Pervasive and Mobile Computing最新文献_第2页

TrustMD — A multi-layer framework for domain, edge and D2D caching based on trust dissemination and blockchain

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 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.

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引用次数: 0

Enhancing crowdsourcing through skill and willingness-aligned task assignment with workforce composition balance

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 DOI: 10.1016/j.pmcj.2025.102012

Riya Samanta , Soumya K. Ghosh , Sajal K. Das

Crowdsourcing platforms face critical challenges in task assignment and workforce retention, particularly in aligning complex, skill-intensive tasks with crowd-worker willingness and potential while ensuring workforce diversity and balanced composition. This study introduces the Skill-Aligned Task Assignment and Potential-Aware Workforce Composition (SATA-PAW) framework to address these challenges. The proposed framework formulates the Task Assignment with Workforce Composition Balance (TACOMB) problem as a multi-constraint optimization task, aiming to maximize net utility under task budget constraints while promoting balanced workforce composition. SATA-PAW integrates two novel algorithms, Skill-Aligned Task Assignment (SATA), which optimizes task-worker matching by considering skills, willingness, and budget constraints, and Potential-Aware Workforce Composition (PAW), which leverages satisfaction score and latent potential to retain skilled workers and improve workforce diversity. Experimental evaluations on real-world (UpWork) and synthetic datasets demonstrate SATA-PAW’s superiority over five state-of-the-art methods. The results highlight SATA-PAW’s ability to integrate human-centric factors with efficient optimization, setting a new benchmark for skill-oriented task assignment and balanced workforce composition in crowdsourcing systems.

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引用次数: 0

Vspp: Verifiable, shareable, and privacy-preserving access control scheme for IoV

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

Pervasive and Mobile Computing

Pub Date : 2025-02-01 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.

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引用次数: 0

Collective victim counting in post-disaster response: A distributed, power-efficient algorithm via BLE spontaneous networks 灾后响应中的集体受害者计数：一种基于BLE自发网络的分布式节能算法

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

Pervasive and Mobile Computing

Pub Date : 2024-11-28 DOI: 10.1016/j.pmcj.2024.101997

Giacomo Longo , Alessandro Cantelli-Forti , Enrico Russo , Francesco Lupia , Martin Strohmeier , Andrea Pugliese

Accurately determining the number of people affected by emergencies is essential for deploying effective response measures during disasters. Traditional solutions like cellular and Wi-Fi networks are often rendered ineffective during such emergencies due to widespread infrastructure damage or non-functional connectivity, prompting the exploration of more resilient methods. This paper proposes a novel solution utilizing Bluetooth Low Energy (BLE) technology and decentralized networks composed entirely of mobile and wearable devices to count individuals autonomously without reliance on external communication equipment or specialized personnel. This count leverages uncoordinated relayed communication among devices within these networks, enabling us to extend our counting capabilities well beyond the direct range of rescuers. A formally evaluated, experimentally validated, and privacy-preserving counting algorithm that demonstrates rapid convergence and high accuracy even in large-scale scenarios is employed.

准确确定受紧急情况影响的人数对于在灾害期间部署有效的应对措施至关重要。在这种紧急情况下，蜂窝和Wi-Fi网络等传统解决方案往往因基础设施大面积受损或连接功能不佳而失效，这促使人们探索更有弹性的方法。本文提出了一种新颖的解决方案，利用蓝牙低功耗（BLE）技术和完全由移动和可穿戴设备组成的分散网络，在不依赖外部通信设备或专业人员的情况下自主计数个体。这种计数利用了这些网络中设备之间不协调的中继通信，使我们能够将计数能力扩展到救援人员的直接范围之外。采用了一种经过正式评估、实验验证和保护隐私的计数算法，即使在大规模场景中也能显示出快速收敛和高精度。

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引用次数: 0

Three-dimensional spectrum coverage gap map construction in cellular networks: A non-linear estimation approach 蜂窝网络中三维频谱覆盖缺口图的构建：一种非线性估计方法

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

Pervasive and Mobile Computing

Pub Date : 2024-11-23 DOI: 10.1016/j.pmcj.2024.101998

Ahmed Fahim Mostafa , Mohamed Abdel-Kader , Yasser Gadallah

Data collection techniques can be used to determine the coverage conditions of a cellular communication network within a given area. In such tasks, the data acquisition process faces significant challenges for larger or inaccessible locations. Such challenges can be alleviated through the use of unmanned aerial vehicles (UAVs). This way, data acquisition obstacles can be overcome to acquire and process the necessary data points with relative ease to estimate a full area coverage map for the concerned network. In this study, we formulate the problem of deploying a UAV to acquire the minimum possible measurement data points in a geographical region for the purpose of constructing a full communication coverage gap map for this region. We then devise an estimation model that utilizes the measured data samples and determines the noise/loss levels of the communication links at the other unvisited spots of the region accordingly. The proposed estimation model is based on a cascade-forward neural network to allow for both nonlinear and direct linear relationships between the input data and the output estimations. We further investigate the conventional method of using linear regression estimators to decide on the received power levels at the different locations of the examined area. Our simulation evaluations show that the proposed nonlinear estimator outperforms the conventional linear regression technique in terms of the communication coverage error level while using the minimum possible collected data points. These minimum data points are then used in constructing a complete coverage gap map visualization that demonstrates the overall network service conditions within the surveyed region.

数据收集技术可用于确定给定区域内蜂窝通信网的覆盖条件。在这些任务中，对于较大或难以进入的位置，数据采集过程面临重大挑战。这些挑战可以通过使用无人驾驶飞行器（uav）来缓解。这样，可以克服数据获取障碍，相对容易地获取和处理必要的数据点，以估计有关网络的全区域覆盖图。在本研究中，我们提出了部署无人机获取地理区域内尽可能少的测量数据点的问题，目的是构建该区域的全通信覆盖缺口图。然后，我们设计了一个估计模型，该模型利用测量的数据样本，并相应地确定该地区其他未访问点的通信链路的噪声/损耗水平。所提出的估计模型基于级联前向神经网络，允许输入数据和输出估计之间的非线性和直接线性关系。我们进一步研究了使用线性回归估计的传统方法来确定在检查区域不同位置的接收功率水平。我们的模拟评估表明，所提出的非线性估计器在使用尽可能少的收集数据点的情况下，在通信覆盖误差水平方面优于传统的线性回归技术。然后使用这些最小数据点构建完整的覆盖缺口图可视化，显示调查区域内的整体网络服务状况。

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引用次数: 0

Blockchain-Inspired Trust Management in Cognitive Radio Networks with Cooperative Spectrum Sensing 合作频谱感知认知无线电网络中的区块链启发式信任管理

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

Pervasive and Mobile Computing

Pub Date : 2024-11-20 DOI: 10.1016/j.pmcj.2024.101999

Mahsa Mahvash , Neda Moghim , Mojtaba Mahdavi , Mahdieh Amiri , Sachin Shetty

Cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs) enhances spectral decision-making precision but introduces vulnerabilities to malicious secondary user (SU) attacks. This paper proposes a decentralized trust and reputation management (TRM) framework to address these vulnerabilities, emphasizing the need to mitigate risks associated with centralized systems. Inspired by blockchain technology, we present a distributed TRM method for CSS in CRNs, significantly reducing the impact of malicious attacks. Our approach leverages a Proof of Trust (PoT) system to enhance the integrity of CSS, thereby improving the accuracy of spectral decision-making while reducing false positives and false negatives. In this system, SUs’ trust scores are dynamically updated based on their sensing reports, and they will collaboratively participate in new blocks' formation using the trust scores. Simulation results validate the effectiveness of the proposed method, indicating its potential to enhance security and reliability in CRNs.

认知无线电网络（CRN）中的合作频谱感知（CSS）提高了频谱决策的精确度，但也带来了受到恶意次级用户（SU）攻击的漏洞。本文提出了一种去中心化的信任和声誉管理（TRM）框架来解决这些漏洞，强调需要降低与中心化系统相关的风险。受区块链技术的启发，我们提出了一种适用于 CRN 中 CSS 的分布式 TRM 方法，大大降低了恶意攻击的影响。我们的方法利用信任证明（PoT）系统来增强 CSS 的完整性，从而提高频谱决策的准确性，同时减少误报和误报。在该系统中，SU 的信任分数会根据其感知报告动态更新，它们将利用信任分数协同参与新区块的形成。仿真结果验证了所提方法的有效性，表明该方法具有提高 CRN 安全性和可靠性的潜力。

引用次数: 0

Delay-aware resource allocation for partial computation offloading in mobile edge cloud computing 移动边缘云计算中部分计算卸载的延迟感知资源分配

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

Pervasive and Mobile Computing

Pub Date : 2024-11-07 DOI: 10.1016/j.pmcj.2024.101996

Lingfei Yu , Hongliu Xu , Yunhao Zeng , Jiali Deng

Mobile Edge Cloud Computing (MECC), as a promising partial computing offloading solution, has provided new possibilities for compute-intensive and delay-sensitive mobile applications, which can simultaneously leverage edge computing and cloud services. However, designing resource allocation strategies for MECC faces an extremely challenging problem of simultaneously satisfying the end-to-end latency requirements and minimum resource allocation of multiple mobile applications. To address this issue, we comprehensively consider the randomness of computing request arrivals, service time, and dynamic computing resources. We model the MECC network as a two-level tandem queue consisting of two sequential computing processing queues, each with multiple servers. We apply a deep reinforcement learning algorithm called Deep Deterministic Policy Gradient (DDPG) to learn the computing speed adjustment strategy for the tandem queue. This strategy ensures the end-to-end latency requirements of multiple mobile applications while preventing overuse of the total computing resources of edge servers and cloud servers. Numerous simulation experiments demonstrate that our approach is significantly superior to other methods in dynamic network environments.

移动边缘云计算（MECC）作为一种前景广阔的部分计算卸载解决方案，为计算密集型和延迟敏感型移动应用提供了新的可能性，这些应用可以同时利用边缘计算和云服务。然而，为 MECC 设计资源分配策略面临着一个极具挑战性的问题，即同时满足端到端延迟要求和多个移动应用的最小资源分配。为了解决这个问题，我们全面考虑了计算请求到达的随机性、服务时间和动态计算资源。我们将 MECC 网络建模为一个两级串联队列，由两个顺序计算处理队列组成，每个队列有多个服务器。我们采用一种名为深度确定性策略梯度（DDPG）的深度强化学习算法来学习串联队列的计算速度调整策略。该策略既能确保多个移动应用的端到端延迟要求，又能防止过度使用边缘服务器和云服务器的总计算资源。大量模拟实验证明，在动态网络环境中，我们的方法明显优于其他方法。

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引用次数: 0

Minimum data sampling requirements for accurate detection of terrain-induced gait alterations change with mobile sensor position 准确检测地形引起的步态变化所需的最低数据采样要求随移动传感器位置而变化

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

Pervasive and Mobile Computing

Pub Date : 2024-10-19 DOI: 10.1016/j.pmcj.2024.101994

Arshad Sher , Otar Akanyeti

Human gait is a key biomarker for health, independence and quality of life. Advances in wearable inertial sensor technologies have paved the way for out-of-the-lab human gait analysis, which is important for the assessment of mobility and balance in natural environments and has applications in multiple fields from healthcare to urban planning. Automatic recognition of the environment where walking takes place is a prerequisite for successful characterisation of terrain-induced gait alterations. A key question which remains unexplored in the field is how minimum data requirements for high terrain classification accuracy change depending on the sensor placement on the body. To address this question, we evaluate the changes in performance of five canonical machine learning classifiers by varying several data sampling parameters including sampling rate, segment length, and sensor configuration. Our analysis on two independent datasets clearly demonstrate that a single inertial measurement unit is sufficient to recognise terrain-induced gait alterations, accuracy and minimum data requirements vary with the device position on the body, and choosing correct data sampling parameters for each position can improve classification accuracy up to 40% or reduce data size by 16 times. Our findings highlight the need for adaptive data collection and processing algorithms for resource-efficient computing on mobile devices.

人类步态是健康、独立性和生活质量的关键生物标志。可穿戴惯性传感器技术的进步为实验室外的人类步态分析铺平了道路，这对于评估自然环境中的移动性和平衡性非常重要，在医疗保健和城市规划等多个领域都有应用。自动识别行走环境是成功描述地形引起的步态变化的先决条件。该领域尚未探索的一个关键问题是，高地形分类准确性所需的最低数据要求如何随传感器在身体上的位置而变化。为了解决这个问题，我们通过改变数据采样参数（包括采样率、片段长度和传感器配置）来评估五种典型机器学习分类器的性能变化。我们对两个独立数据集的分析清楚地表明，单个惯性测量单元足以识别地形引起的步态变化，准确性和最低数据要求随设备在身体上的位置而变化，为每个位置选择正确的数据采样参数可将分类准确性提高 40%，或将数据量减少 16 倍。我们的研究结果凸显了在移动设备上采用自适应数据收集和处理算法以实现资源节约型计算的必要性。

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引用次数: 0

An energy-aware secure routing scheme in internet of things networks via two-way trust evaluation 通过双向信任评估实现物联网网络中的能量感知安全路由方案

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

Pervasive and Mobile Computing

Pub Date : 2024-10-16 DOI: 10.1016/j.pmcj.2024.101995

Tingxuan Fu , Sijia Hao , Qiming Chen , Zihan Yan , Huawei Liu , Amin Rezaeipanah

The rapid advancement of technology has led to the proliferation of devices connected to the Internet of Things (IoT) networks, bringing forth challenges in both energy management and secure data communication. In addition to energy constraints, IoT networks face threats from malicious nodes, which jeopardize the security of communications. To address these challenges, we propose an Energy-aware secure Routing scheme via Two-Way Trust evaluation (ERTWT) for IoT networks. This scheme enhances network protection against various attacks by calculating trust values based on energy trust, direct trust, and indirect trust. The scheme aims to enhance the efficiency of data transmission by dynamically selecting routes based on both energy availability and trustworthiness metrics of fog nodes. Since trust management can guarantee privacy and security, ERTWT allows the service requester and the service provider to check each other's safety and reliability at the same time. In addition, we implement Generative Flow Networks (GFlowNets) to predict the energy levels available in nodes in order to use them optimally. The proposed scheme has been compared with several advanced energy-aware and trust-based routing protocols. Evaluation results show that ERTWT more effectively detects malicious nodes while achieving better energy efficiency and data transmission rates.

技术的飞速发展导致连接到物联网（IoT）网络的设备激增，给能源管理和安全数据通信都带来了挑战。除了能源限制，物联网网络还面临着恶意节点的威胁，从而危及通信安全。为了应对这些挑战，我们为物联网网络提出了一种通过双向信任评估（ERTWT）的能量感知安全路由方案。该方案通过计算基于能量信任、直接信任和间接信任的信任值，增强网络对各种攻击的防护能力。该方案旨在根据雾节点的能量可用性和可信度指标动态选择路由，从而提高数据传输效率。由于信任管理可以保证隐私和安全，ERTWT 允许服务请求者和服务提供者同时检查对方的安全性和可靠性。此外，我们还采用了生成流网络（GFlowNets）来预测节点的可用能量水平，以便优化使用。我们将所提出的方案与几种先进的能量感知路由协议和基于信任的路由协议进行了比较。评估结果表明，ERTWT 能更有效地检测恶意节点，同时实现更高的能效和数据传输速率。

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引用次数: 0

Trust-aware and improved density peaks clustering algorithm for fast and secure models in wireless sensor networks 面向无线传感器网络快速安全模型的信任感知和改进密度峰聚类算法

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

Pervasive and Mobile Computing

Pub Date : 2024-10-10 DOI: 10.1016/j.pmcj.2024.101993

Youjia Han, Huibin Wang, Yueheng Li, Lili Zhang

Many trust-based models for wireless sensor networks do not account for trust attacks, which are destructive phenomena that undermine the security and reliability of these models. Therefore, a trust-based fast security model fused with an improved density peaks clustering algorithm (TFSM-DPC) is proposed to quickly identify trust attacks in this paper. First, when calculating direct trust values, TFSM-DPC designs the adaptive penalty factors based on the state of received and sent packets and behaviors, and introduces the volatilization factors to reduce the effect of historical trust values. Second, TFSM-DPC improved density peaks clustering (DPC) algorithm to evaluate the trustworthiness of each recommendation value, thus filtering malicious recommendations before calculating the indirect trust values. Moreover, to filter two types of recommendations, the improved DPC algorithm incorporates artificial benchmark data along with trust values recommended by neighbors as input data. Finally, based on the relationship between direct trust and indirect trust, a secure formula for calculate the comprehensive trust is designed. Therefore, the proposed TFSM-DPC can improve the accuracy of trust evaluation and speed up the identification of malicious nodes. Simulation results show that TFSM-DPC can effectively identify on-off, bad-mouth and collusion attacks, and improve the speed of excluding malicious nodes from the network, compared to other trust-based algorithms.

许多基于信任的无线传感器网络模型都没有考虑到信任攻击，而信任攻击是一种破坏性现象，会损害这些模型的安全性和可靠性。因此，本文提出了一种与改进密度峰聚类算法（TFSM-DPC）相融合的基于信任的快速安全模型，以快速识别信任攻击。首先，在计算直接信任值时，TFSM-DPC 根据接收和发送数据包的状态和行为设计自适应惩罚因子，并引入波动因子以降低历史信任值的影响。其次，TFSM-DPC 改进了密度峰聚类（DPC）算法，以评估每个推荐值的可信度，从而在计算间接信任值之前过滤恶意推荐。此外，为了过滤两类推荐，改进后的 DPC 算法将人工基准数据和邻居推荐的信任值作为输入数据。最后，根据直接信任和间接信任之间的关系，设计了计算综合信任的安全公式。因此，所提出的 TFSM-DPC 可以提高信任评估的准确性，加快识别恶意节点的速度。仿真结果表明，与其他基于信任的算法相比，TFSM-DPC 能有效识别 on-off、bad-mouth 和 collusion 攻击，并提高从网络中排除恶意节点的速度。

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引用次数: 0