Pervasive and Mobile Computing最新文献

{"title":"Prioritization-based delay sensitive task offloading in SDN-integrated mobile IoT network","authors":"Simran Chaudhary,&nbsp;Fatema Kapadia,&nbsp;Avinesh Singh,&nbsp;Nidhi Kumari,&nbsp;Prasanta K. Jana","doi":"10.1016/j.pmcj.2024.101960","DOIUrl":"10.1016/j.pmcj.2024.101960","url":null,"abstract":"<div><p>Due to enormous growth of Internet of Things (IoT) in the last decade, the amount of data generated through smart devices is increasing exponentially. Fog computing has emerged as a potential technology to deal such a huge volume of data in which task offloading is the most important aspect which has attracted significant attention. Many research works have been carried out, however, task offloading with latency sensitivity, reliability and result migration over a mobile user environment is still not widely addressed. In this paper, we propose a method for delay-sensitive and fault minimized task offloading for service requests made through a mobile/vehicular end user environment implemented via Software Defined Network (SDN) controllers integrated with the fog layer. This is a novel multi-phased model involving determining the optimal number of SDN controllers, clustering of the fog nodes (FNs) on the basis of SDN proximities, task prioritization and Gravitational Search Algorithm (GSA) based target FN selection. The simulation outcomes of our proposed approach show that there is a reduction in delay by around 23%–30% and around 60%–80% lesser number of tasks unassigned in each round as compared to two base algorithms.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141398460","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":"IoT data encryption and phrase search-based efficient processing using a Fully Homomorphic-based SE (FHSE) scheme","authors":"S. Hamsanandhini,&nbsp;P. Balasubramanie","doi":"10.1016/j.pmcj.2024.101952","DOIUrl":"10.1016/j.pmcj.2024.101952","url":null,"abstract":"<div><p>In this study, the Efficient Multikeyword Fully Homomorphic Search Encryption (EMK-FHSE) model is proposed to improve cloud storage security for sensitive data. When fully homomorphic encryption (FHE) and search encryption (SE) technologies are coupled, Fully Homomorphic Search Encryption (FHSE) is a strategy that realizes the shared information's controlled privacy and search security. As more and more encrypted data is kept on cloud servers (CSs), a single-keyword SE approach may cause multiple keyword index duplication concerns, making it challenging for CSs to search for the encrypted information. To reduce these problems, a novel efficiency bottleneck has been developed. An Adaptive Privacy-Preserving Fuzzy Multi-Keyword Search (APPFMK) approach is presented to address the difficulties of low search effectiveness in a single-keyword searching strategy and the high processing cost of the existing multi-keyword schemes. Cloud servers (CS) hold enormous volumes of encrypted data, and the necessary encrypted index is transmitted to the closest edge node (EN) to enable multi-keyword searches and supported decryption. According to security research, the EMK-FHSE multi-keyword index is safe in distinguishability under chosen keyword attacks. The results section compares the proposed model's search, storage, trapdoor, calculation, storage and validation times to those of several other models. The proposed model could achieve the following values: 60.81 kb for storage, 10.92 for the trapdoor, 6.85 ms for search, 0.44 ms for computation cost by changing the keyword in a trapdoor, 156.31 ms for computation cost by changing the keyword in a dictionary, 0.44 kb for storage cost by changing the keyword in a trapdoor, 1.81 kb for storage cost by changing the keyword in a dictionary and 0.016seconds for verification time, respectively.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141399409","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 toolkit for localisation queries","authors":"Gabriele Marini ,&nbsp;Jorge Goncalves ,&nbsp;Eduardo Velloso ,&nbsp;Raja Jurdak ,&nbsp;Vassilis Kostakos","doi":"10.1016/j.pmcj.2024.101946","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101946","url":null,"abstract":"<div><p>While UbiComp research has steadily improved the performance of localisation systems, the analysis of such datasets remains largely unaddressed. In this paper, we present a tool to facilitate querying and analysis of localisation time-series with a focus on semantic localisation. Drawing on well-established models to represent movement and mobility, we first develop a query language for localisation datasets. We then develop a software library in R that implements this querying. We use case studies to demonstrate how our programming tool can be used to query localisation datasets. Our work addresses an important gap in localisation research, by providing a flexible tool that can model and analyse localisation data programmatically and in real time.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000725/pdfft?md5=a76dc096127a400e97ecb6f76c49be0a&pid=1-s2.0-S1574119224000725-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141324735","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":"OcAPO: Fine-grained occupancy-aware, empirically-driven PDC control in open-plan, shared workspaces","authors":"Anuradha Ravi ,&nbsp;Dulaj Sanjaya Weerakoon ,&nbsp;Archan Misra","doi":"10.1016/j.pmcj.2024.101945","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101945","url":null,"abstract":"<div><p>Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a <span><math><mrow><mn>1000</mn><mspace></mspace><msup><mrow><mi>m</mi></mrow><mrow><mn>2</mn></mrow></msup></mrow></math></span> open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle two key practical challenges: (a) the zone-level (i.e., occupant-experienced) temperature differs significantly, depending on occupancy levels, from that measured by the ceiling-mounted thermal sensors that drive the PDC control loop, (b) sparsely deployed sensors are unable to capture the often-significant differences in ambient temperature across neighboring zones. Using extensive real-world coarser-grained measurement data (collected over 60 days under varying occupancy conditions), (a) we first uncover the various parameters that affect the occupant-level ambient temperature, and then (b) devise a trace-based model that helps identify the optimum combination of PDC setpoints, collectively across multiple zones, while accommodating variations in the occupancy levels and weather conditions. Using this trace-based model, our <em>OcAPO</em> system can assure ambient temperature experienced by occupants within a tolerance of <span><math><mrow><mspace></mspace><mn>0</mn><mo>.</mo><mn>3</mn><mspace></mspace><mo>°</mo><mi>C</mi></mrow></math></span>. In contrast, the existing approach of occupancy-agnostic, rule-based setpoint control violates this tolerance interval more than 80% of the time. However, this initial model requires unnecessary and continual database lookups and is unable to derive finer-grained setpoints, thereby potentially missing opportunities for additional energy savings. We thus collected data for another 15 days, with finer-grained setpoint control in increments of 0.2<span><math><msup><mrow></mrow><mrow><mo>∘</mo></mrow></msup></math></span> under varying occupancy conditions in the second phase. To determine PDC setpoints efficiently, we subsequently used the empirical data to train a KNN-based regression model. Additional studies on our real-world testbed demonstrate the regressor-based <em>OcAPO</em> approach is able to assure occupant-level ambient temperature within a narrow <span><math><mrow><mspace></mspace><mn>0</mn><mo>.</mo><mn>2</mn><mspace></mspace><mo>°</mo><mi>C</mi></mrow></math></span> tolerance. We also demonstrate that the regression version of <em>OcAPO</em> can reduce the opening percentage of PDC valves (an in","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":3.0,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479492","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 data minimization and anonymity in pervasive mobile-to-mobile recommender systems","authors":"Tobias Eichinger,&nbsp;Axel Küpper","doi":"10.1016/j.pmcj.2024.101951","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101951","url":null,"abstract":"<div><p>Data minimization is a legal principle that mandates limiting the collection of personal data to a necessary minimum. In this context, we address ourselves to pervasive mobile-to-mobile recommender systems in which users establish ad hoc wireless connections between their mobile computing devices in physical proximity to exchange ratings that represent personal data on which they calculate recommendations. The specific problem is: How can users minimize the collection of ratings over all users while only being able to communicate with a subset of other users in physical proximity? A main difficulty is the mobility of users, which prevents, for instance, the creation and use of an overlay network to coordinate data collection. Users, therefore, have to decide whether to exchange ratings and how many when an ad hoc wireless connection is established. We model the randomness of these connections and apply an algorithm based on distributed gradient descent to solve the distributed data minimization problem at hand. We show that the algorithm robustly produces the least amount of connections and also the least amount of collected ratings compared to an array of baselines. We find that this simultaneously reduces the chances of an attacker relating users to ratings. In this sense, the algorithm also preserves the anonymity of users, yet only of those users who do not establish an ad hoc wireless connection with each other. Users who do establish a connection with each other are trivially not anonymous toward each other. We find that users can further minimize data collection and preserve their anonymity if they aggregate multiple ratings on the same item into a single rating and change their identifiers between connections.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000774/pdfft?md5=a223e1b154eb947d9484c66aff1d4dfa&pid=1-s2.0-S1574119224000774-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141290523","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":"Federated learning energy saving through client selection","authors":"Filipe Maciel ,&nbsp;Allan M. de Souza ,&nbsp;Luiz F. Bittencourt ,&nbsp;Leandro A. Villas ,&nbsp;Torsten Braun","doi":"10.1016/j.pmcj.2024.101948","DOIUrl":"10.1016/j.pmcj.2024.101948","url":null,"abstract":"<div><p>Contemporary applications leverage machine learning models to optimize performance, often necessitating data transmission to a remote server for training. However, this approach entails significant resource consumption. A privacy concern arises, which Federated Learning addresses through a cyclical process involving in-device training (local model update) and subsequent reporting to the server for aggregation (global model update). In each iteration of this cycle, termed a communication round, a client selection component determines participant devices contributing to global model enhancement. However, existing literature inadequately addresses scenarios where optimized energy consumption is imperative. This paper introduces an Energy Saving Client Selection (ESCS) mechanism, considering decision criteria such as battery level, training time capacity, and network quality. As a pertinent use case, classification scenarios are utilized to compare the performance of ESCS against other state-of-the-art approaches. The findings reveal that ESCS effectively conserves energy while maintaining optimal performance. This research contributes to the ongoing discourse on energy-efficient client selection strategies within the domain of Federated Learning.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141143514","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":"Passive Monitoring of Dangerous Driving Behaviors Using mmWave Radar","authors":"Argha Sen,&nbsp;Avijit Mandal,&nbsp;Prasenjit Karmakar,&nbsp;Anirban Das,&nbsp;Sandip Chakraborty","doi":"10.1016/j.pmcj.2024.101949","DOIUrl":"10.1016/j.pmcj.2024.101949","url":null,"abstract":"<div><p>Detecting risky driving has been a significant area of focus in recent years. Nonetheless, devising a practical, effective, and unobtrusive solution remains a complex challenge. Presently available technologies predominantly rely on visual cues or physical proximity, complicating the sensing. With this incentive, we explore the possibility of utilizing mmWave radars exclusively to identify dangerous driving behaviors. Initially, we scrutinize the attributes of unsafe driving and pinpoint distinct patterns in range-doppler readings brought about by nine common risky driving manoeuvres. Subsequently, we create an innovative Fused-CNN model that identifies instances of hazardous driving amidst regular driving and categorizes nine distinct types of dangerous driving actions. After conducting thorough experiments involving seven volunteers driving in real-world settings, we note that our system accurately distinguishes risky driving actions with an average precision of approximately 97% with a deviation of <span><math><mrow><mo>±</mo><mn>2</mn><mtext>%</mtext></mrow></math></span>. To underscore the significance of our approach, we also compare it against established state-of-the-art methods.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132231","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":"CIU-L: A class-incremental learning and machine unlearning passive sensing system for human identification","authors":"Zhongcheng Wei ,&nbsp;Wei Chen ,&nbsp;Yunping Zhang ,&nbsp;Bin Lian ,&nbsp;Jijun Zhao","doi":"10.1016/j.pmcj.2024.101947","DOIUrl":"10.1016/j.pmcj.2024.101947","url":null,"abstract":"<div><p>With the development of passive sensing technology, WiFi-based identification research has attracted much attention in areas such as human–computer interaction and home security. Although WiFi sensing-based human identification has achieved initial success, it is currently mainly applicable to scenarios where the user’s identity category is fixed and not applicable to scenarios where the user’s identity category changes frequently. In this paper, we propose an identification system (CIU-L) in a scenario where user’s identity categories frequently change, allowing for incremental registration and unregistration of identity categories. To the best of our knowledge, this is the first attempt to register and unregister user identity information under the previous identity category constraints. CIU-L proposes a training and updating strategy in the registration phase of new user to avoid catastrophic forgetting of old user’s identity information, and trains a targeted noise for the user to be unregistered in the unregistration phase of old user, achieving precise removal of the user to be unregistered without affecting the retained users. In addition, this paper presents adequate comparative experiments of CIU-L with other systems in the user identity category fixing scenario. The experimental results show that the average difference between CIU-L and other systems in terms of Accuracy, Precision, Recall and F1-Score is within 5% of each other, while running time and storage space are saved by more than 6 times, which is more capable of meeting the needs of identity recognition in real scenarios.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141132419","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":"BmmW: A DNN-based joint BLE and mmWave radar system for accurate 3D localization with goal-oriented communication","authors":"Peizheng Li ,&nbsp;Jagdeep Singh ,&nbsp;Han Cui ,&nbsp;Carlo Alberto Boano","doi":"10.1016/j.pmcj.2024.101944","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101944","url":null,"abstract":"<div><p>Bluetooth Low Energy (BLE) has emerged as one of the reference technologies for the development of indoor localization systems, due to its increasing ubiquity, low-cost hardware, and to the introduction of direction-finding enhancements improving its ranging performance. However, the intrinsic narrowband nature of BLE makes this technology susceptible to multipath and channel interference. As a result, it is still challenging to achieve decimetre-level localization accuracy, which is necessary when developing location-based services for smart factories and workspaces. To address this challenge, we present BmmW, an indoor localization system that augments the ranging estimates obtained with BLE<!--> <!--> <!-->5.1’s constant tone extension feature with mmWave radar measurements to provide 3D localization of a mobile tag with decimetre-level accuracy. Specifically, BmmW embeds a deep neural network (DNN) that is jointly trained with both BLE and mmWave measurements, practically leveraging the strengths of both technologies. In fact, mmWave radars can locate objects and people with decimetre-level accuracy, but their effectiveness in monitoring stationary targets and multiple objects is limited, and they also suffer from a fast signal attenuation limiting the usable range to a few meters. We evaluate BmmW’s performance experimentally, and show that its joint DNN training scheme allows to track mobile tags with a mean 3D localization accuracy of 10 cm when combining angle-of-arrival BLE measurements with mmWave radar data. We further assess two variations of BmmW: BmmW-<span>Lite</span> and BmmW-<span>Lite+</span>, both tailored for single-antenna BLE devices, which eliminates the necessity for bulky and expensive multi-antenna arrays and represents a cost-effective solution that is easy to integrate into compact IoT devices. In contrast to classic BmmW (which utilizes angle-of-arrival info), BmmW-<span>Lite</span> uses raw in-phase/quadrature (I/Q) measurements, and achieves a mean localization accuracy of 36 cm, thus facilitating precise object tracking in indoor environments even when using budget-friendly single-antenna BLE devices. BmmW-<span>Lite+</span> extends BmmW-<span>Lite</span> by allowing the localization task to be transferred from the edge to the cloud due to device memory and power constraints. To this end, BmmW-<span>Lite+</span> employs a goal-oriented communication paradigm that compresses initial BLE features into a more compact <em>semantic</em> format at the edge device, which allows to minimize the amount of data that needs to be sent to the cloud. Our experimental results show that BmmW-<span>Lite+</span> can compress raw BLE features by up to 12% of their initial size (hence allowing to save network bandwidth and minimize energy consumption), with negligible impact on the localization accuracy.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141095201","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":"Analysis of micro- vs. macro-flows management in QKD-secured edge computing","authors":"Claudio Cicconetti,&nbsp;Marco Conti,&nbsp;Andrea Passarella","doi":"10.1016/j.pmcj.2024.101937","DOIUrl":"10.1016/j.pmcj.2024.101937","url":null,"abstract":"<div><p>Quantum Key Distribution (QKD) holds the promise of a secure exchange of cryptographic material between applications that have access to the same network of QKD nodes, interconnected through fiber optic or satellite links. Worldwide several such networks are being deployed at a metropolitan level, where edge computing is already offered by the telco operators to customers as a viable alternative to both cloud and on-premise hosting of computational resources. In this paper, we investigate the implications of enabling QKD for edge-native applications from a practical perspective of resource allocation in the QKD network and the edge infrastructure. Specifically, we consider the dichotomy between aggregating all the applications on the same source–destination path vs. adopting a more flexible micro-flow approach, inspired from Software Defined Networking (SDN) concepts. Our simulation results show that there is a fundamental trade-off between the efficient use of resources and the signaling overhead, which we managed to diminish with the use of suitable hybrid solutions.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000634/pdfft?md5=66f40de1122375679c5bab3134cbf374&pid=1-s2.0-S1574119224000634-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141046172","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}