IEEE Transactions on Mobile Computing最新文献_第7页

Channel Cycle Time: A New Measure of Short-Term Fairness 通道周期时间：衡量短期公平性的新标准

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-21 DOI: 10.1109/TMC.2024.3484177

Pengfei Shen;Yulin Shao;Haoyuan Pan;Lu Lu;Yonina C. Eldar

引用次数: 0

Real-Time Continuous Activity Recognition With a Commercial mmWave Radar

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-21 DOI: 10.1109/TMC.2024.3483813

Yunhao Liu;Jia Zhang;Yande Chen;Weiguo Wang;Songzhou Yang;Xin Na;Yimiao Sun;Yuan He

{"title":"Real-Time Continuous Activity Recognition With a Commercial mmWave Radar","authors":"Yunhao Liu;Jia Zhang;Yande Chen;Weiguo Wang;Songzhou Yang;Xin Na;Yimiao Sun;Yuan He","doi":"10.1109/TMC.2024.3483813","DOIUrl":"https://doi.org/10.1109/TMC.2024.3483813","url":null,"abstract":"mmWave-based activity recognition technology has attracted widespread attention as it provides the ability of device-free, ubiquitous and accurate sensing. Recognition of human activities intrinsically demands to be real-time and continuous, but the state of the arts is still far limited with the capacity in this regard. The main obstacle lies in activity sequence segmentation, i.e., locating the boundaries between consecutive activities in an activity sequence. This is a daunting task, due to the unclear activity boundaries and the variable activity duration. In this paper, we propose <sc>ZuMa, the first mmWave-based approach to real-time continuous activity recognition. When resorting to a machine learning model for activity recognition, our insight is that the recognition confidence of the recognition model is highly correlated to the accuracy of activity sequence segmentation, so that the former can be utilized as a feedback metric to finely adjust the segmentation boundaries. Based on this insight, <sc>ZuMa is a coarse-to-fine grained approach, which includes the fast coarse-grained activity chunk extraction and the find-grained explicit segmentation adjustment and recognition. We have implemented <sc>ZuMa with the commercial mmWave radar and evaluated its performance under various settings. The results demonstrate that <sc>ZuMa achieves an average recognition error of 12.67%, which is 65.08% and 71.87% lower than that of the two baseline methods. The average recognition delay of <sc>ZuMa is only 1.86 s.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1684-1698"},"PeriodicalIF":7.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361015","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

Digital Twin Backed Closed-Loops for Energy-Aware and Open RAN-Based Fixed Wireless Access Serving Rural Areas

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-21 DOI: 10.1109/TMC.2024.3482985

Anselme Ndikumana;Kim Khoa Nguyen;Mohamed Cheriet

{"title":"Digital Twin Backed Closed-Loops for Energy-Aware and Open RAN-Based Fixed Wireless Access Serving Rural Areas","authors":"Anselme Ndikumana;Kim Khoa Nguyen;Mohamed Cheriet","doi":"10.1109/TMC.2024.3482985","DOIUrl":"https://doi.org/10.1109/TMC.2024.3482985","url":null,"abstract":"Internet access in rural areas should be improved to support digital inclusion and 5G services. Due to the high deployment costs of fiber optics in these areas, Fixed Wireless Access (FWA) has become a preferable alternative. Additionally, the Open Radio Access Network (O-RAN) can facilitate the interoperability of FWA elements, allowing some FWA functions to be deployed at the edge cloud. However, deploying edge clouds in rural areas can increase network and energy costs. To address these challenges, we propose a closed-loop system assisted by a Digital Twin (DT) to automate energy-aware O-RAN based FWA resource management in rural areas. We consider the FWA and edge cloud as the Physical Twin (PT) and design a closed-loop that distributes radio resources to edge cloud instances for scheduling. We develop another closed-loop for intra-slice resource allocation to houses. We design an energy model that integrates radio resource allocation and formulate ultra-small and small-timescale optimizations for the PT to maximize slice requirement satisfaction while minimizing energy costs. We then design a reinforcement learning approach and successive convex approximation to address the formulated problems. We present a DT that replicates the PT by incorporating solution experiences into future states. The results show that our approach efficiently uses radio and energy resources.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1669-1683"},"PeriodicalIF":7.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360884","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

Real-Time Video Forgery Detection via Vision-WiFi Silhouette Correspondence

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-21 DOI: 10.1109/TMC.2024.3483550

Jianwei Liu;Xinyue Fang;Yike Chen;Jiantao Yuan;Guanding Yu;Jinsong Han

{"title":"Real-Time Video Forgery Detection via Vision-WiFi Silhouette Correspondence","authors":"Jianwei Liu;Xinyue Fang;Yike Chen;Jiantao Yuan;Guanding Yu;Jinsong Han","doi":"10.1109/TMC.2024.3483550","DOIUrl":"https://doi.org/10.1109/TMC.2024.3483550","url":null,"abstract":"For safety guard and crime prevention, video surveillance systems have been pervasively deployed in many security-critical scenarios, such as the residence, retail stores, and banks. However, these systems could be infiltrated by the adversary and the video streams would be modified or replaced, i.e., under the video forgery attack. The prevalence of Internet of Things (IoT) devices and the emergence of Deepfake-like techniques severely emphasize the vulnerability of video surveillance systems under such attacks. To secure existing surveillance systems, in this paper we propose a vision-WiFi cross-modal video forgery detection system, namely WiSil. Leveraging a theoretical model based on the principle of signal propagation, WiSil constructs wave front information of the object in the monitoring area from WiFi signals. With a well-designed deep learning network, WiSil further recovers silhouettes from the wave front information. Based on a Siamese network-based semantic feature extractor, WiSil can eventually determine whether a frame is manipulated by comparing the semantic feature vectors extracted from the video’s silhouette with those extracted from the WiFi’s silhouette. We enhance the basic version of WiSil Fang et al. 2023 by developing a model compression method and a forgery trace localization method. Extensive experiments show that WiSil achieves 95%<inline-formula><tex-math>$+$</tex-math></inline-formula> accuracy in detecting tampered frames.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1585-1601"},"PeriodicalIF":7.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184521","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

Harmonizing Global and Local Class Imbalance for Federated Learning 协调联邦学习的全局和局部阶级失衡

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-18 DOI: 10.1109/TMC.2024.3476340

Jialiang Zhu;Hao Zheng;Wenchao Xu;Haozhao Wang;Zhiming He;Yuxuan Liu;Shuang Wang;Qi Sun

{"title":"Harmonizing Global and Local Class Imbalance for Federated Learning","authors":"Jialiang Zhu;Hao Zheng;Wenchao Xu;Haozhao Wang;Zhiming He;Yuxuan Liu;Shuang Wang;Qi Sun","doi":"10.1109/TMC.2024.3476340","DOIUrl":"https://doi.org/10.1109/TMC.2024.3476340","url":null,"abstract":"Federated Learning (FL) is to collaboratively train a global model among distributed clients by iteratively aggregating their local updates without sharing their raw data, whereby the global modal can approximately converge to the centralized training way over a global dataset that composed of all local datasets (i.e., union of all users’ local data). However, in real-world scenarios, the distributions of the data classes are often imbalanced not only locally, but also in the global dataset, which severely deteriorate the FL performance due to the conflicting knowledge aggregation. Existing solutions for FL class imbalance either focus on the local data to regulate the training process or purely aim at the global datasets, which often fail to alleviate the class imbalance problem if there is mismatch between the local and global imbalance. Considering these limitations, this paper proposes a Global-Local Joint Learning method, namely GLJL, which simultaneously harmonizes the global and local class imbalance issue by jointly embedding the local and the global factors into each client’s loss function. Through extensive experiments over popular datasets with various class imbalance settings, we show that the proposed method can significantly improve the model accuracy over minority classes without sacrificing the accuracy of other classes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"1120-1131"},"PeriodicalIF":7.7,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938402","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

SANSee: A Physical-Layer Semantic-Aware Networking Framework for Distributed Wireless Sensing SANSee：面向分布式无线传感的物理层语义感知网络框架

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-17 DOI: 10.1109/TMC.2024.3483272

Huixiang Zhu;Yong Xiao;Yingyu Li;Guangming Shi;Marwan Krunz

{"title":"SANSee: A Physical-Layer Semantic-Aware Networking Framework for Distributed Wireless Sensing","authors":"Huixiang Zhu;Yong Xiao;Yingyu Li;Guangming Shi;Marwan Krunz","doi":"10.1109/TMC.2024.3483272","DOIUrl":"https://doi.org/10.1109/TMC.2024.3483272","url":null,"abstract":"Contactless device-free wireless sensing has recently attracted significant interest due to its potential to support a wide range of immersive human-machine interactive applications using ubiquitously available radio frequency (RF) signals. Traditional approaches focus on developing a single global model based on a combined dataset collected from different locations. However, wireless signals are known to be location and environment specific. Thus, a global model results in inconsistent and unreliable sensing results. It is also unrealistic to construct individual models for all the possible locations and environmental scenarios. Motivated by the observation that signals recorded at different locations are closely related to a set of physical-layer semantic features, in this paper we propose SANSee, a semantic-aware networking-based framework for distributed wireless sensing. SANSee allows models constructed in one or a limited number of locations to be transferred to new locations without requiring any locally labeled data or model training. SANSee is built on the concept of physical-layer semantic-aware network (pSAN), which characterizes the semantic similarity and the correlations of sensed data across different locations. A pSAN-based zero-shot transfer learning solution is introduced to allow receivers in new locations to obtain location-specific models by directly aggregating the models trained by other receivers. We theoretically prove that models obtained by SANSee can approach the locally optimal models. Experimental results based on real-world datasets are used to verify that the accuracy of the transferred models obtained by SANSee matches that of the models trained by the locally labeled data based on supervised learning approaches.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1636-1653"},"PeriodicalIF":7.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184511","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

Efficient and Secure Geometric Range Search Over Encrypted Spatial Data in Mobile Cloud 移动云中加密空间数据的高效安全几何范围搜索

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-17 DOI: 10.1109/TMC.2024.3482321

Yinbin Miao;Guijuan Wang;Xinghua Li;Hongwei Li;Kim-Kwang Raymond Choo;Rebert H. Deng

{"title":"Efficient and Secure Geometric Range Search Over Encrypted Spatial Data in Mobile Cloud","authors":"Yinbin Miao;Guijuan Wang;Xinghua Li;Hongwei Li;Kim-Kwang Raymond Choo;Rebert H. Deng","doi":"10.1109/TMC.2024.3482321","DOIUrl":"https://doi.org/10.1109/TMC.2024.3482321","url":null,"abstract":"With the rapid development of mobile computing and the popularity of mobile devices equipped with GPS technology, massive spatial data have become available. Enterprises upload encrypted spatial data to the mobile cloud to save local storage and computation costs. However, the existing secure Geometric Range Search (GRS) solutions are inefficient in terms of building, updating index structure and querying processes. Moreover, the index structures of existing GRS schemes based on Order Preserving Encryption (OPE) leak location order, which may lead to reconstruction attacks. To solve these issues, we first propose an efficient and secure GRS scheme using Radix-Tree, namely GRSRT-I. Specifically, we construct an index structure based on Radix-tree to achieve efficient search and update, then use homomorphic encryption NTRU to resist chosen-plaintext attack, finally design a dual-server architecture to alleviate the burdens on mobile users caused by multiple rounds of interactions. Furthermore, we propose an enhanced scheme, GRSRT-II, by combining Order-Revealing Encryption and OPE, which greatly improves the search efficiency while slightly reducing the security. We formally prove the security of our proposed schemes, and conduct extensive experiments to demonstrate that GRSRT-I can improve the query efficiency by up to at least 1.5 times when compared with previous solutions and GRSRT-II can achieve a higher level of search efficiency.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1621-1635"},"PeriodicalIF":7.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184524","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

Joint Association, Beamforming, and Resource Allocation for Multi-IRS Enabled MU-MISO Systems With RSMA

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-17 DOI: 10.1109/TMC.2024.3483193

Chunjie Wang;Xuhui Zhang;Huijun Xing;Liang Xue;Shuqiang Wang;Yanyan Shen;Bo Yang;Xinping Guan

引用次数: 0

O-RAN-Enabled Intelligent Network Slicing to Meet Service-Level Agreement (SLA) 支持o - ran的智能网络切片满足服务水平协议（SLA）

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-17 DOI: 10.1109/TMC.2024.3476338

Jiongyu Dai;Lianjun Li;Ramin Safavinejad;Shadab Mahboob;Hao Chen;Vishnu V Ratnam;Haining Wang;Jianzhong Zhang;Lingjia Liu

{"title":"O-RAN-Enabled Intelligent Network Slicing to Meet Service-Level Agreement (SLA)","authors":"Jiongyu Dai;Lianjun Li;Ramin Safavinejad;Shadab Mahboob;Hao Chen;Vishnu V Ratnam;Haining Wang;Jianzhong Zhang;Lingjia Liu","doi":"10.1109/TMC.2024.3476338","DOIUrl":"https://doi.org/10.1109/TMC.2024.3476338","url":null,"abstract":"Network slicing plays a critical role in enabling multiple virtualized and independent network services to be created on top of a common physical network infrastructure. In this paper, we introduce a deep reinforcement learning (DRL)-based radio resource management (RRM) solution for radio access network (RAN) slicing under service-level agreement (SLA) guarantees. The objective of this solution is to minimize the SLA violation. Our method is designed with a two-level scheduling structure that works seamlessly under Open Radio Access Network (O-RAN) architecture. Specifically, at an upper level, a DRL-based inter-slice scheduler is working on a coarse time granularity to allocate resources to network slices. And at a lower level, an existing intra-slice scheduler such as proportional fair (PF) is working on a fine time granularity to allocate slice dedicated resources to slice users. This setting makes our solution O-RAN compliant and ready to be deployed as an ‘xApp’ on the RAN Intelligent Controller (RIC). For performance evaluation and proof of concept purposes, we develop two platforms, one industry-level simulator and one O-RAN compliant testbed; evaluation on both platforms demonstrates our solution’s superior performance over conventional methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 2","pages":"890-906"},"PeriodicalIF":7.7,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142938371","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

${sf Img2Acoustic}$Img2Acoustic: A Cross-Modal Gesture Recognition Method Based on Few-Shot Learning

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-10-16 DOI: 10.1109/TMC.2024.3481443

Yongpan Zou;Jianhao Weng;Wenting Kuang;Yang Jiao;Victor C. M. Leung;Kaishun Wu

{"title":"${sf Img2Acoustic}$Img2Acoustic: A Cross-Modal Gesture Recognition Method Based on Few-Shot Learning","authors":"Yongpan Zou;Jianhao Weng;Wenting Kuang;Yang Jiao;Victor C. M. Leung;Kaishun Wu","doi":"10.1109/TMC.2024.3481443","DOIUrl":"https://doi.org/10.1109/TMC.2024.3481443","url":null,"abstract":"Acoustic-based human gesture recognition (HGR) offers diverse applications due to the ubiquity of sensors and touch-free interaction. However, existing machine learning approaches require substantial training data, making the process time-consuming, costly, and labor-intensive. Recent studies have explored cross-modal methods to reduce the need for large training datasets in behavior recognition, but they typically rely on open-source datasets that closely align with the target domain, limiting flexibility and complicating data collection. In this paper, we propose <inline-formula><tex-math>${sf Img2Acoustic}$</tex-math></inline-formula>, a novel cross-modal acoustic-based HGR approach that leverages models trained on open-source image datasets (i.e., EMNIST, Omniglot) to effectively recognize custom gestures detected via acoustic signals. Our model incorporates a task-aware attention layer (TAAL) and a task-aware local matching layer (TALML), enabling seamless transfer of knowledge from image datasets to acoustic gesture recognition. We implement <inline-formula><tex-math>${sf Img2Acoustic}$</tex-math></inline-formula> on commercial devices and conduct comprehensive evaluations, demonstrating that our method not only delivers superior accuracy and robustness compared to existing approaches but also eliminates the need for extensive training data collection.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1496-1512"},"PeriodicalIF":7.7,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184517","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