Pervasive and Mobile Computing最新文献

{"title":"LAAS-KM: Lightweight authentication with aggregate signature verification and key management protocol for VANETs","authors":"A. Anshima ,&nbsp;Jegadeesan Subramani ,&nbsp;Arun Sekar Rajasekaran","doi":"10.1016/j.pmcj.2026.102183","DOIUrl":"10.1016/j.pmcj.2026.102183","url":null,"abstract":"<div><div>Vehicular Ad Hoc Networks (VANETs) are a significant component of upcoming intelligent transportation systems. VANETs improve road safety by sending danger alerts to drivers; therefore, their messages must be secure and unaltered. Digital signatures are used to verify the integrity and authenticity of transmitted messages; however, existing digital signature-based schemes require a high computational time owing to the repeated use of mathematical operations. To address this issue, a novel signature aggregation and key management (LAAS-KM) scheme is proposed in this paper to reduce the computational cost without compromising security. First, the LAAS-KM allows roadside infrastructure to cluster multiple vehicle signatures into a compact signature to reduce the large computational overhead during the verification process. Moreover, LAAS-KM supports group communication with novel key management to update keys as vehicles move and network topologies change dynamically in VANETs. Moreover, the security analysis section indicates that the LAAS-KM can prevent various security attacks, including impersonation and replay attacks. Furthermore, a formal security analysis is performed using the Scyther tool to validate the critical security properties of LAAS-KM. Performance evaluations show that LAAS-KM outperforms traditional schemes in terms of communication and computation overheads. Finally, a practical simulation is performed using MATLAB, and the performance metrics are analyzed.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"118 ","pages":"Article 102183"},"PeriodicalIF":3.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146147121","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}

{"title":"Coordination-free decentralised federated learning in pervasive networks: Overcoming heterogeneity","authors":"Lorenzo Valerio ,&nbsp;Chiara Boldrini ,&nbsp;Andrea Passarella ,&nbsp;János Kertész ,&nbsp;Márton Karsai ,&nbsp;Gerardo Iñiguez","doi":"10.1016/j.pmcj.2026.102184","DOIUrl":"10.1016/j.pmcj.2026.102184","url":null,"abstract":"<div><div>Fully decentralised federated learning enables collaborative model training among edge devices without relying on a central coordinator, thereby avoiding single points of failure and supporting spontaneous collaboration in pervasive environments. However, the absence of coordination introduces challenges that go beyond data heterogeneity alone. In realistic decentralised settings, devices often start from different model initializations, possess limited and non-IID local data, and interact over unstructured communication graphs, making naive parameter averaging ineffective and potentially destructive. In this paper, we address decentralised learning under <em>combined data and initial model heterogeneity</em> by proposing DecDiff+VT, a coordination-free decentralised learning algorithm specifically designed for such environments. DecDiff+VT integrates two complementary mechanisms: DecDiff, a disruption-aware aggregation strategy that updates local models towards their neighborhood average with a magnitude inversely proportional to model disagreement, and a lightweight <em>virtual teacher</em> (VT) mechanism based on soft-label regularization to improve local generalization in the absence of strong or centralized teacher models. Extensive experiments on image classification and activity recognition benchmarks (MNIST, Fashion-MNIST, EMNIST, CIFAR-10, and UCI-HAR) show that DecDiff+VT consistently outperforms or matches state-of-the-art decentralised baselines, achieving faster convergence, improved generalization, and greater robustness to overfitting, without incurring additional communication or memory overhead compared to standard decentralised averaging.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"118 ","pages":"Article 102184"},"PeriodicalIF":3.5,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146192891","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}

{"title":"FreTransLS: Frequency Transformer based large-scale group activity recognition model for sensor data","authors":"Ruohong Huan,&nbsp;Meijiao Cao,&nbsp;Yantong Zhou,&nbsp;Ji Zhang,&nbsp;Peng Chen,&nbsp;Guodao Sun,&nbsp;Ronghua Liang","doi":"10.1016/j.pmcj.2026.102179","DOIUrl":"10.1016/j.pmcj.2026.102179","url":null,"abstract":"<div><div>In large-scale group activities, participants engage in a wider variety of actions, and the interactions among them become significantly more complex. This gives rise to challenges including synchronization and coordination analysis in group activity recognition. As a result, existing methods designed for recognizing small-scale group activities using sensor data often lead to inaccurate identification of dynamic patterns in large-scale settings. To address this issue, this paper proposes FreTransLS—a frequency Transformer-based model for large-scale group activity recognition using sensor data. FreTransLS introduces a novel approach for extracting time–frequency features in large-scale group activities. The approach integrates a spatio-temporal graph convolutional network (ST-GCN) module to capture spatio-temporal features within the group, along with a group location feature extraction (GLFE) module to acquire group location features. These two feature streams are fused to derive comprehensive time-domain representations of group activities. Furthermore, FreTransLS incorporates a frequency Transformer encoder built around a frequency attention mechanism. This encoder performs global analysis in the frequency domain to better model synchronization and coordination patterns in group activities. To enhance the generalization capability of the model, FreTransLS adopts a joint optimization strategy through complementary classification and reconstruction modules, which jointly refine the extracted time–frequency features. Experiments on two public datasets demonstrate that the proposed method effectively captures discriminative features from sensor data in large-scale group scenarios, leading to improved accuracy and robustness in group activity recognition.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"117 ","pages":"Article 102179"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146079111","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}

{"title":"FedEMMD: Entropy and MMD-based data and aggregation selection for non-iid and long-tailed data in federated learning","authors":"Nafas Gul Saadat,&nbsp;Santhosh Kumar G.","doi":"10.1016/j.pmcj.2025.102159","DOIUrl":"10.1016/j.pmcj.2025.102159","url":null,"abstract":"<div><div>The increasing need for privacy-preserving machine learning has rendered centralized data collection progressively unfeasible. To solve this, Federated Learning (FL) has emerged as a distributed learning paradigm in which multiple clients collectively train a shared global model while keeping all data locally, ensuring that no private data is sent over the network. However, FL is often hindered by statistical heterogeneity, where clients’ data are non-independent and identically distributed (non-iid), resulting in biased local updates and reduced global model performance. To overcome these key challenges, this study proposes FedEMMD, a novel method to enhance model performance under heterogeneous data. First, entropy-based data selection is used to identify and select high-quality data with a lower degree of non-iidness. Second, Maximum Mean Discrepancy (MMD) is used to calculate the divergence between local updates and the global model, guaranteeing that only stable and consistent updates are aggregated on the global model. Experiments have been conducted in two heterogeneous settings (non-iid and long-tailed distribution), using CIFAR-10 and CIFAR-10-LT. Additionally, we conduct experiments with centralized Machine Learning (ML) under the same settings to establish a baseline to evaluate the effect of data heterogeneity on centralized ML. The experimental results demonstrate that FedEMMD outperforms state-of-the-art algorithms such as FedAvg, FedProx, Scaffold, and FedOpt in terms of accuracy and convergence speed in both non-iid and long-tailed scenarios, thereby improving robustness and performance under heterogeneous settings.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"117 ","pages":"Article 102159"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145915146","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}

{"title":"Mobility-aware Q-learning for workload offloading in vehicular edge–cloud environment","authors":"Afzal Badshah ,&nbsp;Abdulrahman Ahmed Gharawi ,&nbsp;Mona Eisa ,&nbsp;Nada Alzaben ,&nbsp;Saud Yonbawi ,&nbsp;Ali Daud","doi":"10.1016/j.pmcj.2026.102172","DOIUrl":"10.1016/j.pmcj.2026.102172","url":null,"abstract":"<div><div>The Intelligent Transportation System (ITS) continuously generates data that needs to be processed under strict latency and connectivity constraints across a heterogeneous computing architecture (e.g., Vehicular Edge Computing (VEC), Mobile Edge Computing (MEC), and Cloud Computing (CC)). In this context, efficient task offloading requires mobility and server-aware intelligence to optimize communication delay, cost, and resource utilization. In this paper, we propose a mobility-aware Q-learning offloading scheduler that learns optimal tier selection on real-time metrics (e.g., resource availability, signal strength, and Base Station (BS) handover dynamics). Unlike the previous investigation, this approach explicitly incorporates vehicle mobility patterns to the offloading decision using Q-learning. The scheduler favors VEC when underutilized, transitions to MEC when the VEC is overutilized, and falls back to the cloud only when VEC and MEC are infeasible. A structured reward model reinforces decisions that improve resource efficiency and penalizes excessive switching or skipping underutilized resources. The proposed framework is evaluated using <em>DriveNetSim</em>, a custom-developed vehicular simulator that models realistic mobility, signal degradation, and BS switching. Simulation results show a strong preference for VEC, with shifts to MEC only under VEC over-utilization and minimal reliance on the cloud. As a result, the system achieves up to 43% reduction in transmission delay and 38% reduction in processing cost, validating its effectiveness in dynamic vehicular environments.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"117 ","pages":"Article 102172"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980648","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}

{"title":"ZEL+: Wearable net-zero-energy lifelogging using heterogeneous energy harvesters for sustainable context sensing","authors":"Mitsuru Arita ,&nbsp;Yugo Nakamura ,&nbsp;Shigemi Ishida ,&nbsp;Yutaka Arakawa","doi":"10.1016/j.pmcj.2026.102180","DOIUrl":"10.1016/j.pmcj.2026.102180","url":null,"abstract":"<div><div>This paper presents ZEL+, a wearable lifelogging system designed to operate with net-zero energy consumption by leveraging multiple energy harvesting technologies for continuous context sensing. Self-powered wearable devices often encounter difficulties in environments with inconsistent or low-intensity ambient energy, particularly in indoor settings. To address this challenge, ZEL+ incorporates three key design features. First, it employs a power-switching mechanism based on dual comparators and a capacitor to manage surplus energy and support operation under varying lighting conditions. Second, the system integrates heterogeneous energy harvesters not only as power sources but also as sensing elements. Specifically, a dye-sensitized solar cell provides stable responses under low-light indoor environments, while an amorphous solar cell exhibits sensitivity to changes in ambient illumination; together with a piezoelectric element capturing motion-induced signals, these components contribute complementary cues for location and activity recognition. Third, a Spatial Consistency-Based Correction (SCC) algorithm is applied as a post-processing step to mitigate transient recognition errors and improve the coherence of inferred lifelogs. The system is implemented as a 192<!--> <!-->g nametag-shaped wearable device and evaluated in a real-world office environment with 11 participants. Under a person-dependent setting, ZEL+ achieved an accuracy of 96.62% for 8-location place recognition and 97.09% for static/dynamic activity recognition, while maintaining robust performance on more fine-grained tasks. In terms of energy sustainability, the device sustained autonomous operation using harvested energy alone for approximately 93.97% of a standard 8-hour office workday. These results indicate that ZEL+ provides a practical and energy-sustainable solution for continuous lifelogging in indoor mobile computing environments.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"117 ","pages":"Article 102180"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173757","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}

{"title":"Delay optimized task offloading and performance evaluation in Fog-Enabled IoT networks","authors":"Megha Sharma ,&nbsp;Abhinav Tomar ,&nbsp;Abhishek Hazra","doi":"10.1016/j.pmcj.2026.102177","DOIUrl":"10.1016/j.pmcj.2026.102177","url":null,"abstract":"<div><div>The rapid growth and proliferation of Internet of Things (IoT) applications have intensified the demand for low-latency, energy-efficient task processing at the network edge. Fog computing has emerged as a key enabler to address these challenges by offloading computational workloads from resource-constrained sensor nodes to nearby fog nodes. In this context, we propose TSTO, a Thompson Sampling-based task offloading framework tailored for dynamic and non-stationary fog-enabled IoT environments. The scheme employs a two-tier mechanism: a greedy utility-based fog node selection followed by probabilistic decision-making using Thompson Sampling, ensuring balanced exploration and exploitation in volatile network states. To accelerate learning, a precomputation module estimates early rewards for tasks with optimistic deadlines. We provide a comprehensive delay-aware mathematical formulation, analyze the time complexity of the algorithm, and validate its scalability. Simulation results demonstrate that TSTO outperforms baseline methods such as D2CIT and BLOT, achieving up to 6% lower latency and 5% improved energy efficiency. Additionally, prototype-level validation using Raspberry Pi devices highlights the real-world applicability of the proposed model. These results confirm TSTO’s suitability for adaptive and intelligent task offloading in next-generation fog-assisted IoT systems.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"117 ","pages":"Article 102177"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024310","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}

{"title":"Interpretable healthcare localization with Explainable Artificial Intelligence","authors":"Mina Mohammadi ,&nbsp;Mohammad Mehdi Sepehri ,&nbsp;Vahideh Moghtadaiee ,&nbsp;Motahareh Dehghan","doi":"10.1016/j.pmcj.2026.102162","DOIUrl":"10.1016/j.pmcj.2026.102162","url":null,"abstract":"<div><div>Real-time location solutions, such as the Global Positioning System (GPS), encounter significant limitations in indoor environments. To overcome these challenges, indoor positioning systems (IPS) offer a viable alternative. IPS have emerged as critical tools for healthcare environments, where precise localization of patients and medical equipment can directly impact safety and clinical outcomes. To address the limitations of existing IPS solutions, this study introduces an interpretable framework that leverages Wi-Fi access points (APs) and Received Signal Strength Indicator (RSSI) data. The framework employs a dual-phase approach: the initial phase involves spatial fingerprinting to map indoor locations, where each position is labeled, and the task is addressed as a classification problem. In the second phase, we reframe the task as a regression problem to predict fine-grained coordinates. Extreme Gradient Boosting (XGBoost) achieves the highest performance across both classification and regression tasks. To enhance transparency, Explainable Artificial Intelligence (XAI) techniques, including SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME), are applied to identify key signal contributors and interpret model behavior. Results show that XGBoost achieves 99.07% accuracy for location classification and <span><math><mrow><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup><mo>=</mo><mn>0</mn><mo>.</mo><mn>9997</mn></mrow></math></span> for coordinate regression on a real dataset, while SHAP and LIME provide consistent global and local explanations of the APs contributions. These results indicate that the combination of XGBoost and XAI yields both high accuracy and interpretability in controlled indoor conditions, supporting practical deployment and motivating future validation under dynamic hospital environments.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"117 ","pages":"Article 102162"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146173758","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}

{"title":"Gradient-driven exploration and pattern matching experience replay for efficient UAV path planning: Flying over or around?","authors":"Zhengmiao Jin ,&nbsp;Renxiang Chen ,&nbsp;Ke Wu ,&nbsp;Dong Liang ,&nbsp;Li Yan","doi":"10.1016/j.pmcj.2025.102156","DOIUrl":"10.1016/j.pmcj.2025.102156","url":null,"abstract":"<div><div>The flexibility of unmanned aerial vehicle (UAV) provides a promising solution for large-scale urban services. However, their limited energy remains one of the primary constraints affecting task efficiency. Trajectory optimization is required for energy management, as incorrect path decisions can result in lower task performance and potentially cause damage to the UAV or the urban environment. This paper investigates the path decision-making problem of UAV in dense, high-rise urban environments, characterized by optimal decisions for flying over or around obstacles to minimize energy consumption. Firstly, this study establishes a UAV energy consumption model based on the differences in energy usage across various flight states, and frames the UAV trajectory optimization problem as a Markov Decision Process (MDP), solved using the Deep Deterministic Policy Gradient (DDPG) framework. Secondly, within the Deep Reinforcement Learning (DRL) environment, when the UAV faces a choice between flying over or around obstacles, the exploration-exploitation dilemma arises due to the target-proximity-based dense reward function setup. This research proposes a three-stage learning framework, with a notable feature in the second stage, where exploration is driven by the gradient features of obstacle height to counteract excessive exploitation induced by the reward function. Finally, to address the issue of the algorithm’s experience sampling strategy neglecting the mismatch between the current state and past experiences, which arises due to the progression of the training process, this paper proposes a two-stage experience replay strategy. One notable feature of this strategy is the pattern-matching filtering method in the second stage, which selects experiences that closely match the current state for sampling, thereby accelerating the training process. Extensive simulation experiments demonstrate the necessity and effectiveness of the proposed exploration strategy and experience replay strategy.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"117 ","pages":"Article 102156"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145980647","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}

{"title":"ALIBIS: Assessing and mitigating the risk of sensitive metadata Leakage In moBile Image Sharing","authors":"Tran Thanh Lam Nguyen,&nbsp;Barbara Carminati,&nbsp;Elena Ferrari","doi":"10.1016/j.pmcj.2026.102171","DOIUrl":"10.1016/j.pmcj.2026.102171","url":null,"abstract":"<div><div>Smartphones have become necessary in modern life and can replace traditional devices like cameras. The high demand for taking and sharing photos via smartphones, especially with the explosion of social networks and instant messaging, highlights the importance of smartphones. Android, the leading smartphone operating system, has continuously improved user security and privacy over its 17 years of development (2008–2025). However, security vulnerabilities still exist because of its open-source nature. This paper introduces ALIBIS, a framework that automatically estimates the risk of leakage of sensitive data contained in EXIF metadata when users share images online by combining static analysis and Large Language Models (LLMs). ALIBIS demonstrates consistent and robust estimation capabilities, achieving an average accuracy, precision, recall, and f1 score in k-fold cross-validation (k=5) of 0.8686, 0.8902, 0.881, and 0.8854, respectively. In addition, a survey of 130 global participants, including Android app developers and end-users, revealed a significant lack of awareness about image metadata and its risks: 82.3% of participants (user role) do not delete sensitive metadata before sharing images, and 62.3% do not know how to remove metadata. Furthermore, only 1.9% of participants (developer role) proactively remove EXIF metadata during programming. We propose ExifMetadataLib, a lightweight library for easy integration with Android OS, to mitigate sensitive metadata leakage.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"117 ","pages":"Article 102171"},"PeriodicalIF":3.5,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146024341","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}