IEEE Transactions on Mobile Computing最新文献

Efficient Coordination of Federated Learning and Inference Offloading at the Edge: A Proactive Optimization Paradigm

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-24 DOI: 10.1109/TMC.2024.3466844

Ke Luo;Kongyange Zhao;Tao Ouyang;Xiaoxi Zhang;Zhi Zhou;Hao Wang;Xu Chen

{"title":"Efficient Coordination of Federated Learning and Inference Offloading at the Edge: A Proactive Optimization Paradigm","authors":"Ke Luo;Kongyange Zhao;Tao Ouyang;Xiaoxi Zhang;Zhi Zhou;Hao Wang;Xu Chen","doi":"10.1109/TMC.2024.3466844","DOIUrl":"https://doi.org/10.1109/TMC.2024.3466844","url":null,"abstract":"Benefiting from hardware upgrades and deep learning techniques, more and more end devices can independently support a variety of intelligent applications. Further powered by edge computing technologies, the end-edge collaboration paradigm becomes one mainstream approach for achieving advanced edge intelligence (EI). To fully exploit the system resources, it is desirable to coordinate diverse EI services efficiently. Thus, we present a novel framework to jointly optimize the cost-performance trade-off for two distinct but typical EI services, where end devices simultaneously perform federated learning (FL) model training and conduct model inference with the assistance of edge offloading. However, balancing the long-term cost-performance trade-off is highly non-trivial, especially in the absence of knowledge of future system dynamics. Moreover, the capacity heterogeneity further increases the difficulty of service coordination among resource-limited end devices. To overcome these challenges, we first analyze the optimality of inference offloading decisions with and without FL model training and quantify their mutual effects due to local resource contention. By incorporating the loss estimation of FL training model, we then propose a novel proactive policy with theoretical guarantees, which proactively controls the stopping of FL training procedure to balance well the trade-offs between FL model performance and resource costs while fulfilling the inference performance requirements. Extensive results show the efficiency and robustness of our proposed algorithm for EI service coordination in dynamic end-edge collaboration scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"407-421"},"PeriodicalIF":7.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777767","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multi-User Task Offloading in UAV-Assisted LEO Satellite Edge Computing: A Game-Theoretic Approach

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-24 DOI: 10.1109/TMC.2024.3465591

Ying Chen;Jie Zhao;Yuan Wu;Jiwei Huang;Xuemin Sherman Shen

{"title":"Multi-User Task Offloading in UAV-Assisted LEO Satellite Edge Computing: A Game-Theoretic Approach","authors":"Ying Chen;Jie Zhao;Yuan Wu;Jiwei Huang;Xuemin Sherman Shen","doi":"10.1109/TMC.2024.3465591","DOIUrl":"https://doi.org/10.1109/TMC.2024.3465591","url":null,"abstract":"Unmanned Aerial Vehicle (UAV)-assisted Low Earth Orbit (LEO) satellite edge computing (ULSE) networks can address the challenge communications issues in areas with harsh terrain and achieve global wireless coverage to provide services for mobile user devices (MUDs). This paper studies the LEO-UAV task offloading problem where MUDs compete for limited resources in the ULSE networks. We formulate the optimization problem with the goal of minimizing the cost of all MUDs while meeting resource constraint and satellite coverage time constraint. We first theoretically prove that this problem is NP-hard. We then reformulate the problem as a LEO-UAV task offloading game (LUTO-Game), and show that there is at least one Nash equilibrium solution for the LUTO-Game. We propose a joint UAV and LEO satellite task offloading (JULTO) algorithm to obtain the Nash equilibrium offloading strategy, and analyze the performance of the worst-case offloading strategy obtained by the JULTO algorithm. Finally, extensive experiments, including convergence analysis and comparison experiments, are carried out to validate the effectiveness of our JULTO algorithm.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"363-378"},"PeriodicalIF":7.7,"publicationDate":"2024-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789036","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Model Decomposition and Reassembly for Purified Knowledge Transfer in Personalized Federated Learning

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-23 DOI: 10.1109/TMC.2024.3466227

Jie Zhang;Song Guo;Xiaosong Ma;Wenchao Xu;Qihua Zhou;Jingcai Guo;Zicong Hong;Jun Shan

{"title":"Model Decomposition and Reassembly for Purified Knowledge Transfer in Personalized Federated Learning","authors":"Jie Zhang;Song Guo;Xiaosong Ma;Wenchao Xu;Qihua Zhou;Jingcai Guo;Zicong Hong;Jun Shan","doi":"10.1109/TMC.2024.3466227","DOIUrl":"https://doi.org/10.1109/TMC.2024.3466227","url":null,"abstract":"Personalized federated learning (pFL) is to collaboratively train non-identical machine learning models for different clients to adapt to their heterogeneously distributed datasets. State-of-the-art pFL approaches pay much attention on exploiting clients’ inter-similarities to facilitate the collaborative learning process, meanwhile, can barely escape from the irrelevant knowledge pooling that is inevitable during the aggregation phase, and thus hindering the optimization convergence and degrading the personalization performance. To tackle such conflicts between facilitating collaboration and promoting personalization, we propose a novel pFL framework, dubbed pFedC, to first decompose the global aggregated knowledge into several compositional branches, and then selectively reassemble the relevant branches for supporting conflicts-aware collaboration among contradictory clients. Specifically, by reconstructing each local model into a shared feature extractor and multiple decomposed task-specific classifiers, the training on each client transforms into a mutually reinforced and relatively independent multi-task learning process, which provides a new perspective for pFL. Besides, we conduct a purified knowledge aggregation mechanism via quantifying the combination weights for each client to capture clients’ common prior, as well as mitigate potential conflicts from the divergent knowledge caused by the heterogeneous data. Extensive experiments over various models and datasets demonstrate the effectiveness and superior performance of the proposed algorithm.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"379-393"},"PeriodicalIF":7.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Scrava: Super Resolution-Based Bandwidth-Efficient Cross-Camera Video Analytics

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-23 DOI: 10.1109/TMC.2024.3461879

Yu Liang;Sheng Zhang;Jie Wu

{"title":"Scrava: Super Resolution-Based Bandwidth-Efficient Cross-Camera Video Analytics","authors":"Yu Liang;Sheng Zhang;Jie Wu","doi":"10.1109/TMC.2024.3461879","DOIUrl":"https://doi.org/10.1109/TMC.2024.3461879","url":null,"abstract":"Massively deployed cameras form a tightly connected network which generates video streams continuously. Benefiting from advances in computer vision, automated real-time analytics of video streams can be of practical value in various scenarios. As cameras become more dense, cross-camera video analytics has emerged. Combining video contents from multiple cameras for analytics is certainly more promising than single-camera analytics, which can realize cross-camera pedestrian tracking and cross-camera complex behavior recognition. Some works focused on optimization of cross-camera video analytic applications, but most of them ignore specific network situation between cameras and edge servers. Furthermore, most of them ignore the super resolution technique, which is proven to be a source of efficiency. In this paper, we first verify the potential gain of super resolution on cross-camera video analytic tasks. Then, we design and implement a cross-camera real-time video streaming analytic system, \u0000<inline-formula><tex-math>${mathsf {Scrava}}$</tex-math></inline-formula>\u0000, which leverages super resolution to augment low-resolution videos and simultaneously reduce bandwidth consumption. \u0000<inline-formula><tex-math>${mathsf {Scrava}}$</tex-math></inline-formula>\u0000 enables real-time cross-camera video analytics and enhances video segments with the SR module under poor network conditions. We take cross-camera pedestrian tracking as an example, and experimentally verifies the effectiveness of super resolution on real-time cross-camera video analytics. Compared with using low-resolution video segments, \u0000<inline-formula><tex-math>${mathsf {Scrava}}$</tex-math></inline-formula>\u0000 can improve the F1 score by 47.16%, verifying the feasibility of exploiting super resolution to improve the performance of real-time cross-camera video analytic systems.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"293-305"},"PeriodicalIF":7.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789077","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

FedCRAC: Improving Federated Classification Performance on Long-Tailed Data via Classifier Representation Adjustment and Calibration

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-23 DOI: 10.1109/TMC.2024.3466208

Xujing Li;Sheng Sun;Min Liu;Ju Ren;Xuefeng Jiang;Tianliu He

{"title":"FedCRAC: Improving Federated Classification Performance on Long-Tailed Data via Classifier Representation Adjustment and Calibration","authors":"Xujing Li;Sheng Sun;Min Liu;Ju Ren;Xuefeng Jiang;Tianliu He","doi":"10.1109/TMC.2024.3466208","DOIUrl":"https://doi.org/10.1109/TMC.2024.3466208","url":null,"abstract":"Federated learning has been a popular distributed training paradigm that enables to train a shared model with data privacy protection. However, non-Independent Identically Distribution and long-tailed data distribution characteristics across mobile devices results in evident performance degradation, especially for classification tasks. Although plenty of research studies devote to alleviating classification performance degradation caused by highly-skewed data distribution, they still cannot improve the distinguishability of model representation on hard-to-learn tail classes, and face obvious divergence of local classifiers in FL setting. To this end, we propose Federated Classifier Representation Adjustment and Calibration to improve the representation distinguishability of tail classes and achieve inter-client representation alignment with acceptable resource consumption on attaching operations. We first design a Class Similarity-Aware Margin matrix to enlarge class representation discrepancy and improve local classifier discriminability on tail classes during client-side local training process. To mitigate the divergence of local classifiers across clients, we further propose the Self Distillation Classifier Calibration to achieve the aggregated global classifier calibration with the assistance of generated pseudo representation samples via self-distillation manner. We conduct various experiments under wide-range long-tailed and heterogeneous data settings. Experimental results show that FedCRAC outperforms state-of-the-art methods in terms of accuracy and resource consumption.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"482-499"},"PeriodicalIF":7.7,"publicationDate":"2024-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142777573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Combinatorial Data Augmentation: A Key Enabler to Bridge Geometry- and Data-Driven WiFi Positioning

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-20 DOI: 10.1109/TMC.2024.3465510

Seung Min Yu;Kyuwon Han;Jihong Park;Seong-Lyun Kim;Seung-Woo Ko

{"title":"Combinatorial Data Augmentation: A Key Enabler to Bridge Geometry- and Data-Driven WiFi Positioning","authors":"Seung Min Yu;Kyuwon Han;Jihong Park;Seong-Lyun Kim;Seung-Woo Ko","doi":"10.1109/TMC.2024.3465510","DOIUrl":"https://doi.org/10.1109/TMC.2024.3465510","url":null,"abstract":"Due to the emergence of various wireless sensing technologies, numerous positioning algorithms have been introduced in the literature, categorized into \u0000<italic>geometry-driven positioning\u0000 (GP) and \u0000<italic>data-driven positioning\u0000 (DP). These approaches have respective limitations, e.g., a non-line-of-sight issue for GP and the lack of a high-dimensional and labeled dataset for DP, which could be complemented by integrating both methods. To this end, this paper aims to introduce a novel principle called \u0000<italic>combinatorial data augmentation\u0000 (CDA), a catalyst for the two approaches’ seamless integration. Specifically, GP-based data samples augmented from different positioning element combinations are called \u0000<italic>preliminary estimated locations\u0000 (PELs), which can be used as high-dimensional inputs for DP. We confirm the CDA’s effectiveness from field experiments based on WiFi \u0000<italic>round-trip times\u0000 (RTTs) and \u0000<italic>inertial measurement units\u0000 (IMUs) by designing several CDA-based positioning algorithms. First, we show that CDA offers various metrics quantifying each PEL’s reliability, thereby extracting important PELs for WiFi RTT positioning. Second, CDA helps compute the observation error covariance matrix of a Kalman filter for fusing two position estimates derived by WiFi RTTs and IMUs. Third, we use the important PELs and the above position estimate as the corresponding input feature and the real-time label for fingerprint-based positioning as a representative DP algorithm. It provides accurate and reliable positioning results, with an average positioning error of 1.58 (m) and a standard deviation of 0.90 (m).","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"306-320"},"PeriodicalIF":7.7,"publicationDate":"2024-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Romeo: Fault Detection of Rotating Machinery via Fine-Grained mmWave Velocity Signature

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-19 DOI: 10.1109/TMC.2024.3463955

Yanni Yang;Pengfei Hu;Jun Luo;Zhenlin An;Jiannong Cao;Dongxiao Yu;Xiuzhen Cheng

{"title":"Romeo: Fault Detection of Rotating Machinery via Fine-Grained mmWave Velocity Signature","authors":"Yanni Yang;Pengfei Hu;Jun Luo;Zhenlin An;Jiannong Cao;Dongxiao Yu;Xiuzhen Cheng","doi":"10.1109/TMC.2024.3463955","DOIUrl":"https://doi.org/10.1109/TMC.2024.3463955","url":null,"abstract":"Real-time velocity monitoring is pivotal for fault detection of rotating machinery. However, existing methods rely on either troublesome deployments of optical encoders and IMU sensors or various tachometers delivering coarse-grained velocity measurements insufficient for fault detection. To overcome these limitations, we propose \u0000Romeo\u0000 as the first work to exploit the mmWave radar for \u0000ro\u0000tating \u0000m\u0000achinery fault detection by extracting a fine-grained v\u0000e\u0000l\u0000o\u0000city signature. Though mmWave radars should capture instant rotation information with their claimed high sensitivity and sampling rate, direct adoption entails significant efforts for high-precision velocity measurement per radar to handle; particularly, exhausted system calibration and noise interference. To this end, we first develop a phase-velocity model to characterize the relationship between the mmWave signal phase and the fine-grained angular velocity. We then explore the geometric properties of specific positions in the rotation trajectory to precisely calibrate the rotation sensing model, leading to an iterative algorithm for accurate angular velocity measurement. Finally, we propose a simple yet effective fault detection algorithm by extracting a unique velocity signature. Our extensive experiments show \u0000Romeo\u0000 achieves a median error of 0.4\u0000<inline-formula><tex-math>$^circ$</tex-math></inline-formula>\u0000/s for fine-grained angular speed measurement, outperforming SOTA solutions with over ×16 angular speed granularity and ×7 measurement precision.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"227-242"},"PeriodicalIF":7.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789074","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge-Aware Parameter Coaching for Communication-Efficient Personalized Federated Learning in Mobile Edge Computing

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-19 DOI: 10.1109/TMC.2024.3464512

Mingjian Zhi;Yuanguo Bi;Lin Cai;Wenchao Xu;Haozhao Wang;Tianao Xiang;Qiang He

{"title":"Knowledge-Aware Parameter Coaching for Communication-Efficient Personalized Federated Learning in Mobile Edge Computing","authors":"Mingjian Zhi;Yuanguo Bi;Lin Cai;Wenchao Xu;Haozhao Wang;Tianao Xiang;Qiang He","doi":"10.1109/TMC.2024.3464512","DOIUrl":"https://doi.org/10.1109/TMC.2024.3464512","url":null,"abstract":"Personalized Federated Learning (pFL) can improve the accuracy of local models and provide enhanced edge intelligence without exposing the raw data in Mobile Edge Computing (MEC). However, in the MEC environment with constrained communication resources, transmitting the entire model between the server and the clients in traditional pFL methods imposes substantial communication overhead, which can lead to inaccurate personalization and degraded performance of mobile clients. In response, we propose a Communication-Efficient pFL architecture to enhance the performance of personalized models while minimizing communication overhead in MEC. First, a Knowledge-Aware Parameter Coaching method (KAPC) is presented to produce a more accurate personalized model by utilizing the layer-wise parameters of other clients with adaptive aggregation weights. Then, convergence analysis of the proposed KAPC is developed in both the convex and non-convex settings. Second, a Bidirectional Layer Selection algorithm (BLS) based on self-relationship and generalization error is proposed to select the most informative layers for transmission, which reduces communication costs. Extensive experiments are conducted, and the results demonstrate that the proposed KAPC achieves superior accuracy compared to the state-of-the-art baselines, while the proposed BLS substantially improves resource utilization without sacrificing performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"321-337"},"PeriodicalIF":7.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789044","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential Privacy

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-19 DOI: 10.1109/TMC.2024.3464744

Wenjie Chen;Haoyu Chen;Tingxuan Han;Wei Tong;Sheng Zhong

{"title":"Secure Two-Party Frequent Itemset Mining With Guaranteeing Differential Privacy","authors":"Wenjie Chen;Haoyu Chen;Tingxuan Han;Wei Tong;Sheng Zhong","doi":"10.1109/TMC.2024.3464744","DOIUrl":"https://doi.org/10.1109/TMC.2024.3464744","url":null,"abstract":"Frequent itemset mining is an essential task in data analysis. Therefore, it is crucial to design practical methods for privacy-preserving frequent itemset mining, enabling private data analysis. For two-party data analysis tasks, each party possesses its portion of the data and is reluctant to share the data with the other. While secure computation can enable two-party frequent itemset mining, the output of exact top-\u0000<inline-formula><tex-math>$k$</tex-math></inline-formula>\u0000 itemsets may still leave the adversary a chance to infer the sensitive information. Differential privacy has been utilized in various data analysis tasks to safeguard participating individuals. However, addressing how to ensure differential privacy for two-party frequent itemset mining remains unexplored. To prevent each party’s data from being leaked to the other while achieving differential privacy for releasing the output, this paper investigates the problem of differentially private two-party frequent itemset mining. We first propose a practical method that can efficiently select the frequent items of the union of two confidential databases in a differentially private way without the need to combine all elements. Then we extend this technique for general frequent itemset mining. Extensive experiments were conducted on real-world datasets, and the results show that the proposed method can achieve satisfactory utility with affordable overheads.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"276-292"},"PeriodicalIF":7.7,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142789157","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Charger Placement With Wave Interference 带波浪干扰的充电器安置

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-09-18 DOI: 10.1109/TMC.2024.3460403

Jing Xue;Die Wu;Jian Peng;Wenzheng Xu;Tang Liu

{"title":"Charger Placement With Wave Interference","authors":"Jing Xue;Die Wu;Jian Peng;Wenzheng Xu;Tang Liu","doi":"10.1109/TMC.2024.3460403","DOIUrl":"10.1109/TMC.2024.3460403","url":null,"abstract":"To guarantee the reliability for WRSNs, placing sufficient static chargers effectively ensures charging coverage for the entire network. However, this approach leads to a considerable number of sensors located within charging overlaps. The destructive wave interference caused by concurrent charging in these overlaps may weaken sensors received power, thereby negatively impacting charging performance. This work addresses a CHArging utIlity maximizatioN (CHAIN) problem, which aims to maximize the overall charging utility while considering wave interference among multiple chargers. Specifically, given a set of stationary sensors, we investigate how to determine optimal positions for a fixed number of chargers. To tackle this problem, we first develop a charging model with wave interference, then propose a two-step charger placement scheme to identify the optimal charger positions. In the first step, we maximize the overall additive power of the waves involved in interference by selecting an appropriate initial position for each charger. Then, in the second step, we maximize the overall charging utility by finding the optimal final position for each charger around its initial position. Finally, to evaluate the performance of our scheme, we conduct extensive simulations and field experiments and the results suggest that CHAIN performs better than the existing algorithms.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 1","pages":"261-275"},"PeriodicalIF":7.7,"publicationDate":"2024-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142257207","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0