IEEE Transactions on Mobile Computing最新文献

{"title":"A “Breathing” Mobile Communication Network","authors":"Chao Ge;Ge Chen;Zhipeng Jiang","doi":"10.1109/TMC.2024.3487213","DOIUrl":"https://doi.org/10.1109/TMC.2024.3487213","url":null,"abstract":"The frequent migration of large-scale users leads to the load imbalance of mobile communication networks, which causes resource waste and decreases user experience. To address the load balancing problem, this paper proposes a dynamic optimization framework for mobile communication networks inspired by the average consensus in multi-agent systems. In this framework, all antennas cooperatively optimize their CPICH (Common Pilot Channel) transmit power in real-time to balance their busy-degrees. Then, the coverage area of each antenna would change accordingly, and we call this framework a “breathing” mobile communication network. To solve this optimization problem, two algorithms named BDBA (Busy-degree Dynamic Balancing Algorithm) and BFDBA (Busy-degree Fast Dynamic Balancing Algorithm) are proposed. Moreover, a fast network coverage calculation method is introduced, by which each antenna's minimum CPICH transmit power is determined under the premise of meeting the network coverage requirements. Besides, we present the theoretical analysis of the two proposed algorithms’ performance, which prove that all antennas’ busy-degrees will reach consensus under certain assumptions. Furthermore, simulations carried out on three large datasets demonstrate that our cooperative optimization can significantly reduce the unbalance among antennas as well as the proportion of over-busy antennas.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2056-2072"},"PeriodicalIF":7.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361005","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":"DeepRP: Bottleneck Theory Guided Relay Placement for 6G Mesh Backhaul Augmentation","authors":"Tianxin Wang;Xudong Wang","doi":"10.1109/TMC.2024.3487020","DOIUrl":"https://doi.org/10.1109/TMC.2024.3487020","url":null,"abstract":"Backhaul mesh networks are critical for ensuring coverage and connectivity of high-frequency 6G networks. To maintain high throughput, its architecture needs to be augmented by adding relays. However, how to place relays at appropriate sites poses two challenges: 1) there lacks a theory to capture the relationship between a certain change of network architecture and its throughput gain; 2) selecting the best sites for relays is a complicated combinatorial problem. To tackle the first challenge, this paper first establishes a clique-based bottleneck theory, through which a clique-based bottleneck structure of a given network architecture is constructed to determine the network throughput. Based on this bottleneck structure, clique gradients are then computed to quantify the impact of each clique on the overall network throughput. With the clique-based bottleneck theory, the second challenge is resolved by embedding clique gradients into a deep reinforcement learning (DRL) scheme. Specifically, the DRL actions are masked such that only the relay sites that match the highest clique gradients are selected. This DRL-based relay placement (DeepRP) scheme is evaluated via extensive simulations, and performance results show that it can boost network throughput by more than 50%, which is <inline-formula><tex-math>$text{10.4} !-! text{32.1}% $</tex-math></inline-formula> higher than those of baseline schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1744-1758"},"PeriodicalIF":7.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360975","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":"Joint Optimization of Trajectory, Offloading, Caching, and Migration for UAV-Assisted MEC","authors":"Mingxiong Zhao;Rongqian Zhang;Zhenli He;Keqin Li","doi":"10.1109/TMC.2024.3486995","DOIUrl":"https://doi.org/10.1109/TMC.2024.3486995","url":null,"abstract":"UAV-assisted MEC revolutionizes edge computing by deploying UAVs for real-time data processing in areas lacking infrastructure, supporting a wide range of applications from emergency responses to smart cities. Unlike edge servers, UAVs face substantial computational constraints, necessitating a comprehensive strategy that integrates UAV trajectory with task offloading, caching, and migration. Existing studies often overlook the synergy among these strategies, impacting their overall effectiveness. Furthermore, the focus on content pre-caching overlooks task caching’s critical role in addressing high computational demands with limited UAV resources. This research aims to jointly optimize UAV trajectories and task management strategies, including offloading, caching, and migration. Utilizing the Lyapunov optimization framework, we break down the complex optimization problem into manageable subproblems: UAV placement, user-UAV association, task offloading, scheduling, and bandwidth allocation, addressed iteratively using the Block Coordinate Descent method. Specifically, the scheduling subproblem is transformed into a non-convex quadratically constrained quadratic programming problem, managed effectively through semidefinite relaxation and a probabilistic mapping approach. Our simulations show that this integrated approach significantly boosts system throughput and reduces execution times compared to conventional methods. This study enhances the understanding of the interplay between UAV trajectory planning and task management, offering vital theoretical insights for advancing UAV-assisted MEC systems.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1981-1998"},"PeriodicalIF":7.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361000","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":"SITOff: Enabling Size-Insensitive Task Offloading in D2D-Assisted Mobile Edge Computing","authors":"Zheyuan Hu;Jianwei Niu;Tao Ren;Xuefeng Liu;Mohsen Guizani","doi":"10.1109/TMC.2024.3483951","DOIUrl":"https://doi.org/10.1109/TMC.2024.3483951","url":null,"abstract":"Mobile edge computing (MEC), along with device-to-device (D2D) assisted MEC (D-MEC), are promising technologies that could improve the quality-of-experience for mobile devices (MDs) by offloading their tasks to edge servers or nearby idle MDs. There is a popular trend to develop distributed task offloading algorithms using multi-agent reinforcement learning (MARL), whose adoption of central critics during training makes the offloading still size-sensitive. Therefore, this paper proposes a Size-Insensitive Task Offloading (SITOff) algorithm for D-MEC based on fully-distributed offloading without maintaining any central venue. Specifically, taking advantage of the inherent graph-like structure of D-MEC, SITOff adopts graphs to represent MDs’ states and relationships and form each MD's local knowledge about D-MEC through graph computation. Furthermore, considering the limitation of local knowledge in performing whole performance-oriented offloading, each MD utilizes D2D-transmitting to exchange knowledge with its neighbors and form a comprehensive knowledge about D-MEC to enhance the coordination of distributed offloading. Additionally, regarding the different impacts of neighbors’ knowledge, each MD leverages attention mechanisms to selectively learn its neighbors’ knowledge during knowledge-exchange. Extensive experimental results show the superiority of SITOff over state-of-the-art MARL-based offloading algorithms in D-MEC with various MDs, and the easy collaboration of SITOff with curriculum-learning for large-scale D-MEC offloading.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1567-1584"},"PeriodicalIF":7.7,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184523","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":"Mobility and Cost Aware Inference Accelerating Algorithm for Edge Intelligence","authors":"Xin Yuan;Ning Li;Kang Wei;Wenchao Xu;Quan Chen;Hao Chen;Song Guo","doi":"10.1109/TMC.2024.3484158","DOIUrl":"https://doi.org/10.1109/TMC.2024.3484158","url":null,"abstract":"The edge intelligence (EI) has been widely applied recently. Splitting the model between device, edge server, and cloud can significantly improve the performance of EI. The model segmentation without user mobility has been investigated in detail in previous studies. However, in most EI use cases, the end devices are mobile. Few studies have been conducted on this topic. These works still have many issues, such as ignoring the energy consumption of mobile device, inappropriate network assumption, and low effectiveness on adapting user mobility, etc. Therefore, to address the disadvantages of model segmentation and resource allocation in previous studies, we propose mobility and cost aware model segmentation and resource allocation algorithm for accelerating the inference at edge (MCSA). Specifically, in the scenario without user mobility, the loop iteration gradient descent (Li-GD) algorithm is provided. When the mobile user has a large model inference task that needs to be calculated, it will take the energy consumption of mobile user, the communication and computing resource renting cost, and the inference delay into account to find the optimal model segmentation and resource allocation strategy. In the scenario with user mobility, the mobility aware Li-GD (MLi-GD) algorithm is proposed to calculate the optimal strategy. Then, the properties of the proposed algorithms are investigated, including convergence, complexity, and approximation ratio. The experimental results demonstrate the effectiveness of the proposed algorithms.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1530-1549"},"PeriodicalIF":7.7,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184519","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":"Model Poisoning Attack Against Neural Network Interpreters in IoT Devices","authors":"Xianglong Zhang;Feng Li;Huanle Zhang;Haoxin Zhang;Zhijian Huang;Lisheng Fan;Xiuzhen Cheng;Pengfei Hu","doi":"10.1109/TMC.2024.3486218","DOIUrl":"https://doi.org/10.1109/TMC.2024.3486218","url":null,"abstract":"Neural network models have become integral to Internet of Things (IoT) systems, with applications spanning from industrial automation to critical infrastructure management. Despite their prevalence, the deployment of these models within IoT systems introduces distinctive security vulnerabilities. In particular, adversaries may execute model poisoning attacks, which aim to alter the decision-making processes of embedded models, leading to erroneous outcomes. Existing model poisoning attacks necessitate access to extensive auxiliary datasets, such as the training dataset itself or one with same distribution. These requirements often render such attacks impractical in IoT contexts, given the constrained storage and computational resources of IoT devices. This paper proposes the first model poisoning attack against interpreters without auxiliary datasets to manipulate the model’s behavior. We evaluate the attack on three real-world datasets, and results indicate that this attack can successfully coerce the targeted interpreters to produce outcomes aligned with an adversary’s intentions, while maintaining nearly indistinguishable performance from the original model, thereby ensuring its stealthiness. Furthermore, beyond directly affected interpreters, our experiments reveal that four additional interpreters coupled to the poisoned model are indirectly influenced, underscoring the attack’s transferability.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1715-1730"},"PeriodicalIF":7.7,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361011","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":"Non-Intrusive and Efficient Estimation of Antenna 3-D Orientation for WiFi APs","authors":"Dawei Yan;Panlong Yang;Fei Shang;Nikolaos M. Freris;Yubo Yan","doi":"10.1109/TMC.2024.3485228","DOIUrl":"https://doi.org/10.1109/TMC.2024.3485228","url":null,"abstract":"The effectiveness of WiFi-based localization systems heavily relies on the spatial accuracy of WiFi AP. In real-world scenarios, factors such as AP rotation and irregular antenna tilt contribute significantly to inaccuracies, surpassing the impact of imprecise AP location and antenna separation. In this paper, we propose <i>Anteumbler</i>, a non-invasive, accurate, and efficient system for measuring the orientation of each antenna in physical space. By leveraging the fact that maximum received power occurs when a Tx-Rx antenna pair is perfectly aligned, we build a spatial angle model capable of determining antennas’ orientations without prior knowledge. However, achieving comprehensive coverage across the spatial angle necessitates extensive sampling points. To enhance efficiency, we exploit the orthogonality of antenna directivity and polarization, and adopt an iterative algorithm, thereby reducing the number of sampling points by several orders of magnitude. Additionally, to attain the required antenna orientation accuracy, we mitigate the influence of propagation distance using a dual plane intersection model while filtering out ambient noise. Our real-world experiments, covering six antenna types, two antenna layouts, two antenna separations (<inline-formula><tex-math>$lambda /2$</tex-math></inline-formula> and <inline-formula><tex-math>$lambda$</tex-math></inline-formula> ), and three AP heights, demonstrate that <i>Anteumbler</i> achieves median errors below <inline-formula><tex-math>$text{6}^circ$</tex-math></inline-formula> for both elevation and azimuth angles, and exhibits robustness in NLoS and dynamic environments. Moreover, when integrated into the reverse localization system, <i>Anteumbler</i> deployed over LocAP reduces antenna separation error by <inline-formula><tex-math>$10 ,mathrm{mm}$</tex-math></inline-formula>, while for user localization system, its integration over SpotFi reduces user localization error by more than <inline-formula><tex-math>$1 ,mathrm{m}$</tex-math></inline-formula>.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1453-1468"},"PeriodicalIF":7.7,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184514","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":"DeepSelector: A Deep Learning-Based Virtual Network Function Placement Approach in SDN/NFV-Enabled Networks","authors":"Yi Yue;Xiongyan Tang;Ying-Chang Liang;Chang Cao;Lexi Xu;Wencong Yang;Zhiyan Zhang","doi":"10.1109/TMC.2024.3483779","DOIUrl":"https://doi.org/10.1109/TMC.2024.3483779","url":null,"abstract":"The rapid advancement of Software-Defined Networks (SDN) and Network Function Virtualization (NFV) has popularized the adoption of the Service Function Chain (SFC) paradigm for efficient network service delivery. This paradigm leverages the flexibility and cost-effectiveness of deploying Virtual Network Functions (VNFs) as software entities or virtual machines on off-the-shelf servers. Chaining VNFs together allows traffic to be directed through the network as required. However, existing algorithms for traffic steering and routing path computation in SFC suffer from many challenges, including complexity, lack of scalability, and low time efficiency. This paper focuses on addressing the challenges associated with VNF placement and SFC chaining in SDN/NFV-enabled networks. Our objective is to identify an optimal solution for VNF placement that maximizes the utilization of network resources. We formulate the problem as a Binary Integer Programming (BIP) model to accomplish this. Additionally, we propose a novel algorithm called DeepSelector, which incorporates deep learning techniques and an intelligent node selection network to determine the optimal placement of VNFs for SFC requests. Through performance evaluation, we demonstrate that DeepSelector achieves high network resource utilization and offers efficient VNF placement computation, significantly improving overall network performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1759-1773"},"PeriodicalIF":7.7,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361013","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":"FCER: A Federated Cloud-Edge Recommendation Framework With Cluster-Based Edge Selection","authors":"Jiang Wu;Yunchao Yang;Miao Hu;Yipeng Zhou;Di Wu","doi":"10.1109/TMC.2024.3484493","DOIUrl":"https://doi.org/10.1109/TMC.2024.3484493","url":null,"abstract":"The traditional recommendation system provides web services by modeling user behavior characteristics, which also faces the risk of leaking user privacy. To mitigate the rising concern on privacy leakage in recommender systems, federated learning (FL) based recommendation has received tremendous attention, which can preserve data privacy by conducting local model training on clients. However, devices (e.g., mobile phones) used by clients in a recommender system may have limited capacity for computation and communication, which can severely deteriorate FL training efficiency. Besides, offloading local training tasks to the cloud can lead to privacy leakage and excessive pressure to the cloud. To overcome this deficiency, we propose a novel federated cloud-edge recommendation framework, which is called FCER, by offloading local training tasks to powerful and trusted edge servers. The challenge of FCER lies in the heterogeneity of edge servers, which makes the parameter server (PS) deployed in the cloud face difficulty in judiciously selecting edge servers for model training. To address this challenge, we divide the FCER framework into two stages. In the first pre-training stage, edge servers expose their data statistical features protected by local differential privacy (LDP) to the PS so that edge servers can be grouped into clusters. In the second training stage, FCER activates a single cluster in each communication round, ensuring that edge servers with statistical homogenization are not repeatedly involved in FL. The PS only selects a certain number of edge servers with the highest data quality in each cluster for FL. Effective metrics are proposed to dynamically evaluate the data quality of each edge server. Convergence rate analysis is conducted to show the convergence of recommendation algorithms in FCER. We also perform extensive experiments to demonstrate that FCER remarkably outperforms competitive baselines by <inline-formula><tex-math>$3.85%-9.14%$</tex-math></inline-formula> on HR@10 and <inline-formula><tex-math>$1.46%-11.77%$</tex-math></inline-formula> on NDCG@10.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1731-1743"},"PeriodicalIF":7.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361012","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":"Sequential Privacy Budget Recycling for Federated Vector Mean Estimation: A Game-Theoretic Approach","authors":"Jingyi Li;Guangjing Huang;Liekang Zeng;Lin Chen;Xu Chen","doi":"10.1109/TMC.2024.3484010","DOIUrl":"https://doi.org/10.1109/TMC.2024.3484010","url":null,"abstract":"Privacy-preserving vector mean estimation is a crucial primitive in federated analytics. Existing practices usually resort to Local Differentiated Privacy (LDP) mechanisms that inject random noise into users’ vectors when communicating with users and the central server. Due to the privacy-utility trade-off, the privacy budget has been widely recognized as the bottleneck resource that requires well-provisioning. In this paper, we explore the possibility of privacy budget recycling and propose a novel <italic>ChainDP</i> framework enabling users to carry out data aggregation sequentially to recycle the privacy budget. We establish a sequential game to model the user interactions in our framework. We theoretically show the mathematical nature of the sequential game, solve its Nash Equilibrium, and design an incentive mechanism with provable economic properties. To alleviate potential privacy collusion attacks, we further derive a differentially privacy-guaranteed protocol to avoid holistic exposure. Our numerical simulation validates the effectiveness of ChainDP, showing that it can significantly save privacy budget as well as lower estimation error compared to the traditional LDP mechanism.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1308-1321"},"PeriodicalIF":7.7,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143184151","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}