Pervasive and Mobile Computing最新文献

{"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}

{"title":"DA-HAR: Dual adversarial network for environment-independent WiFi human activity recognition","authors":"Long Sheng ,&nbsp;Yue Chen ,&nbsp;Shuli Ning ,&nbsp;Shengpeng Wang ,&nbsp;Bin Lian ,&nbsp;Zhongcheng Wei","doi":"10.1016/j.pmcj.2023.101850","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101850","url":null,"abstract":"<div><p><span><span>As the cornerstone of the development of emerging integrated sensing and communication, human activity recognition technology based on WiFi signals has been extensively studied. However, the existing activity sensing models will suffer serious </span>performance degradation<span><span> when applied to new scenarios due to the influence of environmental dynamics. To address this issue, we present an environment-independent activity recognition model named DA-HAR, which utilizes dual adversarial network. The framework exploits adversarial training among source domain classifiers and source–target domain </span>discriminators to extract environment-independent activity features. To improve the performance of the model, a pseudo-label prediction based approach is introduced to assign labels to the target domain samples that closely resemble the source domain samples, thus mitigating the distribution deviation of activity features between source domain and target domain. Experimental results show that our proposed model has better cross-domain recognition performance compared to state-of-the-art recognition systems, especially when the distribution of activity features in the source domain and the target domain is significantly different, the accuracy is improved by 6.96% </span></span><span><math><mo>∼</mo></math></span> 11.22%.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"96 ","pages":"Article 101850"},"PeriodicalIF":4.3,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92014450","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":"MRIM: Lightweight saliency-based mixed-resolution imaging for low-power pervasive vision","authors":"Ji-Yan Wu,&nbsp;Vithurson Subasharan,&nbsp;Tuan Tran,&nbsp;Kasun Gamlath,&nbsp;Archan Misra","doi":"10.1016/j.pmcj.2023.101858","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101858","url":null,"abstract":"<div><p><span><span>While many pervasive computing applications increasingly utilize real-time context extracted from a vision sensing infrastructure, the high energy overhead of DNN-based vision sensing pipelines remains a challenge for sustainable in-the-wild deployment. One common approach to reducing such energy overheads is the capture and transmission of lower-resolution images to an edge node (where the </span>DNN inferencing task is executed), but this results in an accuracy-vs-energy tradeoff, as the DNN inference accuracy typically degrades with a drop in resolution. In this work, we introduce </span><em>MRIM</em>, a simple but effective framework to tackle this tradeoff. Under <em>MRIM</em><span>, the vision sensor platform first executes a lightweight preprocessing step to determine the saliency of different sub-regions within a single captured image frame, and then performs a saliency-aware non-uniform downscaling of individual sub-regions to produce a “mixed-resolution” image. We describe two novel low-complexity algorithms that the sensor platform can use to quickly compute suitable resolution choices for different regions under different energy/accuracy constraints. Experimental studies, involving object detection tasks evaluated traces from two benchmark urban monitoring datasets as well as a prototype Raspberry Pi-based </span><em>MRIM</em> implementation, demonstrate <em>MRIM’s</em> efficacy: even with an unoptimized embedded platform, <em>MRIM</em><span> can provide system energy conservation of </span><span><math><mrow><mn>35</mn><mo>+</mo><mtext>%</mtext></mrow></math></span> (<span><math><mo>∼</mo></math></span>80% in high accuracy regimes) or increase task accuracy by <span><math><mrow><mn>8</mn><mo>+</mo><mtext>%</mtext></mrow></math></span><span>, over conventional baselines of uniform resolution downscaling or image encoding, while supporting high throughput. On a low power ESP32 vision board, </span><em>MRIM</em><span> continues to provide 60+% energy savings over uniform downscaling while maintaining high detection accuracy. We further introduce an automated data-driven technique for determining a close-to-optimal number of </span><em>MRIM</em> sub-regions (for differential resolution adjustment), across different deployment conditions. We also show the generalized use of <em>MRIM</em><span> by considering an additional license plate recognition (LPR) task: while alternative approaches suffer 35%–40% loss in accuracy, </span><em>MRIM</em> suffers only a modest recognition loss of <span><math><mo>∼</mo></math></span>10% even when the transmission data is reduced by over 50%.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"96 ","pages":"Article 101858"},"PeriodicalIF":4.3,"publicationDate":"2023-10-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"92108576","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 concept drift handling for batch android malware detection models","authors":"Borja Molina-Coronado ,&nbsp;Usue Mori ,&nbsp;Alexander Mendiburu ,&nbsp;Jose Miguel-Alonso","doi":"10.1016/j.pmcj.2023.101849","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101849","url":null,"abstract":"<div><p>The rapidly evolving nature of Android apps poses a significant challenge to static batch machine learning algorithms employed in malware detection systems, as they quickly become obsolete. Despite this challenge, the existing literature pays limited attention to addressing this issue, with many advanced Android malware detection approaches, such as Drebin, DroidDet and MaMaDroid, relying on static models. In this work, we show how retraining techniques are able to maintain detector capabilities over time. Particularly, we analyze the effect of two aspects in the efficiency and performance of the detectors: (1) the frequency with which the models are retrained, and (2) the data used for retraining. In the first experiment, we compare periodic retraining with a more advanced concept drift detection method that triggers retraining only when necessary. In the second experiment, we analyze sampling methods to reduce the amount of data used to retrain models. Specifically, we compare fixed sized windows of recent data and state-of-the-art active learning methods that select those apps that help keep the training dataset small but diverse. Our experiments show that concept drift detection and sample selection mechanisms result in very efficient retraining strategies which can be successfully used to maintain the performance of the static Android malware state-of-the-art detectors in changing environments.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"96 ","pages":"Article 101849"},"PeriodicalIF":4.3,"publicationDate":"2023-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49767195","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":"Lightweight accurate trigger to reduce power consumption in sensor-based continuous human activity recognition","authors":"Emanuele Lattanzi,&nbsp;Lorenzo Calisti,&nbsp;Paolo Capellacci","doi":"10.1016/j.pmcj.2023.101848","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101848","url":null,"abstract":"<div><p>Wearable devices have become increasingly popular in recent years, and they offer a great opportunity for sensor-based continuous human activity recognition in real-world scenarios. However, one of the major challenges is their limited battery life. In this study, we propose an energy-aware human activity recognition framework for wearable devices based on a lightweight accurate trigger. The trigger acts as a binary classifier capable of recognizing, with maximum accuracy, the presence or absence of one of the interesting activities in the real-time input signal and it is responsible for starting the energy-intensive classification procedure only when needed. The measurement results conducted on a real wearable device show that the proposed approach can reduce energy consumption by up to 95% in realistic case studies, with a cost of performance deterioration of at most 1% or 2% compared to the traditional energy-intensive classification strategy.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"96 ","pages":"Article 101848"},"PeriodicalIF":4.3,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49764728","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":"The geodesic mutual visibility problem: Oblivious robots on grids and trees","authors":"Serafino Cicerone ,&nbsp;Alessia Di Fonso ,&nbsp;Gabriele Di Stefano ,&nbsp;Alfredo Navarra","doi":"10.1016/j.pmcj.2023.101842","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101842","url":null,"abstract":"<div><p>The <span>Mutual Visibility</span> is a well-known problem in the context of mobile robots. For a set of <span><math><mi>n</mi></math></span> robots disposed in the Euclidean plane, it asks for moving the robots without collisions so as to achieve a placement ensuring that no three robots are collinear. For robots moving on graphs, we consider the <span>Geodesic Mutual Visibility</span>\u0000(<span><math><mi>GMV</mi></math></span>) problem. Robots move along the edges of the graph, without collisions, so as to occupy some vertices that guarantee they become pairwise geodesic mutually visible. This means that there is a shortest path (i.e., a “geodesic”) between each pair of robots along which no other robots reside. We study this problem in the context of trees and (finite or infinite) square grids, for robots operating under the standard Look–Compute–Move model. In both scenarios, we provide resolution algorithms along with formal correctness proofs, highlighting the most relevant peculiarities arising within the different contexts, while optimizing the time complexity.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"95 ","pages":"Article 101842"},"PeriodicalIF":4.3,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49746298","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–Aware security protocol for IoT devices","authors":"Malak Barari,&nbsp;Ramzi Saifan","doi":"10.1016/j.pmcj.2023.101847","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101847","url":null,"abstract":"<div><p><span>Internet of Things (IoT) has permeated various aspects of modern life, from smart homes to factories and even gardens. In the coming years, number of IoT devices is expected to surpass that of computers, laptops, mobile phones, and tablets. However, many of these devices are small and operate on batteries, making energy efficiency a significant challenge. This challenge affects all aspects of IoT, including security. To address this issue, we present an adaptive security approach in this paper. Adaptive security involves adjusting the </span>security level<span> based on the level of threats and data context, rather than always assuming the worst-case scenario. This approach reduces energy consumption and is implemented in three parts: 1) Adapting the length of RSA public and private keys, where longer keys provide more security but consume more power. 2) Adapting the trust level between nodes based on the history of the transmitting node, where the receiving node decides whether to verify the correctness of the received messages or not. 3) Utilizing TrustChain, which is transactional verification method inspired<span> by the blockchain concept.</span></span></p><p>We evaluated the performance of our proposed model through exhaustive simulation scenarios and experiments. Our approach outperforms state-of-the-art methods, with the variable key length approach reducing energy consumption by 50%, the trust level approach reducing energy consumption by approximately 50%, and the TrustChain approach reducing energy consumption to 0.771 J, while the blockchain-based method consumed 2.955 J to verify transactions.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"96 ","pages":"Article 101847"},"PeriodicalIF":4.3,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49747530","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":"Behavioural patterns discovery for lifestyle analysis from egocentric photo-streams","authors":"Martín Menchón ,&nbsp;Estefania Talavera ,&nbsp;José Massa ,&nbsp;Petia Radeva","doi":"10.1016/j.pmcj.2023.101846","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101846","url":null,"abstract":"<div><p>Automatic tools for the analysis of human behaviour are very important when aiming to understand the lifestyle of people. Egocentric wearable cameras allow the capture of images during long periods of time and in this way bring objective evidence of the experiences of the user.</p><p>In this paper, we propose a novel framework to discover behavioural patterns following an unsupervised greedy approach based on extracted image descriptors. The method collects and constructs time-frames to extract the semantics of user behaviour in terms of contextual information, such as places, activity, present objects, and others. Later, the similarity among the user time-frames is computed to assess correlations and thus obtain the user’s routine descriptors. To evaluate the performance of our method, we present several score metrics and compare them to state-of-the-art works in the field. We validated our method on 315 days and more than 390,000 images extracted from 14 users. Results show that behavioural patterns can be successfully discovered and that they are able to characterize the routine of people bringing important information about their lifestyle and behaviour change.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"95 ","pages":"Article 101846"},"PeriodicalIF":4.3,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49746857","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 dynamic group key agreement scheme for UAV networks based on blockchain","authors":"Jie Zhou,&nbsp;Ming Luo,&nbsp;Lixin Song,&nbsp;Jinlin Hu","doi":"10.1016/j.pmcj.2023.101844","DOIUrl":"https://doi.org/10.1016/j.pmcj.2023.101844","url":null,"abstract":"<div><p><span>In mobile ad hoc networks<span> (MANETs), group communication is an important communication mode, and group key<span> agreement technology is the key to ensure the security of group communication. Due to the influence of node movement, topology change and </span></span></span>network congestion<span><span><span> in MANETs, the traditional group key agreement technology has many security and performance problems, such as node authentication<span><span>, access control, data integrity, topology maintenance, routing protocol and so on. Therefore, we propose a certificateless dynamic group key protocol scheme based on blockchain, which can be used for </span>cluster communication in </span></span>unmanned aerial vehicle<span> (UAV). In our scheme, blockchain technology is used to realize the agreement of group keys, which protects privacy and ensures the transparency of the agreement process. All nodes can verify the generation process of the group key through the information recorded in the blockchain. Meanwhile, the consensus algorithm of blockchain can ensure that the process of generating group keys is fair and there is no interference from malicious nodes. In addition, we propose a certificateless </span></span>authentication protocol, which solves the problems of key escrow and batch authentication in traditional group key agreement technology. We have made formal security analysis and performance analysis, and the results show that this scheme is more secure and efficient than the existing certificateless group key protocol. This scheme can solve the problems existing in traditional group key agreement technology, such as key leakage and centralized management, and realize secure access control and key agreement mechanism. Therefore, this scheme is a safe and efficient solution, which can be effectively applied to the communication security of UAV network.</span></p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"95 ","pages":"Article 101844"},"PeriodicalIF":4.3,"publicationDate":"2023-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49746862","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}