Pervasive and Mobile Computing最新文献

{"title":"GTDIM: Grid-based Two-stage Dynamic Incentive Mechanism for Mobile Crowd Sensing","authors":"Xin-Wei Yao ,&nbsp;Wei-Wei Xing ,&nbsp;Ke-Chen Zheng ,&nbsp;Chu-Feng Qi ,&nbsp;Xiang-Yang Li ,&nbsp;Qi Song","doi":"10.1016/j.pmcj.2024.101964","DOIUrl":"10.1016/j.pmcj.2024.101964","url":null,"abstract":"<div><p>Mobile Crowd Sensing (MCS) technology, as an emerging data collection paradigm, offers distinct advantages, particularly in applications like smart city management. However, existing researches inadequately address the comprehensive solution to the problem of reliable task allocation according to the requirements such as task budget, sensory data quality, and real-time data collection, especially under varying participant engagement in MCS systems. To bridge this gap, we propose the Grid-based Two-stage Dynamic Incentive Mechanism (GTDIM). In the first stage, the Candidate Participant Set (CPS) establishment phase, participants receive compensation for collecting sensory data when a sufficient number are available. When participants are insufficient, additional rewards inspired by the grid division of sensing areas are progressively offered to attract more participants. In the subsequent stage, utilizing the established CPS, participants are selected through a greedy algorithm based on the newly devised Participant Matching Index (PMI), which integrates various participant features. Extensive simulation results reveal the impact of PMI on participant selection. Numerical findings conclusively demonstrate GTDIM’s superior performance over baseline incentive mechanisms in terms of task assignment ratio, participant payment, and especially when dealing with larger sensing tasks.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101964"},"PeriodicalIF":3.0,"publicationDate":"2024-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141711433","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":"WiCAR: A class-incremental system for WiFi activity recognition","authors":"Zhihua Li ,&nbsp;Shuli Ning ,&nbsp;Bin Lian ,&nbsp;Chao Wang ,&nbsp;Zhongcheng Wei","doi":"10.1016/j.pmcj.2024.101963","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101963","url":null,"abstract":"<div><p>The proposal of Integrated Sensing and Communications has once again drawn researchers’ attention to WiFi sensing, propelling applications based on WiFi sensing into an advanced stage. However, the current field of activity recognition only identifies fixed categories of activities, neglecting the growing demand for perceiving activity types in real applications over time. In response to the issue, we present WiCAR, a WiFi activity recognition system designed for class incremental scenarios. WiCAR takes antenna array-fused image data as input, employing the Wi-RA model with parallel stacked activation functions as its backbone network. To alleviate the typical catastrophic forgetting issue in class-incremental learning, WiCAR employs a strategy of replaying known data. Additionally, we adopts knowledge distillation to improve accuracy among old samples during the incremental process. To tackle the imbalance in the number of samples between old and new classes, the model is updated through weight alignment. This serious of strategies endows the system with the capability to progressively learn and handle new classes. We conducted extensive experiments to evaluate the system performance. The experimental results demonstrate that our system exhibits excellent performance regardless of the number of tasks, whether tasks are uniform or non-uniform, and the order of task arrivals. The highest average accuracy reaches 96.429%, and even in the presence of six incremental stages, the average accuracy remains at 92.867%.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101963"},"PeriodicalIF":3.0,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141540006","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":"Intelligent defense strategies: Comprehensive attack detection in VANET with deep reinforcement learning","authors":"Rukhsar Sultana,&nbsp;Jyoti Grover,&nbsp;Meenakshi Tripathi","doi":"10.1016/j.pmcj.2024.101962","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101962","url":null,"abstract":"<div><p>Vehicular Ad Hoc Network (VANET) facilitates the exchange of vehicular information through Vehicle-to-Vehicle (V2V) communication, contributing to Cooperative Intelligent Transportation Systems (C-ITS). The transmitted messages among vehicles are vulnerable to various security threats executed by malicious insider nodes. The dynamic VANET necessitates context-aware solutions for detecting various security attacks. Existing learning and deterministic mechanisms showed high detection accuracy for attacks on which they were trained explicitly for large datasets. Therefore, we propose an intelligent framework utilizing Deep Reinforcement Learning (DRL) for attack detection in evolving scenarios and mitigate the need for extensive training datasets. Our approach employs a Deep Q Network (DQN) trained on a compact dataset encompassing multiple attacks. The trained model is then applied to an unknown and extensive dataset, detecting various attacks with high accuracy. Notably, the model autonomously updates itself upon observing changes in the network context. This framework represents a promising security solution that is effective and adaptable for V2V communication in VANET.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101962"},"PeriodicalIF":3.0,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141480439","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":"Localizing unknown nodes with an FPGA-enhanced edge computing UAV in wireless sensor networks: Implementation and evaluation","authors":"Rahma Mani ,&nbsp;Antonio Rios-Navarro ,&nbsp;Jose Luis Sevillano Ramos ,&nbsp;Noureddine Liouane","doi":"10.1016/j.pmcj.2024.101961","DOIUrl":"https://doi.org/10.1016/j.pmcj.2024.101961","url":null,"abstract":"<div><p>Great interest is directed toward real-time applications to determine the exact location of sensor nodes deployed in an area of interest. In this paper, we present a novel approach using a combination of the Kalman filter and regularized bounding box method for localizing unknown nodes in an area using an FPGA-enhanced edge computing UAV whose trajectory is known and is represented as the position of many anchors. The UAV is equipped with a GPS system that allows it to gather location data of sensor nodes as it moves around its environment. We employ a regularized bounding box to predict the positions of the unknown nodes using regularization factors and we use the Kalman filter algorithm to smooth and improve the accuracy of the sensor nodes to be localized. In order to localize the unknown nodes, the UAV receives the number of hops from each node and uses this information as input to the localization algorithm. Furthermore, the use of an FPGA board allows for real-time processing of sensory data, enabling the UAV to make fast and accurate decisions in dynamic environments. The localization algorithm was implemented on the FPGA board “Zynq MiniZed 7007s evaluation board” using Xilinx blocks in Simulink, and the generated code was converted into VHDL using Xilinx System Generator. The algorithm was simulated and synthesized using “Vivado” software. In fact, the proposed system was evaluated by comparing the performances achieved through two different implementations: Hardware and Software implementation. In effect, the performance of FPGA hardware implementation presents a new achievement in localization due to its easy testing and fast implementation. Our results show that this approach can efficiently locate unknown nodes with good latency and high accuracy. In fact, the execution time of the FPGA-integrated algorithm is reduced by about 60 times compared to the software implementation and the power consumption is about 100 mW, which proves the suitability of FPGA for localization in WSNs, offering a promising solution for various mobile WSN applications.</p></div>","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101961"},"PeriodicalIF":3.0,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574119224000865/pdfft?md5=4248b9a002154820ef25d24827509c64&pid=1-s2.0-S1574119224000865-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141479544","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":"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":"103 ","pages":"Article 101960"},"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":"103 ","pages":"Article 101952"},"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":"103 ","pages":"Article 101946"},"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":"&lt;div&gt;&lt;p&gt;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 &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mn&gt;1000&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;msup&gt;&lt;mrow&gt;&lt;mi&gt;m&lt;/mi&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; 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 &lt;em&gt;OcAPO&lt;/em&gt; system can assure ambient temperature experienced by occupants within a tolerance of &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;3&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt;. 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&lt;span&gt;&lt;math&gt;&lt;msup&gt;&lt;mrow&gt;&lt;/mrow&gt;&lt;mrow&gt;&lt;mo&gt;∘&lt;/mo&gt;&lt;/mrow&gt;&lt;/msup&gt;&lt;/math&gt;&lt;/span&gt; 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 &lt;em&gt;OcAPO&lt;/em&gt; approach is able to assure occupant-level ambient temperature within a narrow &lt;span&gt;&lt;math&gt;&lt;mrow&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mn&gt;0&lt;/mn&gt;&lt;mo&gt;.&lt;/mo&gt;&lt;mn&gt;2&lt;/mn&gt;&lt;mspace&gt;&lt;/mspace&gt;&lt;mo&gt;°&lt;/mo&gt;&lt;mi&gt;C&lt;/mi&gt;&lt;/mrow&gt;&lt;/math&gt;&lt;/span&gt; tolerance. We also demonstrate that the regression version of &lt;em&gt;OcAPO&lt;/em&gt; can reduce the opening percentage of PDC valves (an in","PeriodicalId":49005,"journal":{"name":"Pervasive and Mobile Computing","volume":"103 ","pages":"Article 101945"},"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":"103 ","pages":"Article 101951"},"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":"103 ","pages":"Article 101948"},"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}