IEEE Transactions on Mobile Computing最新文献

{"title":"Seeing the Invisible: Recovering Surveillance Video With COTS mmWave Radar","authors":"Mingda Han;Huanqi Yang;Mingda Jia;Weitao Xu;Yanni Yang;Zhijian Huang;Jun Luo;Xiuzhen Cheng;Pengfei Hu","doi":"10.1109/TMC.2024.3445507","DOIUrl":"https://doi.org/10.1109/TMC.2024.3445507","url":null,"abstract":"Video surveillance systems play a crucial role in ensuring public safety and security by capturing and monitoring critical events in various areas. However, traditional surveillance cameras face limitations when it comes to malicious physical damage or obscuring by offenders. To overcome this limitation, we propose \u0000<sc>m<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula> Vision</small>\u0000, which is the first millimeter-wave (mmWave)-based video reconstruction system designed to enhance existing video surveillance cameras. \u0000<sc>m<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula> Vision</small>\u0000 utilizes mmWave to sense the profile and motion signature of the target, integrating it with previously acquired visual data about the environment and the target's appearance, thereby facilitating the reconstruction of surveillance video. Specifically, our proposed system incorporates a dual-stage mmWave signal denoising algorithm to efficiently eliminate the noise and multiple-input multiple-output virtual antenna enhanced heatmap generation (MVAE-HG) method to obtain fine-grained mmWave heatmaps responsive to the target's profile and motion information. Moreover, we design the mm2Video generative network that first employs a multi-modal fusion module to fuse the mmWave and pre-acquired visual data, then use a conditional generative adversarial network (cGAN)-based video reconstruction module for surveillance video reconstruction. We conducted comprehensive experiments on \u0000<sc>m<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula> Vision</small>\u0000 using a commercial mmWave radar and four surveillance cameras across various environments, with the participation of seven individuals. Evaluation results show that \u0000<sc>m<inline-formula><tex-math>$^{2}$</tex-math><alternatives><mml:math><mml:msup><mml:mrow/><mml:mn>2</mml:mn></mml:msup></mml:math><inline-graphic></alternatives></inline-formula> Vision</small>\u0000 can achieve an average structural similarity index measure (SSIM) of 0.93, demonstrating its effectiveness and potential.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14592-14606"},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598670","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":"Practical Gaze Tracking on Any Surface With Your Phone","authors":"Jiani Cao;Jiesong Chen;Chengdong Lin;Yang Liu;Kun Wang;Zhenjiang Li","doi":"10.1109/TMC.2024.3445373","DOIUrl":"10.1109/TMC.2024.3445373","url":null,"abstract":"This paper introduces ASGaze, a novel gaze tracking system using the RGB camera of smartphones. ASGaze improves the accuracy of existing methods and uniquely tracks gaze points on various surfaces, including phone screens, computer displays, and non-electronic surfaces like whiteboards or paper - a situation that is challenging for existing methods. To achieve this, we revisit the 3D geometric eye model, commonly used in high-end commercial trackers, and it has the potential to achieve our goals. To avoid the high cost of commercial solutions, we identify three fundamental issues when processing the eye model with an RGB camera, including how to accurately extract iris boundary that is the meta-information in our design, how to remove ambiguity from iris boundary to gaze point transformation, and how to map gaze points onto the target surface. Furthermore, as we consider deploying ASGaze in real-world applications, two additional challenges should be addressed: how to automatically and accurately annotate the training dataset to reduce manual labor and time costs, and how to accelerate the inference speed of ASGaze on mobile devices to improve user experience. We propose effective techniques to resolve these issues. Our prototype and experiments on three tracking surfaces demonstrate significant performance gains.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14689-14707"},"PeriodicalIF":7.7,"publicationDate":"2024-08-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180622","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":"Towards Efficient and Portable Software Modulator via Neural Networks for IoT Gateways","authors":"Jiazhao Wang;Wenchao Jiang;Ruofeng Liu;Shuai Wang","doi":"10.1109/TMC.2024.3444768","DOIUrl":"https://doi.org/10.1109/TMC.2024.3444768","url":null,"abstract":"A physical-layer modulator is crucial for IoT gateways, but current solutions face issues like limited extensibility and platform-specificity due to soldered chipsets for specific technologies or diverse software toolkits for software radios. With the rapid expansion of the Internet of Things (IoT), such limitations are hard to ignore as the demand for versatile wireless technologies has increased. This paper introduces a novel approach using neural networks as an abstraction layer for these modulators in IoT gateways, termed NN-defined modulators. This method overcomes the challenges of extensibility and portability across different hardware platforms. The NN-defined modulator employs a model-driven approach based on mathematical principles, resulting in a lightweight, hardware-acceleration-friendly structure. These modulators are containerized with necessary runtime, facilitating agile deployment on varied platforms. We tested NN-defined modulators on platforms like Nvidia Jetson Nano and Raspberry Pi, showing they perform comparably to traditional modulators while offering efficiency improvements. The implementation is memory-efficient and adds minimal latency. Additionally, we demonstrate real-world applications of our NN-defined modulators in generating ZigBee and WiFi packets, compatible with standard TI CC2650 (ZigBee) and Intel AX201 (WiFi NIC) devices.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"13866-13881"},"PeriodicalIF":7.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142595856","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":"A Localization Algorithm for Underwater Acoustic Sensor Networks With Improved Newton Iteration and Simplified Kalman Filter","authors":"Jingping Liu;Xiujuan Du;Long Jin","doi":"10.1109/TMC.2024.3443992","DOIUrl":"https://doi.org/10.1109/TMC.2024.3443992","url":null,"abstract":"Underwater acoustic localization is a crucial technique for most underwater applications. However, in highly dynamic marine environments, underwater acoustic localization faces many challenges, such as the stratification effect, the clock asynchronization, the node drift, and environmental noises. Concerning above problems, we propose a new underwater localization algorithm for mobile underwater acoustic sensor networks (UASNs). At first, the measurement biases are modeled as the combination of constant biases and random biases according to the physical mechanism of their generation and distribution characteristics in measured data. Then, an error-summation-incorporated Newton iteration (ESINI) algorithm is designed to compute the localization result along the direction of constant biases decrease, and a Taylor expansion is used to approach the actual localization result along the direction of random biases decrease. Subsequently, a simplified Kalman filter (SKF) fuses the two localization results and enhances the localization accuracy. In this way, the proposed algorithm effectively increases the accuracy of localization results without adding extra measurement. Finally, theoretical analyses, simulations, and lake experiments are provided to verify the proposed algorithm's effectiveness and noise resistance performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14459-14470"},"PeriodicalIF":7.7,"publicationDate":"2024-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598634","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":"Reward-Oriented Task Offloading in Energy Harvesting Collaborative Edge Computing Systems","authors":"Zhichen Ni;Honglong Chen;Birong Gao;Kai Lin;Liantao Wu;Jiguo Yu","doi":"10.1109/TMC.2024.3443868","DOIUrl":"https://doi.org/10.1109/TMC.2024.3443868","url":null,"abstract":"The widespread deployment of Internet of Things (IoT) devices brings more and more computation intensive or delay sensitive tasks, causing a series of challenges to efficient services. Collaborative edge computing is an effective way to solve them, where the tasks will be processed in the devices, edge servers, and cloud server in parallel. However, the above collaborative paradigm requires dense deployment of base stations (BSs) and consumes lots of energy. To address this problem, in this paper, we introduce energy harvesting technology and construct a collaborative edge computing system powered by hybrid energy. Considering the highly variable task execution delay caused by the resource contention and the unstable energy state, we further introduce the Holt Linear Exponential Smoothing Prediction to predict the delay and then propose an Online Server Control schedule called OSC based on Lyapunov optimization to obtain the optimized offloading decision without the knowledge of the future system state. The extensive simulations illustrate that the proposed OSC outperforms other benchmark ones.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14414-14426"},"PeriodicalIF":7.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598668","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":"Towards Adaptive Privacy Protection for Interpretable Federated Learning","authors":"Zhe Li;Honglong Chen;Zhichen Ni;Yudong Gao;Wei Lou","doi":"10.1109/TMC.2024.3443862","DOIUrl":"https://doi.org/10.1109/TMC.2024.3443862","url":null,"abstract":"Federated learning (FL) is an effective privacy-preserving mechanism that collaboratively trains the global model in a distributed manner by solely sharing model parameters rather than data from local clients, like mobile devices, to a central server. Nevertheless, recent studies have illustrated that FL still suffers from gradient leakage as adversaries try to recover training data by analyzing shared parameters from local clients. To address this issue, differential privacy (DP) is adopted to add noise to the parameters of local models before aggregation occurs on the server. It, however, results in the poor performance of gradient-based interpretability, since some important weights capturing the salient region in feature maps will be perturbed. To overcome this problem, we propose a simple yet effective adaptive gradient protection (AGP) mechanism that selectively adds noisy perturbations to certain channels of each client model that have a relatively small impact on interpretability. We also offer a theoretical analysis of the convergence of FL using our method. The evaluation results on both IID and Non-IID data demonstrate that the proposed AGP can achieve a good trade-off between privacy protection and interpretability in FL. Furthermore, we verify the robustness of the proposed method against two different gradient leakage attacks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14471-14483"},"PeriodicalIF":7.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598667","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":"Heterogeneous Dual-Attentional Network for WiFi and Video-Fused Multi-Modal Crowd Counting","authors":"Lifei Hao;Baoqi Huang;Bing Jia;Guoqiang Mao","doi":"10.1109/TMC.2024.3444469","DOIUrl":"https://doi.org/10.1109/TMC.2024.3444469","url":null,"abstract":"Crowd counting aims to estimate the number of individuals in targeted areas. However, mainstream vision-based methods suffer from limited coverage and difficulty in multi-camera collaboration, which limits their scalability, whereas emerging WiFi-based methods can only obtain coarse results due to signal randomness. To overcome the inherent limitations of unimodal approaches and effectively exploit the advantage of multi-modal approaches, this paper presents an innovative WiFi and video-fused multi-modal paradigm by leveraging a heterogeneous dual-attentional network, which jointly models the intra- and inter-modality relationships of global WiFi measurements and local videos to achieve accurate and stable large-scale crowd counting. First, a flexible hybrid sensing network is constructed to capture synchronized multi-modal measurements characterizing the same crowd at different scales and perspectives; second, differential preprocessing, heterogeneous feature extractors, and self-attention mechanisms are sequentially utilized to extract and optimize modality-independent and crowd-related features; third, the cross-attention mechanism is employed to deeply fuse and generalize the matching relationships of two modalities. Extensive real-world experiments demonstrate that our method can significantly reduce the error by 26.2%, improve the stability by 48.43%, and achieve the accuracy of about 88% in large-scale crowd counting when including the videos from two cameras, compared to the best WiFi unimodal baseline.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14233-14247"},"PeriodicalIF":7.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598648","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":"Communication-Dependent Computing Resource Management for Concurrent Task Orchestration in IoT Systems","authors":"Qiaomei Han;Xianbin Wang;Weiming Shen","doi":"10.1109/TMC.2024.3444597","DOIUrl":"https://doi.org/10.1109/TMC.2024.3444597","url":null,"abstract":"Recent advances in distributed machine learning and wireless network technologies are bringing new opportunities for Internet of Things (IoT) systems, where smart devices are often wirelessly connected to collaborate, jointly completing tasks known as \u0000<italic>communication-dependent computing (CDC)</i>\u0000 tasks. However, due to the dependence of computing on communication and the presence of concurrent tasks, it remains a challenge to optimize CDC task performance and efficiency while fulfilling multi-dimensional requirements, particularly with incomplete system information and dynamic environmental impacts. To overcome these, we present a concurrent CDC task framework to model the correlated communication and computing stages and multi-dimensional requirements of CDC tasks. We then formulate a task orchestration and resource management problem to optimize overall utility, where each task's utility is designed as a joint metric including the cumulative computing deviation and time efficiency of task completion. To solve this, we employ auxiliary graphs to capture the topological information of tasks and resources, and update weights based on the utility in dynamic environments. Subsequently, a multi-agent reinforcement learning algorithm is leveraged to make distributed decisions with incomplete information. Experiments demonstrate the proposed approach outperforms baselines in terms of task performance and efficiency, indicating our solution holds great potential.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14297-14312"},"PeriodicalIF":7.7,"publicationDate":"2024-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598633","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":"Towards ISAC-Empowered mmWave Radars by Capturing Modulated Vibrations","authors":"Kaiyan Cui;Qiang Yang;Leming Shen;Yuanqing Zheng;Fu Xiao;Jinsong Han","doi":"10.1109/TMC.2024.3443404","DOIUrl":"https://doi.org/10.1109/TMC.2024.3443404","url":null,"abstract":"Integrated Sensing and Communication (ISAC) has emerged as a promising technology for next-generation mobile networks. Towards ISAC, we develop \u0000<italic>mmRipple</i>\u0000 that empowers commodity mmWave radars with communication capabilities through smartphone vibrations. In \u0000<italic>mmRipple</i>\u0000, a smartphone (transmitter) sends messages by modulating smartphone vibrations, while a mmWave radar (receiver) receives the messages by detecting and decoding the smartphone vibrations. By doing so, a smartphone user can not only be passively sensed by a mmWave radar, but also actively send messages to the radar without any hardware modifications. Although promising, the data rate of \u0000<italic>mmRipple</i>\u0000 is limited by Morse-style communication. To address this, we present \u0000<italic>mmRipple+</i>\u0000, which leverages the Pulse Width and Amplitude Modulation (PWAM) technique and suppresses inter-symbol interference to enable faster communication. We prototype \u0000<italic>mmRipple</i>\u0000 and \u0000<italic>mmRipple+</i>\u0000 on commodity mmWave radars and different types of smartphones. Experimental results show that \u0000<italic>mmRipple</i>\u0000 achieves an average vibration pattern recognition accuracy of 98.60% within a \u0000<inline-formula><tex-math>$ 2$</tex-math><inline-graphic></inline-formula>\u0000 m communication range, and 97.74% within \u0000<inline-formula><tex-math>$ 3$</tex-math><inline-graphic></inline-formula>\u0000 m. The maximum communication range extends to \u0000<inline-formula><tex-math>$ 5$</tex-math><inline-graphic></inline-formula>\u0000 m. Meanwhile, \u0000<italic>mmRipple+</i>\u0000 achieves a bit rate of 100 bps with a BER of less than 3%, improving the data rate by 4× over \u0000<italic>mmRippe</i>\u0000 with the same symbol duration. This work pioneers smartphone-to-COTS mmWave radar communication via vibrations, unlocking diverse applications.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"13787-13803"},"PeriodicalIF":7.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142636449","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":"EdgeShield: Enabling Collaborative DDoS Mitigation at the Edge","authors":"Xiaoyu Xia;Feifei Chen;Qiang He;Ruikun Luo;Bowen Liu;Caslon Chua;Rajkumar Buyya;Yun Yang","doi":"10.1109/TMC.2024.3443260","DOIUrl":"https://doi.org/10.1109/TMC.2024.3443260","url":null,"abstract":"Edge computing (EC) enables low-latency services by pushing computing resources to the network edge. Due to the geographic distribution and limited capacities of edge servers, EC systems face the challenge of edge distributed denial-of-service (DDoS) attacks. Existing systems designed to fight cloud DDoS attacks cannot mitigate edge DDoS attacks effectively due to new attack characteristics. In addition, those systems are typically activated upon detected attacks, which is not always realistic in EC systems. DDoS mitigation needs to be cohesively integrated with workload migration at the edge to ensure timely responses to edge DDoS attacks. In this paper, we present EdgeShield, a novel DDoS mitigation system that leverages edge servers’ computing resources collectively to defend against edge DDoS attacks without the need for attack detection. Aiming to maximize system throughput over time without causing significant service delays, EdgeShield monitors service delays and migrates workloads across an EC system with adaptive mitigation strategies. The experimental results show that EdgeShield significantly outperforms state-of-the-art solutions in both system throughput and service delays.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"23 12","pages":"14502-14513"},"PeriodicalIF":7.7,"publicationDate":"2024-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142598609","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}