IEEE Transactions on Mobile Computing最新文献

{"title":"Enable Practical Long-Range Multi-Target Backscatter Sensing","authors":"Yihao Liu;Jinyan Jiang;Jumin Zhao;Jiliang Wang","doi":"10.1109/TMC.2024.3480137","DOIUrl":"https://doi.org/10.1109/TMC.2024.3480137","url":null,"abstract":"Backscatter sensing has emerged as a significant technology within the Internet of Things (IoT), prompting extensive research interest. This paper presents LoMu, the first long-range multi-target backscatter sensing system designed for low-cost tags operating under ambient LoRa. LoMuintroduces an orthogonal sensing model that processes backscatter signals from multiple tags to extract motion information. The design addresses several practical challenges, including near-far interference among multiple tags, phase offsets from unsynchronized transceivers, and phase errors due to frequency drift in low-cost tags. To overcome these issues, we propose a conjugate-based energy concentration method to extract high-quality signals and a Hamming-window-based method to mitigate the near-far problem. Additionally, we exploit the relationship between excitation and backscatter signals to synchronize the transmitter (TX) and receiver (RX) and combine double sidebands of backscatter signals to eliminate tag frequency drift. Furthermore, a novel joint estimation algorithm is introduced to exploit both amplitude and phase information in target signals, enhancing frequency sensing results and robustness. Our implementation and extensive experiments demonstrate that LoMucan accurately sense up to 35 tags simultaneously and achieve an average frequency sensing error of 0.5% at a range of 400 meters, which is <inline-formula><tex-math>$4times$</tex-math></inline-formula> the range of the state-of-the-art.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1437-1452"},"PeriodicalIF":7.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184513","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}

{"title":"AdaWiFi, Collaborative WiFi Sensing for Cross-Environment Adaptation","authors":"Naiyu Zheng;Yuanchun Li;Shiqi Jiang;Yuanzhe Li;Rongchun Yao;Chuchu Dong;Ting Chen;Yubo Yang;Zhimeng Yin;Yunxin Liu","doi":"10.1109/TMC.2024.3474853","DOIUrl":"https://doi.org/10.1109/TMC.2024.3474853","url":null,"abstract":"Deep learning (DL) based Wi-Fi sensing has witnessed great development in recent years. Although decent results have been achieved in certain scenarios, Wi-Fi based activity recognition is still difficult to deploy in real smart homes due to the limited cross-environment adaptability, i.e. a well-trained Wi-Fi sensing neural network in one environment is hard to adapt to other environments. To address this challenge, we propose \u0000<sc>AdaWiFi</small>\u0000, a DL-based Wi-Fi sensing framework that allows multiple Internet-of-Things (IoT) devices to collaborate and adapt to various environments effectively. The key innovation of \u0000<sc>AdaWiFi</small>\u0000 includes a collective sensing model architecture that utilizes complementary information between distinct devices and avoids the biased perception of individual sensors and an accompanying model adaptation technique that can transfer the sensing model to new environments with limited data. We evaluate our system on a public dataset and a custom dataset collected from three complex sensing environments. The results demonstrate that \u0000<sc>AdaWiFi</small>\u0000 is able to achieve significantly better sensing adaptation effectiveness (e.g. 30% higher accuracy with one-shot adaptation) as compared with state-of-the-art baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"845-858"},"PeriodicalIF":7.7,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938476","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}

{"title":"Handling Failures in Secondary Radio Access Failure Handling in Operational 5G Networks","authors":"Yanbing Liu;Chunyi Peng","doi":"10.1109/TMC.2024.3477462","DOIUrl":"https://doi.org/10.1109/TMC.2024.3477462","url":null,"abstract":"In this work, we conduct a measurement study with three US operators to reveal three types of problematic failure handling on secondary radio access which have not been reported before. Compared to primary radio access failures, secondary radio access failures do not hurt radio access availability but significantly impact data performance, particularly when 5G is used as secondary radio access to boost throughput. Improper failure handling results in significant throughput loss, which is unnecessary in most instances. We then pinpoint the root causes behind these three types of problematic failure handling. When 5G provides higher throughput, failures are more likely to be falsely triggered by a specific event, causing the User Equipment (UE) to unnecessarily lose well-performing 5G connections. Moreover, after failures, the recovery of secondary radio access may fail due to inconsistent parameter settings or be delayed due to missing specific signaling fields. To address these issues, we propose SCGFailure Manager (\u0000<sc>SFM</small>\u0000), a solution to optimize the detection and recovery of secondary radio access failures. Our evaluation results demonstrate that \u0000<sc>SFM</small>\u0000 can effectively avoid 60%-80% of problematic failure handling and double throughput in more than half of failure instances.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"956-969"},"PeriodicalIF":7.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938589","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}

{"title":"BIT-FL: Blockchain-Enabled Incentivized and Secure Federated Learning Framework","authors":"Chenhao Ying;Fuyuan Xia;David S. L. Wei;Xinchun Yu;Yibin Xu;Weiting Zhang;Xikun Jiang;Haiming Jin;Yuan Luo;Tao Zhang;Dacheng Tao","doi":"10.1109/TMC.2024.3477616","DOIUrl":"https://doi.org/10.1109/TMC.2024.3477616","url":null,"abstract":"Harnessing the benefits of blockchain, such as decentralization, immutability, and transparency, to bolster the credibility and security attributes of federated learning (FL) has garnered increasing attention. However, blockchain-enabled FL (BFL) still faces several challenges. The primary and most significant issue arises from its essential but slow validation procedure, which selects high-quality local models by recruiting distributed validators. The second issue stems from its incentive mechanism under the transparent nature of blockchain, increasing the risk of privacy breaches regarding workers’ cost information. The final challenge involves data eavesdropping from shared local models. To address these significant obstacles, this paper proposes a Blockchain-enabled Incentivized and Secure Federated Learning (BIT-FL) framework. BIT-FL leverages a novel loop-based sharded consensus algorithm to accelerate the validation procedure, ensuring the same security as non-sharded consensus protocols. It consistently outputs the correct local model selection when the fraction of adversaries among validators is less than \u0000<inline-formula><tex-math>$1/2$</tex-math></inline-formula>\u0000 with synchronous communication. Furthermore, BIT-FL integrates a randomized incentive procedure, attracting more participants while guaranteeing the privacy of their cost information through meticulous worker selection probability design. Finally, by adding artificial Gaussian noise to local models, it ensures the privacy of trainers’ local models. With the careful design of Gaussian noise, the excess empirical risk of BIT-FL is upper-bounded by \u0000<inline-formula><tex-math>$mathcal {O}(frac{ln n_{min}}{ n_{min}^{3/2}}+frac{ln n}{n})$</tex-math></inline-formula>\u0000, where \u0000<inline-formula><tex-math>$n$</tex-math></inline-formula>\u0000 represents the size of the union dataset, and \u0000<inline-formula><tex-math>$n_{{min}}$</tex-math></inline-formula>\u0000 represents the size of the smallest dataset. Our extensive experiments demonstrate that BIT-FL exhibits efficiency, robustness, and high accuracy for both classification and regression tasks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1212-1229"},"PeriodicalIF":7.7,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938417","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}

{"title":"Resource Collaboration Between Satellite and Wide-Area Mobile Base Stations in Integrated Satellite-Terrestrial Network","authors":"Zhen Li;Chunxiao Jiang;Jiachen Sun;Jianhua Lu","doi":"10.1109/TMC.2024.3472081","DOIUrl":"https://doi.org/10.1109/TMC.2024.3472081","url":null,"abstract":"The integrated satellite-terrestrial network with cascaded downlinks from satellites to wide-area mobile base stations and subsequently to terrestrial users enables global communication for terrestrial 4G/5G cellular users and is widely used in emergency rescue scenarios. However, in this network, satellites and wide-area mobile base stations are controlled by distinct resource scheduling systems with disparate packet queues, which means resources allocated by the satellite to the wide-area mobile base stations may not match the resources allocated by the wide-area mobile base stations to the terrestrial users, leading to coordination inefficiencies and resource wastage. To tackle this challenge, a resource collaborative scheduling mechanism based on cooperative game theory for cascaded downlinks is established, which effectively adapts to distinct resource scheduling systems with various QoS constraints. Then, the utility function of the Nash product is converted into a max-min problem, and a convex transformation method is proposed for the non-convex optimization problem. Simulation results demonstrate that the proposed collaborative scheduling mechanism effectively improves resource utilization and the transmission rate of cascaded downlinks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"875-889"},"PeriodicalIF":7.7,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938372","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}

{"title":"AS-MAC: An Adaptive Scheduling MAC Protocol for Reducing the End-to-End Delay in AUV-Assisted Underwater Acoustic Networks","authors":"Jiani Guo;Shanshan Song;Jun Liu;Miao Pan;Jun-Hong Cui;GuangJie Han","doi":"10.1109/TMC.2024.3475428","DOIUrl":"https://doi.org/10.1109/TMC.2024.3475428","url":null,"abstract":"Autonomous Underwater Vehicle (AUV)-assisted Underwater Acoustic Networks (UANs) are promising for complex ocean applications. In essence, an AUV-assisted UAN is still dominated by fixed nodes, and Time Division Multiple Access (TDMA)-based Medium Access Control (MAC) protocols have undisputed practicability in such fixed nodes-dominated UANs since they are simple and easy to deploy. However, AUV-assisted UANs may exist dynamic bidirectional data streams, while most existing protocols assume UANs have a unidirectional data stream, and their fixed scheduling sequence results in the long end-to-end delay in AUV-assisted UANs. In this paper, we first reveal a phenomenon between the data stream and the scheduling sequence, derived from real-world experiments: their consistent direction decreases the packet waiting delay but increases the slot length, and vice versa. To optimize the end-to-end delay, UANs with dynamic bidirectional data streams expect the MAC protocol to provide a flexible scheduling sequence. To this end, we propose a low-delay Adaptive Scheduling MAC protocol (AS-MAC) based on TDMA for AUV-assisted UANs. In AS-MAC, we analyze the relationship between scheduling sequence and data stream, extracting two significant factors: slot length and packet delay. Afterwards, we design Slot Length Model (SLM) and Packet Delay Model (PDM) to analyze the end-to-end delay of different data streams. Based on these two models, we present a Scheduling Sequence and Slot Length allocation Algorithm (SSSLA) to adaptively provide the minimum end-to-end delay for current bidirectional data streams. Extensive simulation results show that AS-MAC efficiently addresses severe queue congestion of the state-of-the-art protocols and reduces the end-to-end delay of different dynamic streams in various scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1197-1211"},"PeriodicalIF":7.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938413","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}

{"title":"Application Adaptive Light-Weight Deep Learning (AppAdapt-LWDL) Framework for Enabling Edge Intelligence in Dairy Processing","authors":"Rahul Umesh Mhapsekar;Lizy Abraham;Steven Davy;Indrakshi Dey","doi":"10.1109/TMC.2024.3475634","DOIUrl":"https://doi.org/10.1109/TMC.2024.3475634","url":null,"abstract":"The dairy industry is experiencing a surge in data from Edge devices, using spectroscopic techniques for milk quality assessment. Milk spectral data can help understand the species of milk producer and detect inter-species adulteration. Transmitting raw milk spectral data to the cloud for processing faces challenges due to limited network resources such as bandwidth, computational memory, and energy availability. Edge processing offers a solution by training data closer to the source, enhancing efficiency and real-time analysis by providing reduced latency, improved accuracy, resource-aware computation, and real-time customization. However, traditional Deep Learning (DL) methods such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) struggle on resource-constrained Edge devices due to complexity. To address this, we propose an Edge-Centric Application-Adaptive Light-Weight DL approach (AppAdapt-LWDL) for milk species identification and adulteration detection. Our method optimizes DL models via double model optimization, involving low-magnitude pruning and post-training quantization. Our novel application-adaptive algorithm balances speed and accuracy by determining the pruning ratio automatically for the specific application. The chosen model is then quantized for smaller databases, ideal for embedded devices. The AppAdapt-LWDL framework significantly accelerates training, speeds up inferencing, enhances energy efficiency, and maintains accuracy based on application needs.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1105-1119"},"PeriodicalIF":7.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938401","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}

{"title":"TRIMP: Three-Sided Stable Matching for Distributed Vehicle Sharing System Using Stackelberg Game","authors":"Yang Xu;Shanshan Zhang;Chen Lyu;Jia Liu;Tarik Taleb;Shiratori Norio","doi":"10.1109/TMC.2024.3475481","DOIUrl":"https://doi.org/10.1109/TMC.2024.3475481","url":null,"abstract":"Distributed Vehicle Sharing System (DVSS) leverages emerging technologies such as blockchain to create a secure, transparent, and efficient platform for sharing vehicles. In such a system, both efficient matching of users with available vehicles and optimal pricing mechanisms play crucial roles in maximizing system revenue. However, most existing schemes utilize user-to-vehicle (two-sided) matching and pricing, which are unrealistic for DVSS due to the lack of participation of service providers. To address this issue, we propose in this paper a novel Three-sided stable Matching with an optimal Pricing (TRIMP) scheme. First, to achieve maximum utilities for all three parties simultaneously, we formulate the optimal policy and pricing problem as a three-stage Stackelberg game and derive its equilibrium points accordingly. Second, relying on these solutions from the Stackelberg game, we construct a three-sided cyclic matching for DVSS. Third, as the existence of such a matching is NP-complete, we design a specific vehicle sharing algorithm to realize stable matching. Extensive experiments demonstrate the effectiveness of our TRIMP scheme, which optimizes the matching process and ensures efficient resource allocation, leading to a more stable and well-functioning decentralized vehicle sharing ecosystem.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1132-1148"},"PeriodicalIF":7.7,"publicationDate":"2024-10-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938439","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}

{"title":"Multi-Target Device-Free Positioning Based on Spatial-Temporal mmWave Point Cloud","authors":"Jie Wang;Jingmiao Wu;Yingwei Qu;Qi Xiao;Qinghua Gao;Yuguang Fang","doi":"10.1109/TMC.2024.3474671","DOIUrl":"https://doi.org/10.1109/TMC.2024.3474671","url":null,"abstract":"Device-free positioning (DFP) using mmWave signals is an emerging technique that could track a target without attaching any devices. It conducts position estimation by analyzing the influence of targets on their surrounding mmWave signals. With the widespread utilization of mmWave signals, DFP will have many potential applications in tracking pedestrians and robots in intelligent monitoring systems. State-of-the-art DFP work has already achieved excellent positioning performance when there is one target only, but when there are multiple targets, the time-varying target state, such as entering or leaving of the wireless coverage area and close interactions, makes it challenging to track every target. To solve these problems, in this paper, we propose a spatial-temporal analysis method to robustly track multiple targets based on the high precision mmWave point cloud information. Specifically, we propose a high precision spatial imaging strategy to construct fine-grained mmWave point cloud of the targets, design a spatial-temporal point cloud clustering method to determine the target state, and then leverage a gait based identity and trajectory association scheme and a particle filter to achieve robust identity-aware tracking. Extensive evaluations on a 77 GHz mmWave testbed have been conducted to demonstrate the effectiveness and robustness of our proposed schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1163-1180"},"PeriodicalIF":7.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938441","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}

{"title":"Biometric Encoding for Replay-Resistant Smartphone User Authentication Using Handgrips","authors":"Long Huang;Chen Wang","doi":"10.1109/TMC.2024.3474673","DOIUrl":"https://doi.org/10.1109/TMC.2024.3474673","url":null,"abstract":"Biometrics have been widely applied for user authentication. However, existing biometric authentications are vulnerable to biometric spoofing, because they can be observed and forged. In addition, they rely on verifying biometric features that rarely change. To address this issue, we propose to verify the handgrip biometric that can be unobtrusively extracted by acoustic signals when the user holds the phone. This biometric is uniquely associated with the user’s hand geometry, body-fat ratio, and gripping strength, which are hard to reproduce. Furthermore, we propose two biometric encoding techniques (i.e., temporal-frequential and spatial) to convert static biometrics into dynamic biometric features to prevent data reuse. In particular, we develop a biometric authentication system to work with the challenge-response protocol. We encode the ultrasonic signal according to a random challenge sequence and extract a distinct biometric code as the response. We further develop two decoding algorithms to decode the biometric code for user authentication. Additionally, we investigate multiple new attacks and explore using a latent diffusion model to solve the acoustic noise discrepancies between the training and testing data to improve system performance. Extensive experiments show our system achieves 97% accuracy in distinguishing users and rejects 100% replay attacks with \u0000<inline-formula><tex-math>$ 0.6 , s$</tex-math></inline-formula>\u0000 challenge sequence.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1230-1248"},"PeriodicalIF":7.7,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938387","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}