baseline models, achieving superior prediction accuracy (MSE, MAE, R²). Furthermore, through transfer learning and model pruning, LNST achieves high computational efficiency with substantially fewer parameters and faster inference speeds. Its lighter design also ensures its feasibility for deployment on resource-constrained edge computing platforms. This work validates the practicality of acoustic sensing for traffic monitoring and presents an accurate, computationally efficient, and LNST as a cost-effective, easily deployable, and privacy-respecting solution, offering a valuable supplementary tool for advancing ITS.

准确的实时交通流量监测对于智能交通系统（ITS）至关重要，可以优化交通管理、城市规划和政策制定。然而，传统方法面临成本、部署、天气和隐私方面的挑战。针对这些缺点，本研究探讨了利用无处不在的交通噪声的潜力，这是一种固有的可访问的、经济高效的、非侵入性的、保护隐私的信号，作为一种可行的数据源。我们提出轻量级噪声频谱转换器（LNST），这是一种用于分析交通噪声频谱的新型深度学习模型，作为概念验证。LNST利用Transformer架构的自关注机制，有效捕获远程时间和频谱依赖关系，这对解释复杂的交通声学至关重要。在不同的城市交通场景中进行训练和评估，LNST显示出显著的优势。实验结果表明，该方法的预测精度优于基线模型（MSE、MAE、R²）。此外，LNST通过迁移学习和模型剪枝，以更少的参数和更快的推理速度实现了更高的计算效率。其更轻的设计也确保了在资源受限的边缘计算平台上部署的可行性。这项工作验证了声传感在交通监控中的实用性，并提出了一种准确的、计算效率高的、LNST作为一种经济、易于部署和尊重隐私的解决方案，为推进ITS提供了一个有价值的补充工具。

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引用次数: 0

Cluster routing algorithm of 4 G wireless communication AD Hoc network based on LoRa transmission technology 基于LoRa传输技术的4g无线通信AD Hoc网络集群路由算法

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-11-01 DOI: 10.1016/j.pmcj.2025.102128

Guofan Sun , Jing Guo , Han Wu , Fei Qi

In complex terrains, underground pipe galleries, and dense building clusters, 4 G wireless communication at power distribution sites often suffers from interruption or delay due to signal obstruction or heavy base station loads. This problem not only hinders real-time data acquisition and emergency response, but also causes load imbalance, reduced throughput, and frequent changes in node states in the clustering structure of ad hoc networks. To address these challenges, this study proposes a clustering routing algorithm for 4 G wireless communication ad hoc networks in power distribution work sites, leveraging LoRa transmission technology. By utilizing the high-speed transmission capability of 4 G networks to handle large-scale real-time data and integrating WaveMesh wireless ad hoc network technology, the proposed solution enables remote control of power distribution field equipment. The IK-means algorithm is employed for clustering, while a clustered network architecture facilitates large-scale networking. Based on received reverse label information, multiple paths are established, and clustering routing for ad hoc networks is achieved using LoRa transmission technology. Experimental results demonstrate that the proposed algorithm generates clusters with strong load balancing, achieving a packet loss rate of 4.5 %, high throughput performance, and the lowest node state change rate, all remaining below 4.

在复杂地形、地下管廊、密集建筑群等环境中，配电点4g无线通信往往会因信号阻塞或基站负荷过大而出现中断或延迟。这个问题不仅阻碍了实时数据采集和应急响应，而且在ad hoc网络的集群结构中造成负载不平衡、吞吐量降低和节点状态频繁变化。为了应对这些挑战，本研究提出了一种利用LoRa传输技术的4g无线通信自组织网络的聚类路由算法。通过利用4g网络的高速传输能力来处理大规模实时数据，并集成WaveMesh无线自组织网络技术，提出的解决方案可以远程控制配电现场设备。聚类采用IK-means算法，聚类网络架构有利于大规模组网。基于接收到的反向标签信息，建立多条路径，采用LoRa传输技术实现ad hoc网络的聚类路由。实验结果表明，该算法生成的集群具有较强的负载均衡性，丢包率为4.5%，吞吐量性能较高，节点状态变化率最低，均保持在4以下。

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引用次数: 0

RTXBEE: Real-time communication module for critical Internet of Things applications RTXBEE：用于关键物联网应用的实时通信模块

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-11-07 DOI: 10.1016/j.pmcj.2025.102133

Valentin Stangaciu , Cristina Stangaciu , Daniel-Ioan Curiac , Mihai V. Micea

The Internet of Things concept has expanded to a large area of applications evolving to the point of providing even real-time support. Critical applications become increasingly suitable at the Edge Layer where real-time operations need to be supported at both node and network level thus communication becomes crucial. This paper presents a real-time communication solution based on the highly popular XBee modules. We describe a predictable and modular driver for such modules along with a full communication platform ready to be integrated into an IoT design for real-time applications. The proposed communication module has been implemented at prototype level and successfully validated through an extensive set of simulations and experiments.

物联网的概念已经扩展到一个大的应用领域，甚至可以提供实时支持。关键应用程序越来越适合边缘层，在边缘层需要在节点和网络级别支持实时操作，因此通信变得至关重要。本文提出了一种基于XBee模块的实时通信解决方案。我们为这些模块描述了一个可预测的模块化驱动程序，以及一个完整的通信平台，可以集成到实时应用的物联网设计中。所提出的通信模块已在原型级实现，并通过大量的仿真和实验成功验证。

引用次数: 0

SecureWearTrade: A comprehensive blockchain-enabled IoT framework for secure personal data trading from wearable devices SecureWearTrade：一个全面的支持区块链的物联网框架，用于可穿戴设备的安全个人数据交易

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-11-04 DOI: 10.1016/j.pmcj.2025.102130

Phat T. Tran-Truong , Trung D. Mai , Ha X. Son , Phien N. Nguyen , Tuan T. Le , Triet M. Nguyen , Khanh H. Vo , Bang K. Le , Ngan T.K. Nguyen , Minh N. Nguyen , Anh T. Nguyen , Tung Q. Nguyen

This article introduces SecureWearTrade, a comprehensive blockchain-enabled IoT framework designed to advance secure personal data trading from wearable devices in healthcare. Addressing critical challenges related to security, privacy, and efficiency in resource-constrained environments, our work makes three key contributions: (1) an enhanced hierarchical identity-based encryption (HIBE) scheme with wildcard support, enabling fine-grained and flexible access control tailored to the dynamic needs of healthcare data management; (2) a novel integration of blockchain with IPFS, providing immutable transaction records and efficient key management; and (3) an optimized batch processing mechanism for effectively handling multiple data streams. By comprehensive evaluation with real-world settings with devices and dataset, SecureWearTrade demonstrates superior performance in encryption and decryption efficiency, resource utilization, and scalability compared to existing solutions. Additionally, the framework maintains robust security under the Bilinear Diffie–Hellman Exponent (BDHE) assumption. By ensuring privacy-preserving data trading, SecureWearTrade offers a scalable and trustworthy solution for the IoT-Cloud continuum.

本文介绍了SecureWearTrade，这是一个全面的支持区块链的物联网框架，旨在推进医疗保健领域可穿戴设备的安全个人数据交易。为了解决资源受限环境中与安全、隐私和效率相关的关键挑战，我们的工作做出了三个关键贡献：(1)增强了具有通配符支持的分层基于身份的加密（HIBE）方案，实现了针对医疗数据管理动态需求量身定制的细粒度和灵活的访问控制；(2)区块链与IPFS的新颖集成，提供不可变的交易记录和高效的密钥管理；(3)用于有效处理多个数据流的优化批处理机制。通过对设备和数据集的实际设置进行综合评估，与现有解决方案相比，SecureWearTrade在加密和解密效率、资源利用率和可扩展性方面表现出卓越的性能。此外，该框架在双线性Diffie-Hellman指数（BDHE）假设下保持了鲁棒安全性。通过确保保护隐私的数据交易，SecureWearTrade为物联网云连续体提供了可扩展且值得信赖的解决方案。

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引用次数: 0

DeSIST: Emergent security in IoT through Decentralized Strategic Interactions — A game-theoretic Zero Trust framework DeSIST：通过分散战略交互的物联网应急安全——博弈论零信任框架

IF 3.5 3区计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS

Pervasive and Mobile Computing

Pub Date : 2026-01-01 Epub Date: 2025-10-28 DOI: 10.1016/j.pmcj.2025.102124

Seyed Hossein Ahmadpanah , Meghdad Mirabi , Sanaz Sobhanloo , Pania Afsharfarnia , Donya Fallah

Numerous Internet of Things (IoT) devices are connecting our world, but they also introduce new security risks—particularly in large, power-constrained networks where traditional security techniques often fail. This paper proposes a new framework for IoT security, called DeSIST (Decentralized Strategic Interaction for Secure IoT), which is grounded in game theory and Zero Trust principles. Unlike approaches that rely on centralized watchdogs or explicit trust scores, DeSIST models interactions between IoT nodes as a sequence of strategic games. Treating nodes as rational agents, each maximizes its own expected utility when making decisions. Security emerges naturally because these games are designed with reward systems that encourage cooperation among trustworthy nodes while strategically isolating malicious or non-compliant actors. DeSIST employs a lightweight Local Information Assessor (LIA) to collect immediate, local, and contextually relevant information about ongoing or anticipated interactions, and a Strategic Decision Unit (SDU) to evaluate possible strategies and select the one that maximizes expected utility. Through theoretical analysis and extensive simulations, we show that DeSIST can decentralize and resource-efficiently uphold Zero Trust principles while significantly enhancing network resilience against common IoT attacks. Compared to existing approaches, the simulation results demonstrate notable improvements in both security and performance across different attack scenarios. DeSIST provides a promising path toward strong, incentive-driven, and emergent security in the evolving IoT landscape.

许多物联网（IoT）设备正在连接我们的世界，但它们也带来了新的安全风险——特别是在传统安全技术经常失效的大型、功率受限的网络中。本文提出了一个基于博弈论和零信任原则的物联网安全新框架，称为DeSIST （Decentralized Strategic Interaction for Secure IoT）。与依赖集中监管机构或明确信任评分的方法不同，DeSIST将物联网节点之间的交互建模为一系列战略博弈。将节点视为理性代理，每个节点在做出决策时都最大化自己的预期效用。安全性自然出现，因为这些游戏设计了奖励系统，鼓励可信节点之间的合作，同时战略性地隔离恶意或不合规的参与者。DeSIST使用轻量级的本地信息评估器（Local Information Assessor， LIA）来收集有关正在进行的或预期的交互的即时、本地和上下文相关的信息，并使用战略决策单元（Strategic Decision Unit， SDU）来评估可能的策略并选择最大化预期效用的策略。通过理论分析和广泛的模拟，我们表明DeSIST可以去中心化和资源高效地维护零信任原则，同时显着增强网络抵御常见物联网攻击的弹性。与现有方法相比，仿真结果表明该方法在不同攻击场景下的安全性和性能都有显著提高。在不断发展的物联网环境中，DeSIST为实现强大、激励驱动和紧急安全提供了一条有希望的道路。

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引用次数: 0