Pervasive and Mobile Computing最新文献_第5页

Efficient community detection in disaster networks using spectral sparsification 基于频谱稀疏的灾害网络社区检测

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

Pervasive and Mobile Computing

Pub Date : 2025-08-19 DOI: 10.1016/j.pmcj.2025.102106

Annalisa Socievole, Clara Pizzuti

Community detection plays a critical role in disaster recovery and pervasive computing, where identifying cohesive social groups enables more effective communication, coordination, and resource allocation. In mobile and resource-constrained environments such as emergency response systems or mobile opportunistic networks, community detection methods must balance accuracy with computational efficiency. In this work, we propose a novel approach that uncovers community structures from a sparse representation of the original graph, addressing the need for lightweight and scalable algorithms in pervasive and mobile systems. Specifically, we apply Spielman–Srivastava spectral sparsification as a preprocessing step to reduce the number of edges while preserving the key spectral properties that underpin community structure. We then apply a modularity-optimizing genetic algorithm on the sparsified graph. Our experiments, conducted on both synthetic benchmarks and real-world networks, demonstrate that the proposed method, namely SSGA, achieves competitive or superior accuracy compared to state-of-the-art baselines, even under aggressive sparsification. We also analyze the cumulative computational complexity of the approach and provide an optimized implementation based on truncated spectral decomposition and parallel genetic operations. The results confirm that SSGA is not only accurate and robust but also computationally efficient, making it particularly well-suited for pervasive and mobile scenarios where time, energy, and connectivity are limited.

社区检测在灾难恢复和普适计算中起着关键作用，在这些领域中，识别有凝聚力的社会群体可以实现更有效的通信、协调和资源分配。在移动和资源受限的环境中，如应急响应系统或移动机会网络，社区检测方法必须平衡准确性和计算效率。在这项工作中，我们提出了一种新的方法，从原始图的稀疏表示中揭示社区结构，解决了普适和移动系统中对轻量级和可扩展算法的需求。具体来说，我们采用Spielman-Srivastava光谱稀疏作为预处理步骤，以减少边缘数量，同时保留支撑群落结构的关键光谱属性。然后在稀疏化图上应用模块化优化遗传算法。我们在合成基准和现实世界网络上进行的实验表明，即使在积极的稀疏化下，与最先进的基线相比，所提出的方法（即SSGA）也具有竞争力或更高的准确性。我们还分析了该方法的累积计算复杂度，并提供了基于截断谱分解和并行遗传操作的优化实现。结果证实，SSGA不仅准确、稳健，而且计算效率高，特别适合时间、能量和连接有限的普及和移动场景。

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引用次数: 0

Position claim verification for emergency message propagation in Vehicular Ad-Hoc Networks 车载自组织网络中紧急信息传播的位置声明验证

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

Pervasive and Mobile Computing

Pub Date : 2025-08-17 DOI: 10.1016/j.pmcj.2025.102107

Armir Bujari , Mirko Franco , Claudio E. Palazzi , Davide Quaglio , Anna Maria Vegni

Pervasive and mobile computing can play a crucial role in the prevention, detection and management of natural and human-caused disasters. In this context, the Internet of Vehicles (IoV) is particularly noteworthy due to its recent technological advancements and increasing prevalence. In fact, IoV can be leveraged to improve various applications, including those aimed at reducing the millions of fatalities that occur every year. The effectiveness of these applications often relies on the rapid dissemination of emergency messages through position-based forwarding protocols, which can unfortunately be vulnerable to adversarial attacks. Without loss of generality, we focus on the specific case study of road safety to provide a realistic example and discuss two potential attacks based on fake position claims that malicious nodes could easily execute to compromise the performance of the position-based forwarding protocol. We also propose and analyze a validation system based on machine learning (ML) techniques designed to detect malicious nodes, discard false information, and protect against these attacks.

普及和移动计算可以在预防、发现和管理自然灾害和人为灾害方面发挥关键作用。在这种情况下，由于其最近的技术进步和日益普及，车联网（IoV）尤其值得注意。事实上，车联网可以用来改善各种应用，包括那些旨在减少每年数百万人死亡的应用。这些应用的有效性往往依赖于通过基于位置的转发协议快速传播紧急信息，不幸的是，这种转发协议很容易受到对抗性攻击。在不失去一般性的前提下，我们将重点放在道路安全的具体案例研究上，提供一个现实的例子，并讨论两种基于虚假位置声明的潜在攻击，恶意节点可以很容易地执行这些攻击来损害基于位置的转发协议的性能。我们还提出并分析了一个基于机器学习（ML）技术的验证系统，该系统旨在检测恶意节点，丢弃虚假信息并防范这些攻击。

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引用次数: 0

An energy-efficient IoMT three-tier architecture for continuous monitoring of endangered bird species 一种节能的IoMT三层结构，用于濒危鸟类物种的持续监测

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

Pervasive and Mobile Computing

Pub Date : 2025-07-24 DOI: 10.1016/j.pmcj.2025.102093

Aya Sakhri , Moufida Maimour , Noureddine Doghmane , Eric Rondeau , Saliha Harize

The alarming decline in animal populations, particularly birds, due to environmental degradation necessitates close monitoring of endangered migratory waterbirds in their natural habitats. This can be accomplished through the continuous capture and transmission for population estimation, habitat analysis, and various relevant studies. This paper introduces a three-tier IoMT (Internet of Multimedia Things) deployed along the Edge-Cloud continuum for automated bird monitoring systems aimed at safeguarding endangered waterbird populations. At the edge level, Wireless Multimedia Sensor Networks (WMSN) are used to periodically capture and transmit images to a central collection station (fog level). Challenges such as limited bandwidth and power in Low-Power and Lossy Networks (LLNs) are addressed through local audio identification of endangered bird calls, which activates cameras only for target birds. This significantly reduces data transmission and conserves energy. To tackle ambient noise issues in audio recognition, especially in complex environments such as wetlands, an appropriate noise reduction technique is employed to augment our automatic bird call recognition system. This paper details an energy-efficient approach addressing LLNs’ challenges and incorporates robust noise reduction techniques to improve local audio recognition. The research includes a thorough analysis of potential technical solutions prior to implementation, establishing a critical phase in the system development.

由于环境退化，动物种群，特别是鸟类的数量急剧下降，因此有必要密切监测濒危迁徙水鸟在其自然栖息地的情况。这可以通过持续捕获和传输来实现，用于种群估计、栖息地分析和各种相关研究。本文介绍了一种沿边缘云连续体部署的三层多媒体物联网（IoMT），用于鸟类自动监测系统，旨在保护濒危水鸟种群。在边缘级，无线多媒体传感器网络（WMSN）用于周期性地捕获图像并将其传输到中央采集站（雾级）。在低功耗和有损网络（lln）中，带宽和功率有限的挑战是通过本地音频识别濒危鸟类的叫声来解决的，这只会激活目标鸟类的摄像机。这大大减少了数据传输，节约了能源。为了解决音频识别中的环境噪声问题，特别是在湿地等复杂环境中，我们采用了一种适当的降噪技术来增强我们的自动鸟叫声识别系统。本文详细介绍了解决lln挑战的节能方法，并结合了强大的降噪技术来提高本地音频识别。该研究包括在实施之前对潜在的技术解决方案进行彻底的分析，建立系统开发的关键阶段。

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引用次数: 0

A federated learning-based selection and incentive system using blockchain technology 基于区块链技术的联邦学习选择与激励系统

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

Pervasive and Mobile Computing

Pub Date : 2025-07-18 DOI: 10.1016/j.pmcj.2025.102091

Yang Han , Tasiu Muazu , Omaji Samuel , Shiyu Miao

Machine learning algorithms are powerful tools for analyzing data with several observations approximately equal to the number of predictors. However, the privacy of data owners may be revealed during the processes of analysis and mining in a distributed scenario. Today, federated learning is employed as the best paradigm for collaborative model training without disclosing the privacy of the data owners. Unfortunately, efficient client selection and incentive mechanisms need to provide for encouraging data sharing and analysis for constrained and non-constrained devices. Furthermore, trust in the system must be considered. To this end, this study proposes a federated blockchain-based incentive and selection mechanism for a federated learning system. Clients are selected using support vector machines (SVM), while the accuracy of SVM is improved by recursive feature elimination (RFE). A real-time incentive is provided to clients for collaborative learning using deep Q reinforcement learning, and an optimal incentive allocation policy is derived using the Markov decision process (MDP) framework. For miners’ selection, a proof of utility consensus is proposed using a sixteen-round addition game. Extensive simulations are conducted to evaluate the efficiency of the proposed system model. The performance of the proposed system is determined by its optimal statistical utility, system utility, and client utility, respectively. From the experimental results, the proposed SVM-RFE model outperform the existing algorithms. Additionally, security analysis is performed, which shows that the proposed system is safe against background knowledge attacks.

机器学习算法是强大的工具，用于分析具有近似等于预测器数量的多个观察值的数据。然而，在分布式场景的分析和挖掘过程中，数据所有者的隐私可能会暴露出来。今天，联邦学习被用作协作模型训练的最佳范例，而不会泄露数据所有者的隐私。不幸的是，需要提供有效的客户选择和激励机制，以鼓励对受限和非受限设备进行数据共享和分析。此外，还必须考虑对系统的信任。为此，本研究提出了一种基于联邦区块链的联邦学习系统激励与选择机制。使用支持向量机（SVM）选择客户端，并通过递归特征消除（RFE）提高支持向量机的精度。利用深度Q强化学习为客户协同学习提供实时激励，并利用马尔可夫决策过程（MDP）框架推导出最优激励分配策略。对于矿工的选择，使用16轮加法博弈提出效用共识证明。进行了大量的仿真来评估所提出的系统模型的效率。所建议系统的性能分别由其最优统计效用、系统效用和客户效用决定。实验结果表明，本文提出的SVM-RFE模型优于现有算法。此外，还进行了安全性分析，表明该系统对后台知识攻击是安全的。

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引用次数: 0

Enhanced hybrid prototype for few-shot class-incremental gait recognition in multi-activity scenarios using wearable sensors 基于可穿戴传感器的多活动场景下多镜头类增量步态识别的增强混合原型

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

Pervasive and Mobile Computing

Pub Date : 2025-07-16 DOI: 10.1016/j.pmcj.2025.102092

Chao Lin, Zhanyong Mei, Linlong Mao, Zijie Mei

Wearable devices for gait information sensing provide a reliable and robust solution for identity recognition. However, in real-world applications, gait recognition systems based on these sensing devices should adapt to diverse walking activities, tackle the challenge of limited individual data, and continuously update to recognize both old and new users. In this study, we propose a framework based on hybrid prototype enhancement to address the challenge of few-shot class-incremental gait recognition in multi-activity scenarios (FC-GRMA). Firstly, hybrid prototypes are generated by introducing auxiliary activity labels, which are more generalizable than ordinary prototypes; secondly, the prototypes are adjusted by a selective prototype enhancement module, which improves the representative and discriminative abilities of the prototypes. Finally, validation on the public dataset USC-HAD and the self-built dataset CDUT-AG shows that our proposed framework performs best in solving the FC-GRMA problem. In particular, we also discuss the effect of different numbers of activities on the model performance, and the results show that our framework effectively addresses the issue of catastrophic forgetting in multi-activity scenarios. The source code is available at https://github.com/lc321/fc-grma.git.

步态信息传感可穿戴设备为身份识别提供了可靠、鲁棒的解决方案。然而，在现实应用中，基于这些传感设备的步态识别系统应该适应不同的步行活动，解决个人数据有限的挑战，并不断更新以识别新老用户。在这项研究中，我们提出了一个基于混合原型增强的框架来解决多活动场景下的少镜头类增量步态识别（FC-GRMA）的挑战。首先，通过引入辅助活动标签生成混合原型，使其具有比普通原型更强的泛化性；其次，通过选择性原型增强模块对原型进行调整，提高了原型的代表性和判别能力；最后，在公共数据集USC-HAD和自建数据集ctut - ag上的验证表明，我们提出的框架在解决FC-GRMA问题上表现最好。特别地，我们还讨论了不同活动数量对模型性能的影响，结果表明我们的框架有效地解决了多活动场景下的灾难性遗忘问题。源代码可从https://github.com/lc321/fc-grma.git获得。

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引用次数: 0

Edge AIoT-based agricultural recommendation platform to improve humus productivity in vermicomposting processes 基于边缘物联网的农业推荐平台，提高蚯蚓堆肥过程中腐殖质的生产力

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

Pervasive and Mobile Computing

Pub Date : 2025-06-23 DOI: 10.1016/j.pmcj.2025.102080

Juan M. Núñez V., Sebastián López Flórez, Juan M. Corchado, Fernando De la Prieta

Climate change represents a critical threat to global food security, affecting agricultural production and exacerbating the food crisis projected by the FAO for 2050. Soil recovery and the adoption of sustainable agricultural practices, such as organic farming, are essential to address this challenge. Smart organic farming improves soil quality, crop productivity, and water retention capacity. In this context, vermiculture, which utilizes Eisenia Foetida (red worms), plays a fundamental role. This article highlights how humus production through vermiculture has been significantly optimized through an Edge AIoT platform that integrates an agricultural recommendation system based on bio-inspired algorithms, an LSTM network for predicting humus and worm populations, and a control system to regulate variables such as temperature, humidity, and pH. The results show an increase in humus production from 37.58% to 87.88% and in the worm population from 35.5% to 83%. Vermicompost, obtained through the non-thermophilic biodegradation of organic waste by worms, acts as a crucial biofertilizer that sustainably increases crop yields and helps farmers adapt to environmental stresses, contributing to the Sustainable Development Goals (SDGs). Finally, seven experiments were conducted in which the Edge AIoT-based agricultural recommendation platform optimized the vermicomposting process, improving efficiency and productivity in humus production. This technological approach not only mitigates the impact of climate change but also supports the recovery of degraded soils and promotes sustainable agricultural practices essential for ensuring future food security.

气候变化对全球粮食安全构成严重威胁，影响农业生产，加剧粮农组织预测的2050年粮食危机。土壤恢复和采用可持续农业做法，如有机农业，对于应对这一挑战至关重要。智能有机农业提高了土壤质量、作物生产力和保水能力。在这种情况下，利用红虫（Eisenia Foetida）的蚯蚓养殖发挥了根本作用。本文重点介绍了如何通过Edge AIoT平台显著优化蚯蚓养殖的腐殖质生产，该平台集成了基于生物启发算法的农业推荐系统、用于预测腐殖质和蠕虫种群的LSTM网络以及调节温度、湿度和ph等变量的控制系统。结果显示腐殖质产量从37.58%增加到87.88%，蠕虫种群从35.5%增加到83%。蚯蚓堆肥是蠕虫通过对有机废物进行非嗜热性生物降解而获得的，是一种重要的生物肥料，可持续提高作物产量，帮助农民适应环境压力，为实现可持续发展目标做出贡献。最后，进行了7项实验，通过基于Edge ai的农业推荐平台优化了蚯蚓堆肥过程，提高了腐殖质生产的效率和生产率。这种技术方法不仅减轻了气候变化的影响，而且还支持退化土壤的恢复，促进对确保未来粮食安全至关重要的可持续农业做法。

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引用次数: 0

Efficiently linking LoRaWAN identifiers through multi-domain fingerprinting 通过多域指纹识别高效链接LoRaWAN标识符

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

Pervasive and Mobile Computing

Pub Date : 2025-06-16 DOI: 10.1016/j.pmcj.2025.102082

Samuel Pélissier , Abhishek Kumar Mishra , Mathieu Cunche , Vincent Roca , Didier Donsez

LoRaWAN is a leading IoT technology worldwide, increasingly integrated into pervasive computing environments through a growing number of sensors in various industrial and consumer applications. Although its security vulnerabilities have been extensively explored in the recent literature, its ties to human activities warrant further privacy research. Existing device identification and activity inference attacks are only effective with a stable identifier. We find that the identifiers in LoRaWAN exhibit high variability, and more than half of the devices use them for less than a week. For the first time in the literature, we explore the feasibility of device fingerprinting in LoRaWAN, allowing long-term device linkage, i.e. associating various identifiers of the same device. We introduce a novel holistic fingerprint representation utilizing multiple domains, namely content, timing, and radio information, and present a machine learning-based solution for linking identifiers. Through a large-scale experimental evaluation based on real-world datasets containing up to 41 million messages, we study multiple scenarios, including an attacker with limited resources. We reach 0.98 linkage accuracy, underscoring the need for privacy-preserving measures. We showcase countermeasures including payload padding, random delays, and radio signal modulation, and conclude by assessing their impact on our fingerprinting solution.

LoRaWAN是全球领先的物联网技术，通过各种工业和消费应用中越来越多的传感器，越来越多地集成到普摄计算环境中。尽管它的安全漏洞在最近的文献中已经被广泛探讨，但它与人类活动的关系需要进一步的隐私研究。现有的设备识别和活动推断攻击只有在稳定的标识符下才有效。我们发现，LoRaWAN中的标识符表现出高度的可变性，超过一半的设备使用不到一周。在文献中，我们首次探索了LoRaWAN中设备指纹识别的可行性，允许长期设备联动，即将同一设备的各种标识符关联起来。我们引入了一种利用多域（即内容、时间和无线电信息）的全新整体指纹表示，并提出了一种基于机器学习的链接标识符解决方案。通过基于包含多达4100万条消息的真实世界数据集的大规模实验评估，我们研究了多种场景，包括资源有限的攻击者。我们达到了0.98的链接精度，强调了隐私保护措施的必要性。我们展示了包括有效载荷填充、随机延迟和无线电信号调制在内的对策，并通过评估它们对指纹识别解决方案的影响来得出结论。

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引用次数: 0

Digital twin-enabled age of information-aware scheduling for Industrial IoT edge networks 工业物联网边缘网络的信息感知调度数字孪生时代

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

Pervasive and Mobile Computing

Pub Date : 2025-06-16 DOI: 10.1016/j.pmcj.2025.102083

Elif Bozkaya-Aras

Mobile Edge Computing (MEC) is a significant technology employed in the development of the Industrial Internet of Things (IIoT) as it allows the collection and processing of high volumes of data at the network edge to support industrial processes and improve operational efficiency and productivity. However, despite significant advances in MEC capabilities, the stringent latency requirement that may occur in computation-intensive tasks may affect the freshness of status information. Therefore, there are practical challenges in scheduling the tasks associated with computational efficiency in local computation and remote computation. In this context, we propose an Age of Information (AoI)-based scheduler to determine where to execute computational tasks in order to continuously track state data updates, where the AoI metric measures the time elapsed from the generation of the computation task at the source to the latest received update at the destination. The contributions of this paper are threefold: First, we propose a digital twin-enabled AoI-based scheduler model that collects real-time data from IIoT nodes and predicts the best task assignment in terms of local computation and remote computation. The digital twin environment allows monitoring of the state changes of the real physical assets over time and optimizes the scheduling strategy. Second, we formulate the average AoI problem with the M/M/1 queueing model and propose a genetic algorithm-based scheduler to minimize AoI and task completion time to efficiently schedule the computation tasks between IIoT devices and MEC servers. Third, we compare the performance of our digital twin-enabled model with the traditional strategies and make a significant contribution to IIoT edge network management by analyzing AoI, task completion time and MEC server utilization.

移动边缘计算（MEC）是工业物联网（IIoT）发展中采用的一项重要技术，因为它允许在网络边缘收集和处理大量数据，以支持工业流程并提高运营效率和生产力。然而，尽管MEC功能取得了重大进展，但在计算密集型任务中可能出现的严格延迟需求可能会影响状态信息的新鲜度。因此，在本地计算和远程计算中，与计算效率相关的任务调度存在着实际的挑战。在这种情况下，我们提出了一个基于信息时代（AoI）的调度器，以确定在何处执行计算任务，以便连续跟踪状态数据更新，其中AoI度量度量从源处的计算任务生成到目标处最新接收到的更新所经过的时间。本文的贡献有三个方面：首先，我们提出了一个基于数字双机的基于aoi的调度器模型，该模型从IIoT节点收集实时数据，并根据本地计算和远程计算预测最佳任务分配。数字孪生环境允许监控实际物理资产随时间的状态变化，并优化调度策略。其次，我们用M/M/1队列模型构造了平均AoI问题，并提出了一种基于遗传算法的调度程序来最小化AoI和任务完成时间，从而有效地调度IIoT设备和MEC服务器之间的计算任务。第三，我们比较了我们的数字孪生模型与传统策略的性能，并通过分析AoI，任务完成时间和MEC服务器利用率，为工业物联网边缘网络管理做出了重大贡献。

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引用次数: 0

An optimized Multi Agent Reinforcement Learning solution for edge caching in the Internet of Vehicles 一种针对车联网边缘缓存的优化多智能体强化学习解决方案

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

Pervasive and Mobile Computing

Pub Date : 2025-06-13 DOI: 10.1016/j.pmcj.2025.102081

Mohamed Amine Ghamri, Badis Djamaa, Mohamed Akrem Benatia, Redouane Bellahmer

The Internet of Vehicles has evolved significantly with the integration of intelligent technologies, transforming vehicular networks by enhancing communication, resource management, and decision-making at the network’s edge. With the increasing complexity of vehicular environments and data demands, efficient caching mechanisms have become essential to ensure seamless service delivery and optimized resource usage. In this paper, we present LF-MARLEC, a Leader Follower Multi-Agent Reinforcement Learning solution for Edge Caching within the Internet of Vehicles. Our approach introduces a hierarchical distribution of action importance, enabling more effective decision-making at the network edge. Extensive experiments, conducted using widely adopted simulation tools such as SUMO and Veins, demonstrate that our approach substantially enhances caching performance and overall system efficiency. Specifically, our approach achieves nearly 9% reduction in content distribution delay and over 11% improvement in cache hit rate compared to state-of-the-art methods, thereby enhancing the effectiveness of intelligent edge caching in Internet of Vehicles environments. The source code is publicly available at: https://github.com/amine9008/RL-EDGE-CACHING.

随着智能技术的融合，车联网已经发生了重大变化，通过增强网络边缘的通信、资源管理和决策，改变了汽车网络。随着车辆环境和数据需求的日益复杂，高效的缓存机制已成为确保无缝服务交付和优化资源使用的必要条件。在本文中，我们提出了LF-MARLEC，一种用于车辆互联网边缘缓存的领导跟随多智能体强化学习解决方案。我们的方法引入了行动重要性的分层分布，从而在网络边缘实现更有效的决策。使用广泛采用的仿真工具（如SUMO和vein）进行的大量实验表明，我们的方法大大提高了缓存性能和整体系统效率。具体来说，与最先进的方法相比，我们的方法使内容分发延迟减少了近9%，缓存命中率提高了11%以上，从而提高了智能边缘缓存在车联网环境中的有效性。源代码可以在：https://github.com/amine9008/RL-EDGE-CACHING上公开获得。

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引用次数: 0

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

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

Pervasive and Mobile Computing

Pub Date : 2025-06-09 DOI: 10.1016/j.pmcj.2025.102078

Mahdi Nikooghadam, Hamid Reza Shahriari

The concept of crowdsourcing uses shared intelligence to solve complex tasks through group collaboration. Crowdsourcing involves gathering information and opinions from participants who submit their data, or solutions, over the Internet using a specific program. Given that the communication environment for crowdsourcing platforms is the Internet, there is a significant opportunity for attackers to compromise the confidentiality and integrity of information and violate participants’ privacy. Despite the great benefits of crowdsourcing, concerns about security and privacy are growing and require attention. Unfortunately based on our knowledge, the schemes presented to preserve security and privacy in crowdsourcing are susceptible to security and privacy attack and have a high computational and communication overhead. Therefore, they are not appropriate for crowdsourcing environments. This paper presents an ultra-lightweight authentication and key establishment protocol based on hash functions. This protocol meets all security requirements, is invulnerable to known attacks, and imposes a very low network overhead. The security of the proposed scheme has been formally proved, depicting the resistance of the proposed scheme to different types of possible attacks. In addition, the robustness of the proposed scheme against potential attacks has been proven through Scyther’s automatic software validation tool. The performance evaluation ultimately demonstrated that the proposed protocol incurs significantly reduced computational and communication costs compared to previous schemes and is very suitable for the crowdsourcing environment.

众包的概念是利用共享的智慧，通过团队协作来解决复杂的任务。众包包括从参与者那里收集信息和意见，这些参与者通过特定的程序在互联网上提交他们的数据或解决方案。鉴于众包平台的通信环境是互联网，攻击者有很大的机会破坏信息的保密性和完整性，侵犯参与者的隐私。尽管众包带来了巨大的好处，但对安全和隐私的担忧也在增加，需要引起人们的关注。不幸的是，根据我们的知识，在众包中提出的保护安全和隐私的方案容易受到安全和隐私攻击，并且具有很高的计算和通信开销。因此，它们不适合众包环境。提出了一种基于哈希函数的超轻量级认证和密钥建立协议。该协议满足所有安全需求，不受已知攻击的影响，并且网络开销非常低。提出的方案的安全性得到了正式证明，描述了提出的方案对不同类型可能的攻击的抵抗力。此外，所提出的方案对潜在攻击的鲁棒性已通过Scyther的自动软件验证工具得到验证。性能评估最终表明，与以前的方案相比，所提出的协议大大减少了计算和通信成本，非常适合众包环境。

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引用次数: 0