Pervasive and Mobile Computing最新文献

{"title":"Sensor-based agitation prediction in institutionalized people with dementia A systematic review","authors":"Jan Kleine Deters ,&nbsp;Sarah Janus ,&nbsp;Jair A. Lima Silva ,&nbsp;Heinrich J. Wörtche ,&nbsp;Sytse U. Zuidema","doi":"10.1016/j.pmcj.2024.101876","DOIUrl":"10.1016/j.pmcj.2024.101876","url":null,"abstract":"<div><p>Early detection of agitation in individuals with dementia can lead to timely interventions, preventing the worsening of situations and enhancing their quality of life. The emergence of multi-modal sensing and advances in artificial intelligence make it feasible to explore and apply technology for this goal. We conducted a literature review to understand the current technical developments and challenges of its integration in caregiving institutions. Our systematic review used the Pubmed and IEEE scientific databases, considering studies from 2017 onwards. We included studies focusing on linking sensor data to vocal and/or physical manifestations of agitation. Out of 1622 identified studies, 12 were selected for the final review. Analysis was conducted on study design, technology, decisional data, and data analytics. We identified a gap in the standardized semantic representation of both behavioral descriptions and system event generation configurations. This research highlighted initiatives that leverage existing information in a caregiver's routine, such as correlating electronic health records with sensor data. As predictive systems become more integrated into caregiving routines, false positive reduction needs to be addressed as those will discourage their adoption. Therefore, to ensure adaptive predictive capacity and personalized system re-configuration, we suggest future work to evaluate a framework that incorporates a human-in-the-loop approach for detecting and predicting agitation.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"98 ","pages":"Article 101876"},"PeriodicalIF":4.3,"publicationDate":"2024-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000026/pdfft?md5=f6a8397ac6e02acc44ce653a7d1a2e87&pid=1-s2.0-S1574119224000026-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139456701","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Transfer Learning and Explainable Solution to Detect mpox from Smartphones images","authors":"Mattia Giovanni Campana ,&nbsp;Marco Colussi ,&nbsp;Franca Delmastro ,&nbsp;Sergio Mascetti ,&nbsp;Elena Pagani","doi":"10.1016/j.pmcj.2023.101874","DOIUrl":"10.1016/j.pmcj.2023.101874","url":null,"abstract":"<div><p>Monkeypox (mpox) virus has become a “public health emergency of international concern” in the last few months, as declared by the World Health Organization, especially for low-income countries. A symptom of mpox infection is the appearance of rashes and skin eruptions, which can lead people to seek medical advice. A technology that might help perform a preliminary screening based on the aspect of skin lesions is the use of Machine Learning<span><span> for image classification. However, to make this technology suitable on a large scale, it should be usable directly on people </span>mobile devices, with a possible notification to a remote medical expert.</span></p><p><span>In this work, we investigate the adoption of Deep Learning<span> to detect mpox from skin lesion images derived from smartphone cameras. The proposal leverages Transfer Learning to cope with the scarce availability of mpox image datasets. As a first step, a homogeneous, unpolluted, dataset was produced by manual selection and preprocessing of available image data, publicly released for research purposes. Subsequently, we compared multiple </span></span>Convolutional Neural Networks<span> (CNNs) using a rigorous 10-fold stratified cross-validation approach and we conducted an analysis to evaluate the models’ fairness towards different skin tones. The best models have been then optimized through quantization for use on mobile devices; measures of classification quality, memory footprint<span>, and processing times validated the feasibility of our proposal. The most favorable outcomes have been achieved by MobileNetV3Large, attaining an F-1 score of 0.928 in the binary task and 0.879 in the multi-class task. Furthermore, the application of quantization led to a reduction in the model size to less than one-third, while simultaneously decreasing the inference time from 0.016 to 0.014 s, with only a marginal loss of 0.004 in F-1 score. Additionally, the use of eXplainable AI has been investigated as a suitable instrument to both technically and clinically validate classification outcomes.</span></span></p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"98 ","pages":"Article 101874"},"PeriodicalIF":4.3,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139372790","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":"Blockchain-based access control architecture for multi-domain environments","authors":"Zhiqiang Du ,&nbsp;Yunliang Li ,&nbsp;Yanfang Fu ,&nbsp;Xianghan Zheng","doi":"10.1016/j.pmcj.2024.101878","DOIUrl":"10.1016/j.pmcj.2024.101878","url":null,"abstract":"<div><p><span>Numerous users from diverse domains access information and perform various operations in multi-domain environments. These users have complex permissions that increase the risk of identity falsification, unauthorized access, and privacy breaches during cross-domain interactions. Consequently, implementing an access control architecture to prevent users from engaging in illicit activities is imperative. This paper proposes a novel blockchain-based access control architecture for multi-domain environments. By integrating the multi-domain environment within a federated chain, the architecture utilizes Decentralized Identifiers (DIDs) for user identification and relies on public/secret key pairs for operational execution. Verifiable credentials are used to authorize permissions and release resources, thereby ensuring </span>authentication<span> and preventing tampering and forgery. In addition, the architecture automates the authorization and access control processes through smart contracts<span>, thereby eliminating human intervention. Finally, we performed a simulation evaluation of the architecture. The most time-consuming process had a runtime of 1074 ms, primarily attributed to interactions with the blockchain. Concurrent testing revealed that with a concurrency level of 2000 demonstrated that the response times for read and write operations were maintained within 1000 ms and 4600 ms, respectively. In terms of storage efficiency, except for user registration, which incurred two gas charges, all the other processes required only one charge.</span></span></p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"98 ","pages":"Article 101878"},"PeriodicalIF":4.3,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139372834","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":"A provably secure and practical end-to-end authentication scheme for tactile Industrial Internet of Things","authors":"Yimin Guo ,&nbsp;Yajun Guo ,&nbsp;Ping Xiong ,&nbsp;Fan Yang ,&nbsp;Chengde Zhang","doi":"10.1016/j.pmcj.2024.101877","DOIUrl":"10.1016/j.pmcj.2024.101877","url":null,"abstract":"<div><p><span>In the Industrial Internet of Things (IIoT), </span>haptic<span><span><span> control of machines or robots can be managed remotely. However, with the emergence of Tactile Industrial Internet of Things (TIIoT), the transmission of haptic data over public channels has raised security and privacy concerns. In such an environment, mutual authentication between haptic users and remotely controlled entities is crucial to prevent illegal control by adversaries. Therefore, we propose an end-to-end </span>authentication scheme, SecTIIoT, to establish secure communication between haptic users and remote </span>IoT<span> devices. The scheme addresses security issues by using lightweight hash cryptographic primitives and employs a useful piggyback strategy to improve authentication efficiency. We demonstrate that SecTIIoT is resilient to various known attacks with formal security proofs and informal security analysis. Furthermore, our detailed performance analysis shows that SecTIIoT outperforms existing lightweight authentication schemes as it provides more security features while reducing computation and communication costs.</span></span></p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"98 ","pages":"Article 101877"},"PeriodicalIF":4.3,"publicationDate":"2024-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139372835","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":"SMARTCOPE: Smartphone Change Of Possession Evaluation for continuous authentication","authors":"Nicholas Cariello ,&nbsp;Seth Levine ,&nbsp;Gang Zhou ,&nbsp;Blair Hoplight ,&nbsp;Paolo Gasti ,&nbsp;Kiran S. Balagani","doi":"10.1016/j.pmcj.2023.101873","DOIUrl":"10.1016/j.pmcj.2023.101873","url":null,"abstract":"<div><p><span><span>The goal of continuous smartphone authentication is to detect when the adversary has gained possession of the user’s device post-login. This is achieved by triggering re-authentication at fixed, frequent intervals. However, these intervals do not take into account external information that might indicate that the impostor has gained physical access to the user’s device. Continuous smartphone authentication typically relies on behavioral cues, such as hand movement and touchscreen swipes, that can be collected without interrupting the user’s activity. Because these behavioral signals are characterized by relatively high error rates compared to physiological </span>biometrics, their use at fixed intervals leads to unnecessary interruptions to the user’s activity in case of a false reject, </span><em>and</em> to not recognizing the impostor in case of a false accept.</p><p>To address these issues, in this paper we introduce a novel framework called SMARTCOPE: <em>Smartphone Change Of Possession Evaluation</em><span>. In this work, SMARTCOPE leverages smartphone movement signals collected during user activity to determine when the smartphone is no longer in the hands of its owner. When this occurs, SMARTCOPE triggers re-authentication. By using these signals, we are able to reduce the total number of re-authentication points while simultaneously lowering re-authentication error rates. Our analysis shows that our technique can reduce equal error rates<span> by over 40%, from 7.8% to 4.6% using movement and keystroke features. Further, we show that SMARTCOPE can be used to transform a static (login-time) authentication system, such as face recognition, to a continuous re-authentication system, with a significant increase in security and limited impact on usability.</span></span></p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"97 ","pages":"Article 101873"},"PeriodicalIF":4.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139027832","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":"A New Lightweight Routing Protocol for Internet of Mobile Things Based on Low Power and Lossy Network Using a Fuzzy-Logic Method","authors":"Zahra Ghanbari ,&nbsp;Nima Jafari Navimipour ,&nbsp;Mehdi Hosseinzadeh ,&nbsp;Hassan Shakeri ,&nbsp;Aso Darwesh","doi":"10.1016/j.pmcj.2023.101872","DOIUrl":"10.1016/j.pmcj.2023.101872","url":null,"abstract":"<div><p><span><span>The IoT<span> devices with embedded mobile devices create the Internet of Mobile Things (IoMT) paradigm. Mobility is not supported by the routing protocol for low-power and lossy networks (RPL) created for static networks. IoMT has raised routing challenges such as link failure, instability, energy depletion, </span></span>packet loss<span>, and handover delay in the network. In this context, IoMT Fuzzy-based RPL (IoMT-FRPL) is proposed in this paper to enhance routing performance. Receiving Signal Strength Indicator (RSSI), </span></span>Euclidean distance<span>, Hop Count, and Expected Transmission Count (ETX) metrics are built into the fuzzy interface system for the mobile nodes in the network to conserve energy. The IoMT-FRPL consists of the following three key steps: The first steps are data transmission and motion investigation, the second is fuzzy-based prediction of a new static parent for the mobile node, and the third is verifying the unique attachment point. When conventional RPL, mRPL, and EMA-RPL were compared to IoMT-performance FRPL's in Cooja/Contiki 2.7, the simulation results revealed improvements in energy consumption, handover delay, packet delivery rate (PDR), and signaling cost.</span></p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"97 ","pages":"Article 101872"},"PeriodicalIF":4.3,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139021172","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":"Energy consumption of smartphones and IoT devices when using different versions of the HTTP protocol","authors":"Chiara Caiazza ,&nbsp;Valerio Luconi ,&nbsp;Alessio Vecchio","doi":"10.1016/j.pmcj.2023.101871","DOIUrl":"10.1016/j.pmcj.2023.101871","url":null,"abstract":"<div><p>HTTP is frequently used by smartphones and IoT devices to access information and Web services. Nowadays, HTTP is used in three major versions, each introducing significant changes with respect to the previous one. We evaluated the energy consumption of the major versions of the HTTP protocol when used in the communication between energy-constrained devices and cloud-based or edge-based services. Experimental results show that in a machine-to-machine communication scenario, for the considered client devices – a smartphone and a Single Board Computer – and for a number of cloud/edge services and facilities, HTTP/3 frequently requires more energy than the previous versions of the protocol. The focus of our analysis is on machine-to-machine communication, but to obtain a broader view we also considered a client–server interaction pattern that is more browsing-like. In this case, HTTP/3 can be more energy efficient than the other versions.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"97 ","pages":"Article 101871"},"PeriodicalIF":4.3,"publicationDate":"2023-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119223001293/pdfft?md5=c73530019c8ccf2d77f8c4830f5951c0&pid=1-s2.0-S1574119223001293-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138742538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"PDCHAR: Human activity recognition via multi-sensor wearable networks using two-channel convolutional neural networks","authors":"Yvxuan Ren,&nbsp;Dandan Zhu,&nbsp;Kai Tong,&nbsp;Lulu Xv,&nbsp;Zhengtai Wang,&nbsp;Lixin Kang,&nbsp;Jinguo Chai","doi":"10.1016/j.pmcj.2023.101868","DOIUrl":"10.1016/j.pmcj.2023.101868","url":null,"abstract":"<div><p>Realizing human activity recognition is an important issue in pedestrian navigation and intelligent prosthetic control. Utilizing miniature multi-sensor wearable networks is a reliable method to improve the efficiency and convenience of the recognition system. Effective feature extraction and fusion of multimodal signals is a key issue in recognition. Therefore, this paper proposes an enhanced algorithm based on PCA sensor coupling analysis for data preprocessing. Subsequently, an innovative two-channel convolutional neural network with an SPF feature fusion layer as the core is built. The network fully analyzes the local and global features of multimodal signals using the local contrast and luminance properties of feature images. Compared with traditional methods, the model can reduce the data dimensionality and automatically identify and fuse the key information of the signals. In addition, most of the current mode recognition only supports simple actions such as walking and running, this paper constructs a database containing sixteen states by building a network with inertial sensors (IMU), curvature sensors (FLEX) and electromyography sensors (EMG). The experimental results show that the proposed system exhibits better results in complex action recognition and provides a new scheme for the realization of feature fusion and enhancement.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"97 ","pages":"Article 101868"},"PeriodicalIF":4.3,"publicationDate":"2023-12-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138581597","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":"Tracking people across ultra populated indoor spaces by matching unreliable Wi-Fi signals with disconnected video feeds","authors":"Hai Truong ,&nbsp;Dheryta Jaisinghani ,&nbsp;Shubham Jain ,&nbsp;Arunesh Sinha ,&nbsp;JeongGil Ko ,&nbsp;Rajesh Balan","doi":"10.1016/j.pmcj.2023.101860","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101860","url":null,"abstract":"<div><p>Tracking in dense indoor environments where several thousands of people move around is an extremely challenging problem. In this paper, we present a system — <em>DenseTrack</em> for tracking people in such environments. <em>DenseTrack</em><span> leverages data from the sensing modalities that are already present in these environments — Wi-Fi (from enterprise network deployments) and Video (from surveillance cameras). We combine Wi-Fi information with video data to overcome the individual errors induced by these modalities. More precisely, the locations derived from video are used to overcome the localization errors<span> inherent in using Wi-Fi signals where precise Wi-Fi MAC IDs are used to locate the same devices across different levels and locations inside a building. Typically, localization<span> in dense environments is a computationally expensive process when done with just video data; hence hard to scale. </span></span></span><em>DenseTrack</em> combines Wi-Fi and video data to improve the accuracy of tracking people that are represented by video objects from non-overlapping video feeds. <em>DenseTrack</em><span> is a scalable and device-agnostic solution as it does not require any app installation on user smartphones or modifications to the Wi-Fi system. At the core of </span><em>DenseTrack</em>, is our algorithm — inCremental Association of Independent Variables under Uncertainty (CAIVU). CAIVU is inspired by the multi-armed bandits model and is designed to handle various complex features of practical real-world environments. CAIVU matches the devices reported by an off-the-shelf Wi-Fi system using connectivity information to specific video blobs obtained through a computationally efficient analysis of video data. By exploiting data from heterogeneous sources, <em>DenseTrack</em> offers an effective real-time solution for individual tracking in heavily populated indoor environments. We emphasize that no other previous system targeted nor was validated in such dense indoor environments. We tested <em>DenseTrack</em> extensively using both simulated data, as well as two real-world validations using data from an extremely dense convention center and a moderately dense university environment. Our simulation results show that <em>DenseTrack</em> achieves an average video-to-Wi-Fi matching accuracy of up to 90% in dense environments with a matching latency of 60 s on the simulator. When tested in a real-world extremely dense environment with over 500,000 people moving between different non-overlapping camera feeds, <em>DenseTrack</em><span> achieved an average match accuracy of 83% to within a 2-people distance with an average latency of 48 s.</span></p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"97 ","pages":"Article 101860"},"PeriodicalIF":4.3,"publicationDate":"2023-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138474608","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":"Hybrid machine learning model for malware analysis in android apps","authors":"Saba Bashir ,&nbsp;Farwa Maqbool ,&nbsp;Farhan Hassan Khan ,&nbsp;Asif Sohail Abid","doi":"10.1016/j.pmcj.2023.101859","DOIUrl":"10.1016/j.pmcj.2023.101859","url":null,"abstract":"<div><p><span>Android<span><span> smartphones have been widely adopted across the globe. They have the capability to access private and confidential information resulting in these devices being targeted by malware devisers. The dramatic escalation of assaults build an awareness to create a robust system that detects the occurrence of malicious actions in </span>Android applications. The malware exposure study consists of static and dynamic analysis. This research work proposed a hybrid </span></span>machine learning<span><span><span> model based on static and dynamic analysis which offers efficient classification and detection of Android malware. The proposed novel malware classification technique can process any android application, then extracts its features, and predicts whether the applications under process is malware or benign. The proposed malware detection model can characterizes diverse malware types from Android platform with high positive rate. The proposed approach detects </span>malicious applications<span><span> in reduced execution time while also improving the security of Android as compared to existing approaches. State-of-the-art machine learning algorithms such as </span>Support Vector Machine, k-Nearest Neighbor, Naïve Bayes, and different ensembles are employed on benign and malign applications to assess the execution of all classifiers on permissions, API calls and intents to identify malware. The proposed technique is evaluated on Drebin, MalGenome and Kaggle dataset, and outcomes indicate that this robust system improved runtime detection of malware with high speed and accuracy. Best accuracy of 100% is achieved on benchmark dataset when compared with </span></span>state of the art techniques. Furthermore, the proposed approach outperforms state of the art techniques in terms of computational time, true positive rate, false positive rate, accuracy, precision, recall, and f-measure.</span></p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"97 ","pages":"Article 101859"},"PeriodicalIF":4.3,"publicationDate":"2023-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135515228","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}