IEEE Transactions on Mobile Computing最新文献

{"title":"Mystique: User-Level Adaptation for Real-Time Video Analytics in Edge Networks via Meta-RL","authors":"Xiaohang Shi;Sheng Zhang;Meizhao Liu;Lingkun Meng;Liu Wei;Yingcheng Gu;Kai Liu;Huanyu Cheng;Yu Song;Lei Tang;Andong Zhu;Ning Chen;Zhuzhong Qian","doi":"10.1109/TMC.2024.3514088","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514088","url":null,"abstract":"Deep neural network (DNN)-based real-time video analytics service, as a core module for numerous crucial applications such as augmented reality (AR), has garnered increasing research attention, where mobile edge computing (MEC) is often leveraged to mitigate its real-time processing burden on resource-constrained user devices. For Quality of Experience (QoE) optimization, latest works employ reinforcement learning (RL)-based methods to adaptively adjust configurations (e.g., resolution and frame rate), yet still presenting significant challenges. Firstly, we observe a substantial diversity in QoE patterns among users. Given that existing methods integrate a fixed QoE pattern in parameter training, it is intuitive to customize a policy network for each user. However, this necessitates significant training investment, failing to support on-the-fly deployment for new users. Secondly, given the dual dynamics from both the network and video content in edge video analytics system, existing methods often fall into the dilemma of fitting newly emerged and diverse system states with offline-trained fixed parameters. While it is promising to employ online learning algorithms, most of them struggle to catch up with the high dynamics. We hence propose <monospace>Mystique</monospace>. In real-time edge video analytics domain, it is the first meta-RL-based user-level configuration adaptation framework. Mystique establishes an initial model in offline meta training with model-agnostic meta-learning (MAML), enabling swift online adaptation to new users and system states through limited gradient updates from initial parameters. Comprehensive experiments illustrate that Mystique can improve QoE by 42% on average compared to prior works.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3615-3632"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776241","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":"Optimizing Fault-Tolerant Time-Aware Flow Scheduling in TSN-5G Networks","authors":"Guizhen Li;Shuo Wang;Yudong Huang;Tao Huang;Yuanhao Cui;Zehui Xiong","doi":"10.1109/TMC.2024.3510604","DOIUrl":"https://doi.org/10.1109/TMC.2024.3510604","url":null,"abstract":"The integration of time-sensitive networking (TSN) and fifth-generation (5G) offers a promising solution for real-time and reliable data transmission in the Industrial Internet of Things (IIoT). However, current research focuses on traffic scheduling in TSN-5G networks to support low latency. New challenges arise when TSN-5G networks leverage time-aware shaper (TAS) and frame replication and elimination for reliability (FRER) to achieve low latency and high reliability. Simply combining TAS and FRER (SCTF) requires scheduling all time-triggered (TT) flows and their replica flows, which substantially increases the computational complexity of gate control lists (GCLs) and severely weakens scheduling capabilities. Moreover, the packet elimination function (PEF) in FRER may induce packet misordering. In this paper, we propose an efficient and fault-tolerant time-aware shaper (EF-TAS) mechanism for TSN-5G networks. EF-TAS only allocates timeslots for TT flows, while replica TT (RT) flows are delivered using a best-effort strategy. Due to the potential violation of deadlines in RT flows, we design an adaptive cyclic GCL window (ACGW)-based hybrid scheduling (AHS) algorithm to schedule TT and RT flows differentially. The AHS algorithm utilizes network calculus to ensure the timely arrival of RT flows without affecting the deterministic transmission of TT flows. In particular, we provide upper bounds on the amount of reordering to quantify the disorder caused by PEF and analyze the impact of introducing the packet ordering function (POF) on EF-TAS performance. The evaluation results show that EF-TAS not only meets the reliability and deadline requirements but also significantly reduces the total number of GCL entries and the computation time of GCLs compared to state-of-the-art methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3441-3455"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583184","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":"Service Function Chain Deployment With VNF-Dependent Software Migration in Multi-Domain Networks","authors":"Yuhan Zhang;Ran Wang;Jie Hao;Qiang Wu;Yidan Teng;Ping Wang;Dusit Niyato","doi":"10.1109/TMC.2024.3514173","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514173","url":null,"abstract":"In the 6G era, user demand for low-latency, cost-effective extreme services such as extended reality (XR) and holographic communications has significantly increased. Multi-domain networks, known for their vast capacity and coverage, are essential in fulfilling the growing demand for high-performance services. Despite their potential, these networks face challenges with domain isolation, requiring a software defined network (SDN) controller for inter-domain communication. Network function virtualization (NFV) enhances flexibility of service delivery with customizable service function chain (SFC), yet prior research falls short in delivering low-latency, cost-efficient services in multi-domain NFV networks alongside an unreasonable assumption that software on physical nodes can support the execution of all virtualization network functions (VNFs). In this paper, we study the problem of SFC deployment with VNF-dependent software migration (SD-VDSM) in multi-domain networks. Particularly, we first formulate the problem by setting an objective to minimize the end-to-end communication delay and the associated costs of service provisioning, while simultaneously ensuring load balancing across multi-domain networks. However, complexity of the issue escalates to an intractable level due to the intertwined nature of SFC deployment strategies and VNF-dependent software migration tactics, which mutually influence each other intricately. To tackle this issue, we propose an innovative heuristic algorithm, designated as the Joint SFC Deployment with VNF-Dependent Software Migration Algorithm (JSD-VDSMA). Comprising three fundamental steps, this algorithm is crafted to adeptly resolve the complexities of service provisioning across multi-domain networks. A suite of rigorous experimental assessments is detailed, demonstrating the capability of our proposed JSD-VDSMA. Through these comparative analyses, we demonstrate its effectiveness not only to increase the service acceptance rate but also to diminish both the end-to-end communication delay and resource utilization costs in comparison to its counterparts.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3685-3702"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777921","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":"R2Pricing: A MARL-Based Pricing Strategy to Maximize Revenue in MoD Systems With Ridesharing and Repositioning","authors":"Shuxin Ge;Xiaobo Zhou;Tie Qiu","doi":"10.1109/TMC.2024.3514124","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514124","url":null,"abstract":"Pricing strategy is crucial for improving the revenue of mobility on-demand (MoD) systems by achieving supply-demand equilibrium across different city zones. Modern MoD systems commonly utilize order ridesharing and vehicle repositioning to improve the order completion rate while supporting this equilibrium, thereby improving the revenue. However, most existing pricing strategies overlook the effects of ridesharing and repositioning, resulting in supply-demand mismatch and revenue decline. To fill this gap, we propose a multi-agent reinforcement learning (MARL) based pricing strategy via a mutual attention mechanism, named R2Pricing, where the impact of ridesharing and repositioning is considered. First, we formulate the pricing with ridesharing and repositioning as an optimization problem toward maximum overall revenue. Then, we transform it into a MARL model, where the agent makes coupled decisions about order fare with ridesharing and vehicle income with repositioning for each zone. Next, the agents are clustered based on supply-demand observation and reward to train more efficiently. The pricing messages between agents are generated based on mutual information theory, which is then aggregated with an attention mechanism to estimate the impact of price differences among zones. Finally, simulations based on real-world data are conducted to demonstrate the superiority of R2Pricing over the benchmarks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3552-3566"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776240","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 Encoding and Enhancement for Low-Light Video Analytics in Mobile Edge Networks","authors":"Yuanyi He;Peng Yang;Tian Qin;Jiawei Hou;Ning Zhang","doi":"10.1109/TMC.2024.3514214","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514214","url":null,"abstract":"In this paper, we present our design and analysis of a Joint Encoding and Enhancement (JEE) system for low-light video analytics in mobile edge networks. First, it is observed that, relying solely on a single pipeline for encoding and enhancement of mobile videos proves insufficient, because of the fluctuations in end-edge bandwidth and computing resources. Therefore, two distinct pipelines are introduced in the JEE system, namely, the encode-decode-enhance pipeline and the enhance-encode-decode pipeline. We then characterize the relationship of accuracy, transmission overhead, and computing overhead of these two pipelines through extensive experiments. Considering the significant demands of transmission and computing for low-light videos, we formulate an optimization problem to strike a balance between accuracy and delay, where the available end-edge bandwidth and computing resources are unknown in advance. To solve this mixed-integer nonlinear programming problem, we propose an algorithm based on online gradient descent, enabling adaptive pipeline selection and joint encoding and enhancement configuration. Theoretical analysis indicates that the proposed algorithm achieves sub-linear dynamic regret, highlighting its capability to the accuracy improvement and delay reduction in online environments. Experimental comparison against baselines demonstrates that, JEE can achieve up to a 27.32% increase in accuracy and a 26.18% reduction in delay.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3330-3345"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583182","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":"Efficient Hybrid Transmission for Cell-Free Systems via NOMA and Multiuser Diversity","authors":"Lin Bai;Jinpeng Xu;Jiaxing Wang;Rui Han;Jinho Choi","doi":"10.1109/TMC.2024.3514165","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514165","url":null,"abstract":"Cell-free technology is considered a pivotal advancement for next-generation mobile communications, which can effectively enhance the quality of service for user equipments (UEs) located at the cell edge. For cell-free systems, in this paper, we propose a hybrid downlink transmission method that combines non-orthogonal multiple access (NOMA) and multiuser diversity (MUD). To evaluate the communication performance of the system, we derive closed-form expressions for both instantaneous and average sum rates of UEs using the NOMA and MUD transmission methods. Furthermore, we comprehensively investigate the spectrum efficiency of the NOMA and MUD transmission methods to provide a basis for selecting the hybrid transmission strategy. On the basis of the proposed hybrid transmission strategy, we can derive an optimal hybrid transmission strategy for the scenarios with two access points (APs) and two UEs. Particularly, we extend the aforementioned strategy to the scenarios with multiple UEs, and formulate an optimization problem to maximize the system spectrum efficiency subject to the transmission strategy and power allocation. Furthermore, we propose a low-complexity user selection strategy and power allocation algorithm to solve the problem. Numerical results demonstrate that the hybrid transmission method and power allocation strategy can achieve higher system spectrum efficiency. Our results reveal the influence of key parameters on the downlink spectrum efficiency, analytically and numerically.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3359-3371"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583183","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":"Multicast Scheduling Over Multiple Channels: A Distribution-Embedding Deep Reinforcement Learning Method","authors":"Ran Li;Chuan Huang;Xiaoqi Qin;Dong Yang;Xinyao Nie","doi":"10.1109/TMC.2024.3514169","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514169","url":null,"abstract":"Multicasting is an efficient technique for simultaneously transmitting common messages from the base station (BS) to multiple mobile users (MUs). Multicast scheduling over multiple channels, which aims to jointly minimize the energy consumption of the BS and the latency of serving asynchronized requests from the MUs, is formulated as an infinite-horizon Markov decision process (MDP) problem with a large discrete action space, multiple time-varying constraints, and multiple time-invariant constraints. To address these challenges, this paper proposes a novel distribution-embedding multi-agent proximal policy optimization (DE-MAPPO) algorithm, which consists of one modified MAPPO and one distribution-embedding module. The former one handles the large discrete action space and time-varying constraints by modifying the structure of the actor networks and the training kernel of the conventional MAPPO; and the latter one iteratively adjusts the action distribution to satisfy the time-invariant constraints. Moreover, a performance upper bound of the considered MDP is derived by solving a two-step optimization problem. Finally, numerical results demonstrate that our proposed algorithm outperforms the existing ones in terms of applicability, effectiveness, and robustness, and achieves comparable performance to the derived upper bound.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3502-3519"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777868","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":"Advancing RFID Technology for Virtual Boundary Detection","authors":"Xiaoyu Li;Jia Liu;Zihao Lin;Xuan Liu;Yanyan Wang;Shigeng Zhang;Baoliu Ye","doi":"10.1109/TMC.2024.3514895","DOIUrl":"https://doi.org/10.1109/TMC.2024.3514895","url":null,"abstract":"A boundary is a physical or virtual line that marks the edge or limit of a specific region, which has been widely used in many applications, such as autonomous driving, virtual wall, and robotic lawn mowers. However, none of existing work can well balance the deployability and the scalability of a boundary. In this paper, we propose a brand new RFID-based virtual boundary scheme together with its detection algorithm called RF-Boundary, which has the competitive advantages of being battery-free and easy-to-maintain. We develop two technologies of phase gradient and dual-antenna AoA to address the key challenges posed by RF-boundary, in terms of lack of calibration information and multi-edge interference. Besides, we consider the presence of multipath in the real world applications, model the effect on signals in the dynamic scenarios, and demonstrate the robustness of our phase gradient-based scheme under multipath. We implement a prototype of RF-Boundary with commercial RFID systems and a mobile robot. Extensive experiments verify the feasibility as well as the good performance of RF-Boundary, with a mean detection error of only 8.6 cm.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3407-3422"},"PeriodicalIF":7.7,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583227","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":"FedMTPP: Federated Multivariate Temporal Point Processes for Distributed Event Sequence Forecasting","authors":"Houxin Gong;Haishuai Wang;Peng Zhang;Sheng Zhou;Hongyang Chen;Jiajun Bu","doi":"10.1109/TMC.2024.3509915","DOIUrl":"https://doi.org/10.1109/TMC.2024.3509915","url":null,"abstract":"With the rapid development of mobile network technology and wearable mobile devices, user-scenario interactions generate a large amount of user behavioral data in the form of multivariate event sequences. Due to data isolation, these multi-scenario events need to be jointly trained to achieve better prediction results. However, traditional federated learning methods face significant challenges when handling distributed event sequences. And the effectiveness of existing modeling approaches for event sequences in federated contexts has not been thoroughly explored. To this end, we propose Federated Multivariate Temporal Point Processes (FedMTPP), which enables learning from distributed event sequences within a novel federated learning framework and leverages efficient event modeling technology, MTPP, to forecast future events. Specifically, FedMTPP restores the temporal structure of the original event sequence by rearranging event embeddings and redesigns the autoregressive-based hidden representation computation in traditional MTPP, making it more suitable for federated prediction tasks. Additionally, FedMTPP incorporates advanced encryption techniques to effectively safeguard user privacy and security. Experimental results on both synthetic and real datasets demonstrate that FedMTPP substantially improves the performance of local models and achieves results comparable to state-of-the-art centralized MTPP methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3302-3315"},"PeriodicalIF":7.7,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583270","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":"Optimization of Models and Strategies for Computation Offloading in the Internet of Vehicles: Efficiency and Trust","authors":"Qinghang Gao;Jianmao Xiao;Zhiyong Feng;Jingyu Li;Yang Yu;Hongqi Chen;Qiaoyun Yin","doi":"10.1109/TMC.2024.3509542","DOIUrl":"https://doi.org/10.1109/TMC.2024.3509542","url":null,"abstract":"With the rapid development of the Internet of Vehicles (IoV), vehicles will generate massive data and computation demands, necessitating computation offloading at the edge. However, existing research faces challenges in efficiency and trust. In this paper, we explore the IoV computation offloading from both user and edge facility provider perspectives, working to optimize the quality of experience (QoE), load balancing, and success rate based on challenges to efficiency and trust. First, two vehicle interconnection models are constructed to extend the linkable range of intra-road and inter-road vehicles while considering the maximum link time constraint. Then, a dynamic planning method is proposed, combining the reputation and feedback mechanisms, which can schedule edge resources online based on the cumulative computation latency of each service side, reliability value, and historical behavior. These two phases further improve the efficiency of edge services. Subsequently, blockchain is combined to optimize the trust problem of edge collaboration, and an edge-limited Byzantine fault tolerance local consensus mechanism is proposed to optimize consensus efficiency and ensure the reliability of edge services. Finally, this paper conducts dynamic experiments on real-world datasets, verifying the effectiveness of the proposed algorithm and models in multiple vehicle density datasets and experimental scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3372-3389"},"PeriodicalIF":7.7,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583181","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}