Pervasive and Mobile Computing最新文献

{"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":"2025-09-27","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":"On-device indoor place prediction using WiFi-RTT and inertial sensors","authors":"Pritam Sen ,&nbsp;Xiaopeng Jiang ,&nbsp;Qiong Wu ,&nbsp;Manoop Talasila ,&nbsp;Wen-Ling Hsu ,&nbsp;Cristian Borcea","doi":"10.1016/j.pmcj.2025.102118","DOIUrl":"10.1016/j.pmcj.2025.102118","url":null,"abstract":"<div><div>High-accuracy and low-latency indoor place prediction for mobile users can enable a wide range of applications for domains such as assisted living and smart homes. In this paper, we propose GoPlaces, a practical indoor place prediction system that works on mobile devices without requiring any new infrastructure. GoPlaces does not rely on servers or specialized localization infrastructure, except for a single cheap off-the-shelf WiFi access point that supports ranging with Round Trip Time (RTT) protocol. GoPlaces enables personalized place naming and prediction, and it protects users’ location privacy. It fuses inertial sensor data with distances estimated using the WiFi-RTT protocol to predict the indoor places a user will visit. GoPlaces employs an attention-based BiLSTM model to detect user’s current trajectory, which is then used together with historical information stored in a prediction tree to infer user’s future places. We implemented GoPlaces in Android and evaluated it in several indoor spaces. The experimental results demonstrate prediction accuracy as high as 86%. Furthermore, they show GoPlaces is feasible in real life because it has low latency and low resource consumption on the phones.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102118"},"PeriodicalIF":3.5,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145158997","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":"2025-09-15","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":"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":"2025-09-15","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":"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":"2025-09-09","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":"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":"2025-09-09","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":"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":"2025-09-02","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":"Minimizing communication-computing energy consumption for UAV assisted collaborative computing offloading","authors":"Zhiqi Li,&nbsp;Qing Wei,&nbsp;Wenle Bai","doi":"10.1016/j.pmcj.2025.102104","DOIUrl":"10.1016/j.pmcj.2025.102104","url":null,"abstract":"<div><div>Unmanned aerial vehicles (UAVs) are viewed as a potential technology for handling user offloading duties as edge nodes. With their adaptable qualities, UAVs may be quickly deployed to useful locations and service consumers. However, the inability of UAVs to operate continuously for an extended time is a challenge for the current UAV-assisted mobile edge computing solutions. We put forth an optimization problem that involves the dynamic division of computational windows for UAVs, the optimization of user grouping and user transmission power, and the optimization of UAV deployment locations to save energy. We design a Communication-Computing Resource Scheduling with Dynamic computational Window allocation (CCRS-DW) algorithm to realize the problem decomposition and optimization. Specifically, the <span><math><mi>K</mi></math></span>-means clustering technique and the bisection search are used to tackle this problem. Simulation results show that the energy consumption of the proposed CCRS-DW scheme is significantly lower than that of other benchmark schemes.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"113 ","pages":"Article 102104"},"PeriodicalIF":3.5,"publicationDate":"2025-08-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144913694","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":"Efficient community detection in disaster networks using spectral sparsification","authors":"Annalisa Socievole,&nbsp;Clara Pizzuti","doi":"10.1016/j.pmcj.2025.102106","DOIUrl":"10.1016/j.pmcj.2025.102106","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"113 ","pages":"Article 102106"},"PeriodicalIF":3.5,"publicationDate":"2025-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865802","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":"Position claim verification for emergency message propagation in Vehicular Ad-Hoc Networks","authors":"Armir Bujari ,&nbsp;Mirko Franco ,&nbsp;Claudio E. Palazzi ,&nbsp;Davide Quaglio ,&nbsp;Anna Maria Vegni","doi":"10.1016/j.pmcj.2025.102107","DOIUrl":"10.1016/j.pmcj.2025.102107","url":null,"abstract":"<div><div>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.</div></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"114 ","pages":"Article 102107"},"PeriodicalIF":3.5,"publicationDate":"2025-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145027667","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}