Pervasive and Mobile Computing最新文献

{"title":"Quantum-resistance blockchain-assisted certificateless data authentication and key exchange scheme for the smart grid metering infrastructure","authors":"Hema Shekhawat ,&nbsp;Daya Sagar Gupta","doi":"10.1016/j.pmcj.2024.101919","DOIUrl":"10.1016/j.pmcj.2024.101919","url":null,"abstract":"<div><p>In the contemporary landscape of energy infrastructure, the “smart-grid metering infrastructure (SGMI)” emerges as a pivotal entity for efficiently monitoring and regulating electricity generation in response to client behavior. Within this context, SGMI addresses a spectrum of pertinent security and privacy concerns. This study systematically addresses the inherent research problems associated with SGMI and introduces a lattice-based blockchain-assisted certificateless data authentication and key exchange scheme. The primary aim of this scheme is to establish quantum resistance, conditional anonymity, dynamic participation, and the capacity for key updates and revocations, all of which are imperative facets for the robust implementation of mutual authentication within SGMI. Our scheme harnesses blockchain technology to mitigate the vulnerabilities associated with centralized administrative control, thus eliminating the risk of a single-point failure and distributed denial-of-service attacks. Furthermore, our proposed scheme is meticulously designed to accommodate the resource constraints of smart meters, characterized by lightweight operations. Rigorous formal security analysis is conducted within the framework of the quantum-accessible random oracle model, fortified by ’history-free reduction,’ substantiating its security credentials. Complementing this, simulation orchestration serves to underscore its superiority over existing methodologies, particularly in the realms of energy efficiency, data computation, communication, and the costs associated with private key storage.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140167635","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":"SEEK+: Securing vehicle GPS via a sequential dashcam-based vehicle localization framework","authors":"Peng Jiang ,&nbsp;Hongyi Wu ,&nbsp;Yanxiao Zhao ,&nbsp;Dan Zhao ,&nbsp;Gang Zhou ,&nbsp;Chunsheng Xin","doi":"10.1016/j.pmcj.2024.101916","DOIUrl":"10.1016/j.pmcj.2024.101916","url":null,"abstract":"<div><p>Nowadays, the Global Positioning System (GPS) plays an critical role in providing navigational services for transportation and a variety of other location-dependent applications. However, the emergent threat of GPS spoofing attacks compromises the safety and reliability of these systems. In response, this study introduces a cutting-edge computer vision-based methodology, the SEquential dashcam-based vEhicle localization frameworK Plus (SEEK+), designed to counteract GPS spoofing. By analyzing dashcam footage to ascertain a vehicle’s actual location, SEEK+ scrutinizes the authenticity of reported GPS data, effectively identifying spoofing incidents. The application of dashcam imagery for localization, however, presents inherent obstacles, such as adverse lighting and weather conditions, seasonal and temporal image variations, obstructions within the camera’s field of view, and fluctuating vehicle velocities. To overcome these issues, SEEK+ integrates innovative strategies within its framework, demonstrating superior efficacy over existing approaches with a notable detection accuracy rate of up to 94%.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140275819","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":"Seeing the world from its words: All-embracing Transformers for fingerprint-based indoor localization","authors":"Son Minh Nguyen ,&nbsp;Duc Viet Le ,&nbsp;Paul J.M. Havinga","doi":"10.1016/j.pmcj.2024.101912","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101912","url":null,"abstract":"<div><p>In this paper, we present all-embracing Transformers (AaTs) that are capable of deftly manipulating attention mechanism for Received Signal Strength (RSS) fingerprints in order to invigorate localizing performance. Since most machine learning models applied to the RSS modality do not possess any attention mechanism, they can merely capture superficial representations. Moreover, compared to textual and visual modalities, the RSS modality is inherently notorious for its sensitivity to environmental dynamics. Such adversities inhibit their access to subtle but distinct representations that characterize the corresponding location, ultimately resulting in significant degradation in the testing phase. In contrast, a major appeal of AaTs is the ability to focus exclusively on relevant anchors in RSS sequences, allowing full rein to the exploitation of subtle and distinct representations for specific locations. This also facilitates disregarding redundant clues formed by noisy ambient conditions, thus enhancing accuracy in localization. Apart from that, explicitly resolving the representation collapse (<em>i.e.</em>, none-informative or homogeneous features, and gradient vanishing) can further invigorate the self-attention process in transformer blocks, by which subtle but distinct representations to specific locations are radically captured with ease. For that purpose, we first enhance our proposed model with two sub-constraints, namely covariance and variance losses at the <em>Anchor2Vec</em>. The proposed constraints are automatically mediated with the primary task towards a novel multi-task learning manner. In an advanced manner, we present further the ultimate in design with a few simple tweaks carefully crafted for transformer encoder blocks. This effort aims to promote representation augmentation via stabilizing the inflow of gradients to these blocks. Thus, the problems of representation collapse in regular Transformers can be tackled. To evaluate our AaTs, we compare the models with the state-of-the-art (SoTA) methods on three benchmark indoor localization datasets. The experimental results confirm our hypothesis and show that our proposed models could deliver much higher and more stable accuracy.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000385/pdfft?md5=119bfe7ecffea68a2c6b1240cc6ebda1&pid=1-s2.0-S1574119224000385-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140135031","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":"Privacy-preserving pedestrian tracking with path image inpainting and 3D point cloud features","authors":"Masakazu Ohno,&nbsp;Riki Ukyo,&nbsp;Tatsuya Amano,&nbsp;Hamada Rizk,&nbsp;Hirozumi Yamaguchi","doi":"10.1016/j.pmcj.2024.101914","DOIUrl":"10.1016/j.pmcj.2024.101914","url":null,"abstract":"<div><p>Tracking pedestrian flow in large public areas is vital, yet ensuring privacy is paramount. Traditional visual-based tracking systems are raising concerns for potentially obtaining persistent and permanent identifiers that can compromise individual identities. Moreover, in areas such as the vicinity of restrooms, any form of data acquisition capturing human behavior should be refrained from, making it also crucial to appropriately address and complement these blind spots for a comprehensive analysis of pedestrian movement in the entire area. In this paper, we present our pedestrian tracking algorithm using distributed 3D LiDARs (Light Detection and Ranging), which capture pedestrians as 3D point clouds, omitting identifiable features. Our system bridges blind spots by leveraging historical movement data and 3D point cloud features, complemented by a generative diffusion model to predict trajectories in unseen areas. In a large-scale testbed with 70 LiDARs, the system achieved a 0.98 F-measure, highlighting its potential as a leading privacy-preserving tracking solution.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000403/pdfft?md5=d59e7b592fa6f7f65168d1cbc0adb7a2&pid=1-s2.0-S1574119224000403-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105444","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 comprehensive survey on Machine Learning techniques in opportunistic networks: Advances, challenges and future directions","authors":"Jay Gandhi,&nbsp;Zunnun Narmawala","doi":"10.1016/j.pmcj.2024.101917","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101917","url":null,"abstract":"<div><p>Machine Learning (ML) is growing in popularity and is applied in numerous fields to solve complex problems. Opportunistic Networks are a type of Ad-hoc Network where a contemporaneous path does not always exist. So, forwarding methods that assume the availability of contemporaneous paths does not work. ML techniques are applied to resolve the fundamental problems in Opportunistic Networks, including contact probability, link prediction, making a forwarding decision, friendship strength, and dynamic topology. The paper summarises different ML techniques applied in Opportunistic Networks with their benefits, research challenges, and future opportunities. The study provides insight into the Opportunistic Networks with ML and motivates the researcher to apply ML techniques to overcome various challenges in Opportunistic Networks.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140135032","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 framework for offloading and migration of serverless functions in the Edge–Cloud Continuum","authors":"Gabriele Russo Russo,&nbsp;Valeria Cardellini,&nbsp;Francesco Lo Presti","doi":"10.1016/j.pmcj.2024.101915","DOIUrl":"10.1016/j.pmcj.2024.101915","url":null,"abstract":"<div><p>Function-as-a-Service (FaaS) has emerged as an evolution of traditional Cloud service models, allowing users to define and execute pieces of codes (i.e., functions) in a serverless manner, with the provider taking care of most operational issues. With the unending growth of resource availability in the Edge-to-Cloud Continuum, there is increasing interest in adopting FaaS near the Edge as well, to better support geo-distributed and pervasive applications. However, as the existing FaaS frameworks have mostly been designed with Cloud in mind, new architectures are necessary to cope with the additional challenges of the Continuum, such as higher heterogeneity, network latencies, limited computing capacity.</p><p>In this paper, we present an extended version of Serverledge, a FaaS framework designed to span Edge and Cloud computing landscapes. Serverledge relies on a decentralized architecture, where each FaaS node is able to autonomously schedule and execute functions. To take advantage of the computational capacity of the infrastructure, Serverledge nodes also rely on horizontal and vertical function offloading mechanisms. In this work we particularly focus on the design of mechanisms for function offloading and live function migration across nodes. We implement these mechanisms in Serverledge and evaluate their impact and performance considering different scenarios and functions.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000415/pdfft?md5=0b746bfff0acade4c42c5e021cec20da&pid=1-s2.0-S1574119224000415-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105442","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":"Inferring in-air gestures in complex indoor environment with less supervision","authors":"Zhongkai Deng ,&nbsp;Qizhen Zhou ,&nbsp;Jianchun Xing ,&nbsp;Qiliang Yang ,&nbsp;Yin Chen ,&nbsp;Hu Zhang ,&nbsp;Zhaoyi Chen ,&nbsp;Deyu Deng ,&nbsp;Yixin Mo ,&nbsp;Bowei Feng","doi":"10.1016/j.pmcj.2024.101904","DOIUrl":"10.1016/j.pmcj.2024.101904","url":null,"abstract":"<div><p>People have high demands for comfort and technology in indoor environments. Gestures, as a natural and friendly human computer interaction (HCI) method, have received widespread attention and have been the subject of many research studies. Traditional approaches are based on wearable devices and cameras, which can be cumbersome to operate and infringe upon users’ privacy. Millimeter-wave (mmWave) radar avoids these problems by detecting gestures in a noninvasive manner. However, it encounters practical challenges in complex indoor environments, such as dynamic disturbance from surroundings, variable usage conditions and diverse gesture patterns, which conventionally require considerable manual effort to address. In this paper, we attempt to minimize human supervision and propose a noninvasive gesture recognition method named RaGe that involves a commercial mmWave indoor radar. First, a parameter optimization framework considering gesture prior constraints is proposed for radar configuration, which functions to weaken the disturbance from surroundings. Second, we alleviate data shortages in variable usage conditions and achieve low-cost data augmentation by applying affine transformations. Third, we combine deformable convolution operations with an unsupervised attention mechanism, thus exploring the intrinsic features involved in diverse gesture patterns. Experimental results show that RaGe is able to recognize 7 gestures with 99.3% accuracy and less human supervision, surpassing the state-of-the-art methods in comparative experiments.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105443","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":"An evaluation of heart rate monitoring with in-ear microphones under motion","authors":"Kayla-Jade Butkow ,&nbsp;Ting Dang ,&nbsp;Andrea Ferlini ,&nbsp;Dong Ma ,&nbsp;Yang Liu ,&nbsp;Cecilia Mascolo","doi":"10.1016/j.pmcj.2024.101913","DOIUrl":"10.1016/j.pmcj.2024.101913","url":null,"abstract":"<div><p>With the soaring adoption of in-ear wearables, the research community has started investigating suitable in-ear heart rate detection systems. Heart rate is a key physiological marker of cardiovascular health and physical fitness. Continuous and reliable heart rate monitoring with wearable devices has therefore gained increasing attention in recent years. Existing heart rate detection systems in wearables mainly rely on photoplethysmography (PPG) sensors, however, these are notorious for poor performance in the presence of human motion. In this work, leveraging the occlusion effect that enhances low-frequency bone-conducted sounds in the ear canal, we investigate for the first time <em>in-ear audio-based motion-resilient</em> heart rate monitoring. We first collected heart rate-induced sounds in the ear canal using an in-ear microphone under seven stationary activities and two full-body motion activities (i.e., walking, and running). Then, we devised a novel deep learning based motion artefact (MA) mitigation framework to denoise the in-ear audio signals, followed by a heart rate estimation algorithm to extract heart rate. With data collected from 15 subjects over nine activities, we demonstrate that hEARt, our end-to-end approach, achieves a mean absolute error (MAE) of 1.88 ± 2.89 BPM, 6.83 ± 5.05 BPM, and 13.19 ± 11.37 BPM for stationary, walking, and running, respectively, opening the door to a new non-invasive and affordable heart rate monitoring with useable performance for daily activities. Not only does hEARt outperform previous in-ear heart rate monitoring work, but it outperforms reported in-ear PPG performance.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000397/pdfft?md5=1d87af00a83a5ca4188bd2b75b510b82&pid=1-s2.0-S1574119224000397-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140105447","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":"Role of IoT technologies in big data management systems: A review and Smart Grid case study","authors":"A.R. Al-Ali ,&nbsp;Ragini Gupta ,&nbsp;Imran Zualkernan ,&nbsp;Sajal K. Das","doi":"10.1016/j.pmcj.2024.101905","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101905","url":null,"abstract":"<div><p>Empowered by Internet of Things (IoT) and cloud computing platforms, the concept of smart cities is making a transition from conceptual models to development and implementation phases. Multiple smart city initiatives and services such as Smart Grid and Smart Meters have emerged that have led to the accumulation of massive amounts of energy big data. Big data is typically characterized by five distinct features namely, volume, velocity, variety, veracity, and value. To gain insights and to monetize big data, data has to be collected, stored, processed, analyzed, mined, and visualized. This paper identifies the primary layers of a big data architecture with start-of-the-art communication, storage, and processing technologies that can be utilized to gain meaningful insights and intelligence from big data. In addition, this paper gives an in-depth overview for research and development who intend to explore the various techniques and technologies that can be implemented for harnessing big data value utilizing the recent big data specific processing and visualization tools. Finally, a use case model utilizing the above mentioned technologies for Smart Grid is presented to demonstrate the energy big data road map from generation to monetization. Our key findings highlight the significance of selecting the appropriate big data tools and technologies for each layer of big data architecture, detailing their advantages and disadvantages. We pinpoint the critical shortcomings of existing works, particularly the lack of a unified framework that effectively integrates these layers for smart city applications. This gap presents both a challenge and an opportunity for future research, suggesting a need for more holistic and interoperable solutions in big data management and utilization.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140030431","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":"LICAPA: Lightweight collective attestation for physical attacks detection in highly dynamic networks","authors":"Ziyu Wang ,&nbsp;Cong Sun","doi":"10.1016/j.pmcj.2024.101903","DOIUrl":"10.1016/j.pmcj.2024.101903","url":null,"abstract":"<div><p>UAVs or vehicular networks have been extensively used in different domains. Such a system network consists of various heterogeneous and mobile devices operating autonomously and cooperatively to provide flexible services. However, ensuring devices’ runtime integrity has always been critical to such highly dynamic and disruptive networks. Collective attestation is a popular technique in ensuring service integrity on remote devices. However, the physical attacks pose significant threats to the enforcement of the runtime integrity, and the existing detection approaches raise a considerable number of false positives, which impede the robustness of the network. We propose LICAPA, a collective attestation framework for detecting physical attacks with high accuracy. LICAPA can detect a device under physical attack with the timestamps signed by other recently-attested devices. Such a proof-from-others mechanism provides more knowledge about the compromised device for physical attack detection. It reduces the potential false positives compared with the state-of-the-art approaches. LICAPA provides a physical-adversary-tolerant runtime device joining mechanism and a new attestation report aggregation scheme to reduce the storage and communication cost of the device. On the prototype implementation of the trust anchor, we evaluate LICAPA’s computational costs. The simulation results demonstrate LICAPA’s low communication cost and long resistance time against false detection on physical attack. LICAPA reduces the overall swarm attestation cost by over 20% compared with SALAD (<em>Secure and Lightweight Attestation of Highly Dynamic and Disruptive Networks</em>) and PASTA (<em>Practical Attestation Protocol for Autonomous Embedded Systems</em>).</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000294/pdfft?md5=16eb6fb6c8f2a44387364de5b0970a87&pid=1-s2.0-S1574119224000294-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139920781","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}