IEEE Transactions on Mobile Computing最新文献_第10页

Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks 基于量子多智能体强化学习的空-空-地集成网络协同移动接入

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-08-18 DOI: 10.1109/TMC.2025.3599683

Gyu Seon Kim;Yeryeong Cho;Jaehyun Chung;Soohyun Park;Soyi Jung;Zhu Han;Joongheon Kim

{"title":"Quantum Multi-Agent Reinforcement Learning for Cooperative Mobile Access in Space-Air-Ground Integrated Networks","authors":"Gyu Seon Kim;Yeryeong Cho;Jaehyun Chung;Soohyun Park;Soyi Jung;Zhu Han;Joongheon Kim","doi":"10.1109/TMC.2025.3599683","DOIUrl":"https://doi.org/10.1109/TMC.2025.3599683","url":null,"abstract":"Achieving global space-air-ground integrated network (SAGIN) access only with CubeSats presents significant challenges such as the access sustainability limitations in specific regions (e.g., polar regions) and the energy efficiency limitations in CubeSats. To tackle these problems, high-altitude long-endurance uncrewed aerial vehicles (HALE-UAVs) can complement these CubeSat shortcomings for providing cooperatively global access sustainability and energy efficiency. However, as the number of CubeSats and HALE-UAVs, increases, the scheduling dimension of each ground station (GS) increases. As a result, each GS can fall into the curse of dimensionality, and this challenge becomes one major hurdle for efficient global access. Therefore, this paper provides a quantum multi-agent reinforcement Learning (QMARL)-based method for scheduling between GSs and CubeSats/HALE-UAVs in order to improve global access availability and energy efficiency. The main reason why the QMARL-based scheduler can be beneficial is that the algorithm facilitates a logarithmic-scale reduction in scheduling action dimensions, which is one critical feature as the number of CubeSats and HALE-UAVs expands. Additionally, individual GSs have different traffic demands depending on their locations and characteristics, thus it is essential to provide differentiated access services. The superiority of the proposed scheduler is validated through data-intensive experiments in realistic CubeSat/HALE-UAV settings.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1200-1218"},"PeriodicalIF":9.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659214","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

DRDST: Low-Latency DAG Consensus Through Robust Dynamic Sharding and Tree-Broadcasting for IoV DRDST：通过鲁棒动态分片和树广播实现IoV低延迟DAG共识

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-08-18 DOI: 10.1109/TMC.2025.3599385

Runhua Chen;Haoxiang Luo;Gang Sun;Hongfang Yu;Dusit Niyato;Schahram Dustdar

{"title":"DRDST: Low-Latency DAG Consensus Through Robust Dynamic Sharding and Tree-Broadcasting for IoV","authors":"Runhua Chen;Haoxiang Luo;Gang Sun;Hongfang Yu;Dusit Niyato;Schahram Dustdar","doi":"10.1109/TMC.2025.3599385","DOIUrl":"https://doi.org/10.1109/TMC.2025.3599385","url":null,"abstract":"The Internet of Vehicles (IoV) is emerging as a pivotal technology for enhancing traffic management and safety. Its rapid development demands solutions for enhanced communication efficiency and reduced latency. However, traditional centralized networks struggle to meet these demands, prompting the exploration of decentralized solutions such as blockchain. Addressing blockchain’s scalability challenges posed by the growing number of nodes and transactions calls for innovative solutions, among which sharding stands out as a pivotal approach to significantly enhance blockchain throughput. However, existing schemes still face challenges related to <italic>a) the impact of vehicle mobility on blockchain consensus, especially for cross-shard transaction; and <italic>b) the strict requirements of low latency consensus in a highly dynamic network. In this paper, we propose a DAG (Directed Acyclic Graph) consensus leveraging Robust Dynamic Sharding and Tree-broadcasting (DRDST) to address these challenges. Specifically, we first develop a standard for evaluating the network stability of nodes, combined with the nodes’ trust values, to propose a novel robust sharding model that is solved through the design of the Genetic Sharding Algorithm (GSA). Then, we optimize the broadcast latency of the whole sharded network by improving the tree-broadcasting to minimize the maximum broadcast latency within each shard. On this basis, we also design a DAG consensus scheme based on an improved hashgraph protocol, which can efficiently handle cross-shard transactions. Finally, the simulation proves the proposed scheme is superior to the comparison schemes in latency, throughput, consensus success rate, and node traffic load.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1236-1253"},"PeriodicalIF":9.2,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659157","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

Security-Enhanced Spatial Range Query Over Large-Scale Encrypted Mobile Cloud Datasets 大规模加密移动云数据集的安全增强空间范围查询

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-08-15 DOI: 10.1109/TMC.2025.3599519

Yinbin Miao;Jiaqi Yu;Jiliang Li;Xinghua Li;Jun Feng;Zhiquan Liu;Robert H. Deng

{"title":"Security-Enhanced Spatial Range Query Over Large-Scale Encrypted Mobile Cloud Datasets","authors":"Yinbin Miao;Jiaqi Yu;Jiliang Li;Xinghua Li;Jun Feng;Zhiquan Liu;Robert H. Deng","doi":"10.1109/TMC.2025.3599519","DOIUrl":"https://doi.org/10.1109/TMC.2025.3599519","url":null,"abstract":"Privacy-preserving spatial range query allows users to obtain valid data based on specific spatial attributes or geographical location while ensuring privacy. However, many existing Privacy-Preserving Spatial Range Query (PSRQ) schemes generally face the problems of low query efficiency and insufficient security when dealing with large-scale mobile cloud data sets, and it is difficult to resist Indistinguishability under Chosen-Plaintext Attack (IND-CPA). To solve these challenges, we first propose an Efficient and Secure Spatial Range Query scheme (ESSRQ), which is based on a dual mobile cloud architecture by integrating Geohash algorithm, Circular Shift Coalesce Zero-Sum Garbled Bloom Filter (CSC-ZGBF) and Symmetric Homomorphic Encryption (SHE), achieving a constant search complexity. However, ESSRQ cannot protect the access patterns, where the cloud server still has the potential to infer attacks based on the index position and even obtain plaintext queries. On this basis, we further propose an extended scheme ESSRQ-PIR, which introduces Private Information Retrieval (PIR) into single mobile cloud-based architecture, effectively prevents the leakage of access patterns, enhances the security of ESSRQ and can also realize efficient query on large-scale cloud datasets. Formal security analysis proves that our proposed schemes are secure against IND-CPA, and extensive experiments demonstrate that our schemes improve the query efficiency by up to nearly 20 times when compared with previous solutions. These features make the proposed schemes particularly suitable for privacy-preserving spatial queries in mobile cloud computing environments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1184-1199"},"PeriodicalIF":9.2,"publicationDate":"2025-08-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659182","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

WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting 多变量时间序列预测的小波旋转路径关注

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-08-14 DOI: 10.1109/TMC.2025.3599406

Aobo Liang;Yan Sun;Nadra Guizani

{"title":"WaveRoRA: Wavelet Rotary Route Attention for Multivariate Time Series Forecasting","authors":"Aobo Liang;Yan Sun;Nadra Guizani","doi":"10.1109/TMC.2025.3599406","DOIUrl":"https://doi.org/10.1109/TMC.2025.3599406","url":null,"abstract":"Various sensors of Internet of Things (IoT) generate massive amounts of mobile traffic data, forming multivariate time series (MTS). Accurate forecasting of MTS facilitates the enhancement of proactive autoscaling and resource allocation in edge networks. While recent Transformer-based models (Transformers) have achieved significant success in MTS forecasting (MTSF), they tend to rely solely on either time-domain or frequency-domain features, which captures inadequate trends and periodic characteristics. To this end, we propose a wavelet learning framework that seamlessly integrates wavelet transforms with Transformers to benefit from time and frequency characteristics. We design a mixing-splitting architecture to model multi-scale wavelet coefficients and utilizes the attention mechanism to capture inter-series dependencies in the wavelet domain. However, the vanilla softmax self-attention (SA) is high-computational-cost and its smoothing effect diminishes the contrast between strong and weak variable correlations. Therefore, we propose a novel attention mechanism: Rotary Route Attention (RoRA). RoRA incorporates rotary positional embeddings to enhance feature diversity and introduces a small number of routing tokens <inline-formula><tex-math>$r$</tex-math></inline-formula> to aggregate information from the <inline-formula><tex-math>$KV$</tex-math></inline-formula> matrices and redistribute it to the <inline-formula><tex-math>$Q$</tex-math></inline-formula> matrix. Such design strengthens interactions among strongly correlated variables while mitigating the impact of weakly correlated noise. We further propose WaveRoRA, a unified model that leverages RoRA capturing inter-series dependencies in the wavelet domain. We conduct extensive experiments on eight real-world datasets. The results indicate that WaveRoRA outperforms existing state-of-the-art models while maintaining lower computational costs.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1287-1301"},"PeriodicalIF":9.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659191","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

Spatio-Temporal Pyramid-Based Multi-Scale Data Completion in Sparse Crowdsensing 基于时空金字塔的稀疏众感多尺度数据补全

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-08-14 DOI: 10.1109/TMC.2025.3599322

Wenbin Liu;Hao Du;En Wang;Jiajian Lv;Weiting Liu;Bo Yang;Jie Wu

{"title":"Spatio-Temporal Pyramid-Based Multi-Scale Data Completion in Sparse Crowdsensing","authors":"Wenbin Liu;Hao Du;En Wang;Jiajian Lv;Weiting Liu;Bo Yang;Jie Wu","doi":"10.1109/TMC.2025.3599322","DOIUrl":"https://doi.org/10.1109/TMC.2025.3599322","url":null,"abstract":"Sparse Crowdsensing has emerged as a crucial and flexible method for collecting spatio-temporal data in various applications, such as traffic management, environmental monitoring, and disaster response. By recruiting users and utilizing their diverse mobile devices, this approach often results in data that is both sparse and multi-scale, complicating the data completion process. Although numerous data completion algorithms have been developed to address data sparsity, most assume that the collected data is of the same or similar scale, rendering them ineffective for multi-scale data. To overcome this limitation, in this paper, we propose a spatio-temporal pyramid-based multi-scale data completion framework in Sparse Crowdsensing. The basic idea is to leverage a pyramid structure to efficiently capture the complex interrelations between different scales. We first develop a Spatial-Temporal Pyramid Construction Module (ST-PC) to handle multi-scale inputs, and then propose a Spatial-Temporal Pyramid Attention Mechanism (ST-PAM) to capture multi-scale correlations while reducing computational complexity. Furthermore, our method incorporates cross-scale constraints to optimize completion performance. Extensive experiments on four real-world spatio-temporal datasets demonstrate the effectiveness of our framework in multi-scale data completion.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1270-1286"},"PeriodicalIF":9.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659218","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

Two-Tier Submodel Partition Framework for Enhancing UAV Swarm Robustness in Forest Fire Detection 增强森林火灾探测无人机群鲁棒性的两层子模型划分框架

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-08-14 DOI: 10.1109/TMC.2025.3599384

Xingyu Li;Wenzhe Zhang;Linfeng Liu;Ping Wang

{"title":"Two-Tier Submodel Partition Framework for Enhancing UAV Swarm Robustness in Forest Fire Detection","authors":"Xingyu Li;Wenzhe Zhang;Linfeng Liu;Ping Wang","doi":"10.1109/TMC.2025.3599384","DOIUrl":"https://doi.org/10.1109/TMC.2025.3599384","url":null,"abstract":"The deployment of Unmanned Aerial Vehicle (UAV) swarm for Forest Fire Detection (FFD) missions presents unique challenges, e.g., the early forest fires are difficult to identify due to environment diversity and feature complexity, especially when some UAVs could be destroyed in harsh environments. To address these challenges, UAV swarm-based FFD missions can leverage advanced deep learning techniques, where online model updates, robustness, and communication overhead control become crucial for ensuring the effectiveness and adaptability of these missions. In this paper, we propose a Two-tier Submodel Partition Framework (TSPF) to enhance the robustness of UAV swarm conducting FFD missions. TSPF utilizes online model updates to adapt to diverse mission environments, thus strengthening the generalization capability of the model. In addition, a graph coloring method, an intragroup backup mechanism, and a Dynamic Server Selection (DSS) mechanism for the grouping are employed to enhance the robustness of FFD missions when some UAVs are destroyed, hence maintaining the high performance of FFD missions in harsh environments. Moreover, TSPF enables submodel updates by aggregating the parameters of selected layers within/between UAV groups, thereby effectively reducing the model parameter uploads (communication overhead) in model training. Experimental evaluations demonstrate that our proposed TSPF significantly improves the detection accuracy of forest fires, enhances the robustness of FFD missions against the destruction of some UAVs, and reduces the communication overhead in FFD missions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"25 1","pages":"1169-1183"},"PeriodicalIF":9.2,"publicationDate":"2025-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145659188","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

DRAM: Digital Twin-Driven Double-Layer Reverse Auction Method for Multi-Platform Vehicular Crowdsensing 数字双驱动双层逆向拍卖多平台车辆众测方法

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-07-31 DOI: 10.1109/TMC.2025.3594488

Zhenning Wang;Yue Cao;Huan Zhou;Xiaokang Zhou;Jiawen Kang;Houbing Song

{"title":"DRAM: Digital Twin-Driven Double-Layer Reverse Auction Method for Multi-Platform Vehicular Crowdsensing","authors":"Zhenning Wang;Yue Cao;Huan Zhou;Xiaokang Zhou;Jiawen Kang;Houbing Song","doi":"10.1109/TMC.2025.3594488","DOIUrl":"https://doi.org/10.1109/TMC.2025.3594488","url":null,"abstract":"Recently, For-Hire Vehicles (FHVs) have emerged as major players in Vehicular CrowdSensing (VCS). However, the heterogeneity of tasks issued by Data Requesters (DRs) and the heterogeneity of sensors equipped on FHVs under different Vehicle Platforms (VPs) bring difficulties to task allocation and execution. It can be concluded that it is important to reasonably analyze the relationship among DRs, VPs, and FHVs, as well as to motivate VPs and FHVs to complete sensing tasks. Therefore, taking advantage of the real-time simulation and intelligent decision-making of Digital Twins (DT), this paper proposes a DT-driven <underline>Double-layer <underline>Reverse <underline>Auction <underline>Method (DRAM). In the first layer, the reverse auction is established between each DR and VPs, and in the second layer, the reverse auction is established between each VP and FHVs. Meanwhile, we also introduce a sensing fairness index to ensure the sensing balance of different sub-regions and consider it in the DRAM process. Here, the idea of backward induction is used to solve the above problems, with the goal of minimizing the overhead of winning VP and the average overhead of all DRs. Finally, the effectiveness of the DRAM proposed in this paper is verified based on the real data set. Compared with the baseline method, DRAM can reduce the average overhead of DR by about 4%-25%. Meanwhile, in terms of sensing fairness, it can be improved by up to 55%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 12","pages":"13725-13742"},"PeriodicalIF":9.2,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145442694","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

User Context Generation for Large Language Models From Mobile Sensing Data 基于移动传感数据的大型语言模型的用户上下文生成

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-07-28 DOI: 10.1109/TMC.2025.3591561

Rui Xing;Zhenzhe Zheng;Fan Wu;Guihai Chen

{"title":"User Context Generation for Large Language Models From Mobile Sensing Data","authors":"Rui Xing;Zhenzhe Zheng;Fan Wu;Guihai Chen","doi":"10.1109/TMC.2025.3591561","DOIUrl":"https://doi.org/10.1109/TMC.2025.3591561","url":null,"abstract":"Large language models (LLMs) exhibit remarkable capabilities in natural language understanding and generation. However, the accuracy of the inference depends deeply on the contexts of queries, especially for personal services. Abundant mobile sensing data collected by sensors embedded in smart devices can proactively capture real-time user contexts. However, raw sensing data are low-quality (e.g., existing missing data and data redundancy) and are incapable of providing accurate contexts. In this work, we present ConGen, a user context generation framework for LLMs, aiming at prompting users’ contexts through their implicit mobile sensing information. ConGen integrates two components: refined data completion and multi-granularity context compression. Specifically, the refined data completion couples data-centric feature selection by leveraging the eXplainable AI (XAI) method into the data imputation model to generate fewer but more informative features for efficient and effective context generation. Additionally, we implement multi-granularity context compression, reducing timestep- and context-level data redundancy while further elevating context quality. Experiment results show that ConGen can generate more accurate context, surpassing competitive baselines by 1.3%-8.3% in context inference on all four datasets. Moreover, context compression significantly reduces redundancy to <inline-formula><tex-math>$1/70sim 1/40$</tex-math></inline-formula> of the original data amount, and further improves the context accuracy. Finally, the enhanced performance of LLMs, as demonstrated by both quantitative and qualitative evaluations of prompting ConGen-generated user contexts, underscores the effectiveness of ConGen.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 12","pages":"13678-13695"},"PeriodicalIF":9.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145442695","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 MU-MIMO Beamforming With Limited Channel Samples in 5G Networks 5G网络中有限信道采样的实时MU-MIMO波束形成

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-07-25 DOI: 10.1109/TMC.2025.3592929

Shaoran Li;Nan Jiang;Chengzhang Li;Shiva Acharya;Yubo Wu;Weijun Xie;Wenjing Lou;Y. Thomas Hou

引用次数: 0

FluidEdge: Expediting Serverless Machine Learning Inference via Bottleneck-Aware Auto-Scaling on Edge SoCs FluidEdge：通过边缘soc上的瓶颈感知自动缩放加速无服务器机器学习推理

IF 9.2 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-07-24 DOI: 10.1109/TMC.2025.3592334

Borui Li;Tiange Xia;Weilong Wang;Jingyuan Zhang;Shuai Wang;Chenhong Cao;Zheng Dong;Shuai Wang

{"title":"FluidEdge: Expediting Serverless Machine Learning Inference via Bottleneck-Aware Auto-Scaling on Edge SoCs","authors":"Borui Li;Tiange Xia;Weilong Wang;Jingyuan Zhang;Shuai Wang;Chenhong Cao;Zheng Dong;Shuai Wang","doi":"10.1109/TMC.2025.3592334","DOIUrl":"https://doi.org/10.1109/TMC.2025.3592334","url":null,"abstract":"Mobile applications based on machine learning (ML) are increasingly relying on offloading to the edge devices for low-latency, resource-efficient computation. Applying serverless computing for these ML applications on the edge offers a promising solution for handling dynamic workloads while meeting user-specified latency service-level objectives (SLOs). However, existing serverless frameworks, with their coarse-grained data parallelism and rigid model partitioning, are inadequate for ML inference on widely adopted edge System-on-Chip (SoC) devices. This paper presents FluidEdge, an edge-native serverless inference framework. FluidEdge identifies bottleneck operators in ML models and addresses them through a novel fine-grained intra-function latency-sensitive auto-scaling approach that dynamically scales inference bottlenecks during online serving. Additionally, it employs inter-function scaling to further prevent latency SLO violations and leverages the unified memory of edge SoCs for efficient data sharing during inference. Experimental results demonstrate that FluidEdge achieves a 37.4% latency improvement and 67.3% -87.6% SLO violation reduction compared to best-performed state-of-the-art serverless inference frameworks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 12","pages":"13586-13599"},"PeriodicalIF":9.2,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145442687","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