Pervasive and Mobile Computing最新文献

{"title":"Energy-aware vehicle localization in dynamic environments via efficient machine learning techniques for positioning and power management","authors":"Hend Marouane ,&nbsp;Mohamed Mosbah ,&nbsp;Hassene Mnif ,&nbsp;Amel Meddeb Makhlouf","doi":"10.1016/j.pmcj.2025.102135","DOIUrl":"10.1016/j.pmcj.2025.102135","url":null,"abstract":"<div><div>This paper considers the localization problem for Intelligent Transport Systems (ITS) where micromobility vehicles are localized by determining their own coordinates according to the surrounding neighbors. In this work, we develop two approaches for the self-localization service by adopting efficient methods, exploiting V2X traffic exchange to offer an accurate positioning technique and to fulfill energy efficiency. Micromobility entities in this case are not supposed to be equipped with specific modules or particular sensors. First, we propose a Local Localization Approach (LLA), based on advanced KNN machine learning called combined KNN, where the algorithm adjusts the K parameter in order to estimate neighbors distances by considering all possible combinations. To update the K-value, we take into account a predefined error threshold in order to enhance the accuracy of the localization service. The proposed LLA demonstrates significant improvements compared to existing approaches. The estimated error depends on the K value. In fact, it is equal to 0.0163 m (for K = 3), 0.0151 m (for K = 5) and 0.0143 m (for K = 7). Secondly, we develop the Energy Saving version (ES-LLA) to enhance the energy efficiency. The numerical results demonstrate the performance of the two algorithms. The ES-LLA offers a gain of 0.2 W, compared to the LLA in terms of power consumption. Both algorithms present advantageous approaches because of their low cost, scalability, and fast adaptation in dynamic environments. We offer a sustainable solution for total transmit power reduction that optimizes the usage of resources on the vehicular network.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"115 ","pages":"Article 102135"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145618285","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":"SmartCert: A Multi-modal framework for automated guided vehicle screening","authors":"Xu Chen ,&nbsp;Sandeep Kanta ,&nbsp;Vincent Koc ,&nbsp;Santhi Bharath Punati ,&nbsp;Arif Hussain ,&nbsp;Sunny Katyara","doi":"10.1016/j.pmcj.2025.102127","DOIUrl":"10.1016/j.pmcj.2025.102127","url":null,"abstract":"<div><div>Global used vehicle market is undergoing rapid transformation with proliferating demand for scalable, efficient and trustworthy inspection systems that is capable of meeting stringent requirements of online marketplaces and regulatory standards. This paper introduces SmartCert, a multi-modal inspection framework engineered for robust, scalable and pervasive vehicle screening. The novelty of SmartCert lies in synergistic integration of tailored multi-modal transformer architecture with fine-grained temporal diagnostics and optimized edge deployment. An embedded cross-attention mechanism fosters seamless fusion of visual data with on-board diagnostic signals to simultaneously detect exterior damages and internal performance anomalies. To ensure reliable evaluation, SmartCert incorporates reinforcement learning agent with human-in-the-loop reward scheme for adaptive certification thresholding that reduces false positive rates by 6.8% and false negative rates by 4.2% compared to optimally tuned static thresholds. Rigorously evaluated on large-scale dataset of 10240 vehicles with edge deployment validated exclusively on 240 vehicles (2.3%) collected from diverse mobile inspection locations, SmartCert achieves F1-score of 95% for damage classification and 92% anomaly detection rate. These results demonstrate statistically significant improvements over same-dataset baseline implementations by average of 7.4% in classification accuracy and 9.6% in anomaly detection (<span><math><mrow><mi>p</mi><mo>&lt;</mo><mn>0</mn><mo>.</mo><mn>001</mn></mrow></math></span>). Furthermore in ablation study, SmartCert improves processing efficiency by 40%, reduces certification-to-sale by 30% and decreases post-sale complaints by 25% compared to traditional manual methods. By integrating explainable AI with optimized edge deployment achieve 18 FPS inference on resource-constrained hardware, SmartCert articulates end-to-end solution for next generation of trustworthy and efficient vehicle certification ecosystem.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"115 ","pages":"Article 102127"},"PeriodicalIF":3.5,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145468155","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":"XAI-driven multi-attention DeepCRNN for enhanced cyberattack detection in internet of medical things environments","authors":"Prashant Giridhar Shambharkar ,&nbsp;Nikhil Sharma","doi":"10.1016/j.pmcj.2025.102123","DOIUrl":"10.1016/j.pmcj.2025.102123","url":null,"abstract":"<div><div>The rapid proliferation of Internet of Medical Things (IoMT) devices has transformed healthcare by enabling continuous monitoring and intelligent data exchange, but it has also broadened the attack surface for cyber intrusions. Conventional intrusion detection systems (IDS) face critical challenges such as high-dimensional and imbalanced traffic patterns, dynamic data distributions, and limited adaptability in real-world IoMT settings. To overcome these limitations, we propose MA-DeepCRNN, a hybrid deep learning framework that integrates Convolutional Neural Networks (CNNs), Bidirectional LSTMs (Bi-LSTMs), and a multi-attention mechanism for robust binary and multiclass intrusion detection. The model employs a four-stage preprocessing pipeline incorporating feature augmentation, Gaussian noise injection, and categorical randomization to improve data balance and resilience. The Performance is further enhanced through epoch and batch size tuning, while an ablation study and statistical significance tests validate architectural effectiveness. Moreover, computational complexity analysis ensures suitability for resource-constrained IoMT environments, and a dual-layer explainable AI approach offers interpretability for security analysts. The Extensive experiments on the WUSTL-HDRL-2024 dataset demonstrate superior outcomes, achieving 0.9979 accuracy and 0.9966 F1-score in binary classification and 0.9823 accuracy and 0.9812 F1-score in multiclass detection. Compared with state-of-the-art, MA-DeepCRNN delivers 6–12% higher accuracy and 7–10% higher F1-score, with an overall improvement of 6.17% accuracy and 7.91% F1-score. These results establish MA-DeepCRNN as a statistically validated, interpretable, and computationally efficient IDS for real-time IoMT cybersecurity.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102123"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362446","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":"AerialDB: A federated peer-to-peer spatio-temporal edge datastore for drone fleets","authors":"Shashwat Jaiswal ,&nbsp;Suman Raj ,&nbsp;Subhajit Sidhanta ,&nbsp;Yogesh Simmhan","doi":"10.1016/j.pmcj.2025.102109","DOIUrl":"10.1016/j.pmcj.2025.102109","url":null,"abstract":"<div><div>Recent years have seen an unprecedented explosion in research that leverages the newest computing paradigm of Internet of Drones comprised of a fleet of connected Unmanned Aerial Vehicles (UAVs) used for a wide range of tasks such as monitoring and analytics in highly mobile and changing environments characteristic of disaster regions. Given that the typical data (i.e., videos and images) collected by the fleet of UAVs deployed in such scenarios can be considerably larger than what the onboard computers can process, the UAVs need to offload their data in real-time to the edge and the cloud for further processing. To that end, we present the design of AerialDB- a lightweight decentralized data storage and query system that can store and process time series data on a multi-UAV system comprising: (A) a fleet of hundreds of UAVs fitted with onboard computers, and (B) ground-based edge servers connected through a cellular link. Leveraging lightweight techniques for content-based replica placement and indexing of shards, AerialDB has been optimized for efficient processing of different possible combinations of typical spatial and temporal queries performed by real-world disaster management applications. Using containerized deployment spanning up to 400 drones and 80 edges, we demonstrate that AerialDB is able to scale efficiently while providing near real-time performance with different realistic workloads. Further, AerialDB comprises a decentralized and locality-aware distributed execution engine which provides graceful degradation of performance upon edge failures with relatively low latency while processing large spatio-temporal data. AerialDB exhibits comparable insertion performance and 100 times improvement in query performance against state-of-the-art baseline. Moreover, it experiences a 10 times improvement in performance with insertion workloads and 100 times improvement with query workloads over the cloud baseline.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102109"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107448","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":"Home activity recognition using infrequently-monitored HEMS Data","authors":"Fukuharu Tanaka ,&nbsp;Teruhiro Mizumoto ,&nbsp;Hirozumi Yamaguchi","doi":"10.1016/j.pmcj.2025.102119","DOIUrl":"10.1016/j.pmcj.2025.102119","url":null,"abstract":"<div><div>This paper proposes a method for estimating household activities based only on the cumulative power consumption data obtained from the HEMS home distribution board every 30 min. The proposed method predicts the activity of each 30 min timeslot from the eight activity labels; household-level waking-up, household-level going-to-bed, room-level waking-up, room-level going-to-bed, cooking, laundry, dishwashing, and bathing. For the prediction, we first identify the branch circuit that is strongly correlated with each activity label and detect the turn-on/off of home appliances on the circuit to detect those activities. We also incorporate machine learning for estimating the other activities based on the circuit’s time series of power consumption. Furthermore, to cope with the difference among households, we apply transfer learning to the constructed model. In collaboration with a Japanese home builder, we conducted an experiment on five households using their HEMS data. In parallel, we obtained verifiable activity labels as our ground truth by the installation of specialized sensors in the respective homes. Under a ±30 min tolerance (i.e. allowing a prediction in the immediately preceding or following half-hour slot), our model achieved an average F1 score of 0.689 across all activities. We also confirmed that transfer learning improved the F1 score of each activity recognition and achieved an average improvement of 0.260 in household-level waking-up, household-level going-to-bed, room-level waking-up, room-level going-to-bed, and bathing activities.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102119"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145227084","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":"Coordinated Q-learning based Multi-hop Routing for UAV-assisted communication","authors":"N.P. Sharvari ,&nbsp;Dibakar Das ,&nbsp;Jyotsna Bapat ,&nbsp;Debabrata Das","doi":"10.1016/j.pmcj.2025.102105","DOIUrl":"10.1016/j.pmcj.2025.102105","url":null,"abstract":"<div><div>Unmanned Aerial Vehicle (UAV) assisted communication is gaining prominence as a vital solution for establishing effective emergency communication during disaster management operations. UAVs are essential for enhancing and expanding communication systems, acting as relays to boost data transmission to ground stations, extend network coverage, and provide connectivity. However, the dynamic and resource-limited nature of aerial networks necessitates robust routing mechanisms to facilitate seamless data dissemination. While existing Q-learning-based routing protocols are adaptive to changing network conditions and resilient to failures, they often lead to suboptimal network-wide decisions due to UAVs operating independently, each maximizing its gains. This paper proposes a novel Coordinated Q-learning-based Multi-hop Routing (CQMR) algorithm for multi-UAV networks. To the best of our knowledge, this is the first time a routing algorithm introduces UAV coordination for data routing through utility function approximation with a message-passing scheme, enabling the selection of globally optimal joint actions. This novel approach meticulously considers a comprehensive set of parameters for data routing, including minimizing the expected number of hops to the destination, monitoring energy usage, maintaining network connectivity, preventing UAV collisions, and supporting adaptive network reorganization. This integrated consideration of multiple factors positions the proposed solution as superior to existing work, offering a uniquely robust and highly effective strategy for UAV-assisted communication in dynamic, resource-constrained environments, such as emergency scenarios. CQMR builds upon and extends the Improved Q-learning-based Multi-hop Routing (IQMR) algorithm, demonstrating a 12.47% increase in energy efficiency and a 13.34% higher success rate in data transmission compared to IQMR while requiring 40% fewer hops to reach the destination.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102105"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107510","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":"TDoA localization in wireless sensor networks using constrained total least squares, Newton’s methods, and alternating direction method of multipliers","authors":"Bamrung Tausiesakul,&nbsp;Krissada Asavaskulkiet","doi":"10.1016/j.pmcj.2025.102108","DOIUrl":"10.1016/j.pmcj.2025.102108","url":null,"abstract":"<div><div>An important service in the wireless systems for human daily life is the information of a mobile user’s location. Wireless sensor network is a structure that can be deployed to determine a mobile user position. Time-difference-of-arrival (TDoA) technique is often considered for wireless localization due to the low cost of the sensor network. In this work, three new Newton’s methods are proposed for computing the constrained total least squares solution in TDoA localization. Numerical simulation is conducted to demonstrate the performance of the three proposed techniques. It is found that most of them can provide better performance, in terms of about 30% lower bias and root mean square error, approximately 50% to 75% less computational time, and around 50% more reliability, than the former Newton-based algorithms.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102108"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145050375","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":"IDENTIFY: Intelligent device identification using device fingerprints and machine learning","authors":"Liwei Liu ,&nbsp;Muhammad Ajmal Azad ,&nbsp;Harjinder Lallie ,&nbsp;Hany Atlam","doi":"10.1016/j.pmcj.2025.102103","DOIUrl":"10.1016/j.pmcj.2025.102103","url":null,"abstract":"<div><div>The Internet of Things (IoT) consists of a rapidly growing network of heterogeneous devices that autonomously monitor, collect, and exchange data across a wide range of application domains. The rapid increase of IoT devices highlighted the importance of scalable, secure, and adaptive network management strategies for dynamic networks. A key challenge in this context is the automatic identification of devices, which is critical for detecting and mitigating malicious devices that can compromise network integrity. Accurate device identification strengthens the security of dynamic IoT environments by facilitating early detection of anomalous or adversarial traffic. Device fingerprinting offers a non-intrusive solution by leveraging protocol and traffic characteristics, without relying on vendor-specific identifiers. In this work, we propose a lightweight and efficient framework for IoT device identification based on machine learning. Our model utilises a Random Forest classifier in conjunction with a data-driven feature selection strategy that emphasises low-overhead features derived from packet headers and traffic flow statistics. The proposed approach achieves high classification performance, attaining 97.32% accuracy in identifying general device categories and 94.39% accuracy for specific device types. It also demonstrates approximately a 40% improvement in computational efficiency compared to traditional classifiers, making it well-suited for deployment in resource-constrained edge environments. We evaluate the model under various real-world conditions, including spatiotemporal traffic variations, changes in operational modes, and different sampling intervals. Comparative experiments with established classifiers—such as J48, SMO, BayesNet, and Naive Bayes—are performed using standard metrics, including precision, recall, F1-score, and inference latency. Our approach strengthens network security by automatically identifying and classifying IoT devices in dynamic, heterogeneous environments. It is lightweight, scalable, and well-suited for deployment in resource-constrained IoT scenarios.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102103"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145107447","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":"Optimization of safety energy efficiency of alternating relay communication systems for UAVs","authors":"Jianbin Xue,&nbsp;Qingdou Chen,&nbsp;Xiangrui Guan,&nbsp;Han Zhang","doi":"10.1016/j.pmcj.2025.102110","DOIUrl":"10.1016/j.pmcj.2025.102110","url":null,"abstract":"<div><div>The UAV alternate relay communication system shows significant advantages in the field of information transmission, as it efficiently transmits information from the sending end to the receiving end through the cooperative work of two UAVs, effectively improving the band utilization. However, this system also faces two major challenges: first, due to the limited energy on-board the UAVs, how to effectively improve the energy efficiency has become a key issue; second, there may be malicious eavesdroppers during the information transmission process, making the information security issue not to be ignored. In order to address the above issues, this paper explores a model for an alternate relay communication system for UAVs in the presence of eavesdroppers. Our aim is to improve the energy efficiency of the system by means of optimization while ensuring information security. To this end, this paper studies the joint optimization problem of the transmit power and the UAV trajectory, aiming to maximize the safety energy efficiency of the system. To solve this complex optimization problem, we first formalize it as a nonconvex mixed integer nonlinear fractional programming (MINLFP) problem. Since it is extremely challenging to solve such a problem directly, we further decompose it into more tractable optimization subproblems and propose a set of efficient iterative methods for solving it. Simulation experimental outcomes indicate that as compared to the baseline scheme, our proposed algorithm not only excels in convergence, but also significantly enhances the safety energy efficiency. In summary, the research in this paper not only proposes an effective solution to the energy efficiency and information security problems in the UAV alternate relay communication as well as improves the overall performance of the system through algorithm optimization, which provides valuable references for research in related fields.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102110"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158996","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":"Construction of Wi-Fi fingerprint database based on WGAN-PSO: A method to alleviate signal sparsity and environmental noise","authors":"Heng Xu,&nbsp;Fanyu Meng,&nbsp;Long Sun,&nbsp;Hui Shao,&nbsp;Cheng Wang","doi":"10.1016/j.pmcj.2025.102120","DOIUrl":"10.1016/j.pmcj.2025.102120","url":null,"abstract":"<div><div>To address signal sparsity and environmental noise in offline fingerprint database construction, this paper proposes a collaborative optimization method that integrates Wasserstein Generative Adversarial Network (WGAN) and Particle Swarm Optimization (PSO). First, the target area is gridded, and mobile-collected coordinates/RSSI data form the base fingerprint database. WGAN then expands the fingerprints of the sparse region under geographic boundary constraints, enhancing data diversity and spatial coverage. Finally, PSO optimizes the parameters of the path loss model through a comprehensive objective function that minimizes the RSSI estimation error. The experiments were carried out in two scenarios, in which 4 AP nodes were deployed, respectively. Data from 114 and 99 reference points were collected, generating 338,710 and 517,332 fingerprint data entries, respectively. The results demonstrate that the optimized database retains the original data features while reducing the positioning error caused by multipath effects and signal fading. Compared to traditional methods, RMSE is improved by 7.16% and 2.46% in two distinct scenarios, validating the efficacy of the proposed co-optimization framework.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102120"},"PeriodicalIF":3.5,"publicationDate":"2025-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145268086","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}