IEEE Transactions on Mobile Computing最新文献

2024 Reviewers List

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-02-05 DOI: 10.1109/TMC.2025.3527174

引用次数: 0

Intelligent End-to-End Deterministic Scheduling Across Converged Networks

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-16 DOI: 10.1109/TMC.2025.3530486

Zongrong Cheng;Weiting Zhang;Dong Yang;Chuan Huang;Hongke Zhang;Xuemin Sherman Shen

{"title":"Intelligent End-to-End Deterministic Scheduling Across Converged Networks","authors":"Zongrong Cheng;Weiting Zhang;Dong Yang;Chuan Huang;Hongke Zhang;Xuemin Sherman Shen","doi":"10.1109/TMC.2025.3530486","DOIUrl":"https://doi.org/10.1109/TMC.2025.3530486","url":null,"abstract":"Deterministic network services play a vital role for supporting emerging real-time applications with bounded low latency, jitter, and high reliability. The deterministic guarantee is penetrated into various types of networks, such as 5G, WiFi, satellite, and edge computing networks. From the user’s perspective, the real-time applications require end-to-end deterministic guarantee across the converged network. In this paper, we investigate the end-to-end deterministic guarantee problem across the whole converged network, aiming to provide a scalable method for different kinds of converged networks to meet the bounded end-to-end latency, jitter, and high reliability demands of each flow, while improving the network scheduling QoS. Particularly, we set up the global end-to-end control plane to abstract the deterministic-related resources from converged network, and model the deterministic flow transmission by using the abstracted resources. With the resource abstraction, our model can work well for different underlying technologies. Given large amounts of abstracted resources in our model, it is difficult for traditional algorithms to fully utilize the resources. Thus, we propose a deep reinforcement learning based end-to-end deterministic-related resource scheduling (E2eDRS) algorithm to schedule the network resources from end to end. By setting the action groups, the E2eDRS can support varying network dimensions both in horizontal and vertical end-to-end deterministic-related network architectures. Experimental results show that E2eDRS can averagely increase 1.33x and 6.01x schedulable flow number for horizontal scheduling compared with MultiDRS and MultiNaive algorithms, respectively. The E2eDRS can also optimize 2.65x and 3.87x server load balance than MultiDRS and MultiNaive algorithms, respectively. For vertical scheduling, the E2eDRS can still perform better on schedulable flow number and server load balance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2504-2518"},"PeriodicalIF":7.7,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563922","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

Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-01-03 DOI: 10.1109/TMC.2025.3525463

Keshav Sood;Shigang Liu;Dinh Duc Nha Nguyen;Neeraj Kumar;Bohao Feng;Shui Yu

{"title":"Alleviating Data Sparsity to Enhance AI Models Robustness in IoT Network Security Context","authors":"Keshav Sood;Shigang Liu;Dinh Duc Nha Nguyen;Neeraj Kumar;Bohao Feng;Shui Yu","doi":"10.1109/TMC.2025.3525463","DOIUrl":"https://doi.org/10.1109/TMC.2025.3525463","url":null,"abstract":"In Internet of Things (IoT) networks, the IoT sensors collect valuable raw data required to sustain Artificial Intelligence (AI) based networks operation. AI models are data-driven as they use the data to make accurate network security, management, and operational decisions. Unfortunately, the sensors are deployed in harsh environments which affects the sensor behaviour and eventually the networks’ operations. Further, IoT devices are typically vulnerable to a range of malicious events. Therefore, IoT sensor's correct operation including resilience to failure is essential for sustained operations. Naturally, the state variables of time-series data can be changed, i.e., the data streams generated in these situations can be incorrect, incomplete or missing, and sparse presenting a significant challenge for real-time decision-making ability of AI models to make explainable and intelligent management and control decisions. In this paper, we aim to alleviate this fundamental problem to predict the missing and faulty reading correctly so that the decision-making ability of the AI models should not deteriorate in the presence of incorrect, missing, and highly imbalanced data sets. We use a novel approach using fuzzy-based information decomposition to recover the missed data values. We use three data sets, and our preliminary results show that our approach effectively recovers the missed or compromised data samples and help AI models in making accurate decision. Finally, the limitations and future work of this research have been discussed.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3764-3778"},"PeriodicalIF":7.7,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143776239","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

Blockchain Assisted Trust Management for Data-Parallel Distributed Learning

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-23 DOI: 10.1109/TMC.2024.3521443

Yuxiao Song;Daojing He;Minghui Dai;Sammy Chan;Kim-Kwang Raymond Choo;Mohsen Guizani

{"title":"Blockchain Assisted Trust Management for Data-Parallel Distributed Learning","authors":"Yuxiao Song;Daojing He;Minghui Dai;Sammy Chan;Kim-Kwang Raymond Choo;Mohsen Guizani","doi":"10.1109/TMC.2024.3521443","DOIUrl":"https://doi.org/10.1109/TMC.2024.3521443","url":null,"abstract":"Machine learning models can support decision-making in mobile terminals (MTs) deployments, but their training generally requires massive datasets and abundant computation resources. This is challenging in practice due to the resource constraints of many MTs. To address this issue, data-parallel distributed learning can be conducted by offloading computation tasks from MTs to the edge-layer nodes. To facilitate the establishment of trust, one can leverage trust management, say to use trust values derived from local model quality and evaluations by other nodes as access criteria. Nonetheless, security and performance considerations remain unsolved. In this paper, we propose a blockchain-assisted dynamic trust management scheme for distributed learning, which comprises nodes attributes registration, trust calculation, information saving, and block writing. The proof of stake (PoS) consensus mechanism is leveraged to enable efficient consensus among the nodes using trust values as stakes. The incentive mechanism and corresponding dynamic optimization are then proposed to further improve system performance and security. The reinforcement-learning approach is leveraged to provide the optimal strategy for nodes’ local iterations and selection. Simulations and security analysis demonstrate that our proposed scheme can achieve an optimal trade-off between efficiency and quality of distributed learning while maintaining system security.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3826-3843"},"PeriodicalIF":7.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777917","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

EdgeLLM: Fast On-Device LLM Inference With Speculative Decoding

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-23 DOI: 10.1109/TMC.2024.3513457

Daliang Xu;Wangsong Yin;Hao Zhang;Xin Jin;Ying Zhang;Shiyun Wei;Mengwei Xu;Xuanzhe Liu

{"title":"EdgeLLM: Fast On-Device LLM Inference With Speculative Decoding","authors":"Daliang Xu;Wangsong Yin;Hao Zhang;Xin Jin;Ying Zhang;Shiyun Wei;Mengwei Xu;Xuanzhe Liu","doi":"10.1109/TMC.2024.3513457","DOIUrl":"https://doi.org/10.1109/TMC.2024.3513457","url":null,"abstract":"Generative tasks, such as text generation and question answering, are essential for mobile applications. Given their inherent privacy sensitivity, executing them on devices is demanded. Nowadays, the execution of these generative tasks heavily relies on the Large Language Models (LLMs). However, the scarce device memory severely hinders the scalability of these models. We present <monospace>EdgeLLM</monospace>, an efficient on-device LLM inference system for models whose sizes exceed the device's memory capacity. <monospace>EdgeLLM</monospace> is built atop speculative decoding, which delegates most tokens to a smaller, memory-resident (draft) LLM. <monospace>EdgeLLM</monospace> integrates three novel techniques: (1) Instead of generating a fixed width and depth token tree, <monospace>EdgeLLM</monospace> proposes compute-efficient branch navigation and verification to pace the progress of different branches according to their accepted probability to prevent the wasteful allocation of computing resources to the wrong branch and to verify them all at once efficiently. (2) It uses a self-adaptive fallback strategy that promptly initiates the verification process when the smaller LLM generates an incorrect token. (3) To not block the generation, <monospace>EdgeLLM</monospace> proposes speculatively generating tokens during large LLM verification with the compute-IO pipeline. Through extensive experiments, <monospace>EdgeLLM</monospace> exhibits impressive token generation speed which is up to 9.3× faster than existing engines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3256-3273"},"PeriodicalIF":7.7,"publicationDate":"2024-12-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564041","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

A Hypergraph Approach to Deep Learning Based Routing in Software-Defined Vehicular Networks

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-20 DOI: 10.1109/TMC.2024.3520657

Ankur Nahar;Nishit Bhardwaj;Debasis Das;Sajal K. Das

{"title":"A Hypergraph Approach to Deep Learning Based Routing in Software-Defined Vehicular Networks","authors":"Ankur Nahar;Nishit Bhardwaj;Debasis Das;Sajal K. Das","doi":"10.1109/TMC.2024.3520657","DOIUrl":"https://doi.org/10.1109/TMC.2024.3520657","url":null,"abstract":"Software-Defined Vehicular Networks (SDVNs) revolutionize modern transportation by enabling dynamic and adaptable communication infrastructures. However, accurately capturing the dynamic communication patterns in vehicular networks, characterized by intricate spatio-temporal dynamics, remains a challenge with traditional graph-based models. Hypergraphs, due to their ability to represent multi-way relationships, provide a more nuanced representation of these dynamics. Building on this hypergraph foundation, we introduce a novel hypergraph-based routing algorithm. We jointly train a model that incorporates Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) using a Deep Deterministic Policy Gradient (DDPG) approach. This model carefully extracts spatial and temporal traffic matrices, capturing elements such as location, time, velocity, inter-dependencies, and distance. An integrated attention mechanism refines these matrices, ensuring precision in capturing vehicular dynamics. The culmination of these components results in routing decisions that are both responsive and anticipatory. Through detailed empirical experiments using a testbed, simulations with OMNeT++, and theoretical assessments grounded in real-world datasets, we demonstrate the distinct advantages of our methodology. Furthermore, when benchmarked against existing solutions, our technique performs better in model interpretability, delay minimization, rapid convergence, reducing complexity, and minimizing memory footprint.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3844-3859"},"PeriodicalIF":7.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777766","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

Minimizing Age of Processed Information Over Unreliable Wireless Network Channels

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-20 DOI: 10.1109/TMC.2024.3520913

Wasin Meesena;Chanikarn Nikunram;Stephen John Turner;Sucha Supittayapornpong

{"title":"Minimizing Age of Processed Information Over Unreliable Wireless Network Channels","authors":"Wasin Meesena;Chanikarn Nikunram;Stephen John Turner;Sucha Supittayapornpong","doi":"10.1109/TMC.2024.3520913","DOIUrl":"https://doi.org/10.1109/TMC.2024.3520913","url":null,"abstract":"The freshness of real-time status processing of time-sensitive information is crucial for many applications, including flight control, image processing, and autonomous vehicles. In this paper, unprocessed information is sent from sensors to a base station over a shared, unreliable wireless network. The base station has a set of dedicated non-preemptive processors with constant processing times to process information from each sensor. The age of processed information is the time elapsed since the generation of the packet that the processor most recently processed. Our objective is to minimize the expected weighted sum of this age over an infinite time horizon. Here, the challenge is the coupling between a scheduling problem under unreliable communications and the processing times. We first break the coupling by tracking the age of information during processing and derive a lower performance bound of the objective. We then design a stationary randomized policy and a Max-Weight policy for two queueing disciplines: no queues and single-packet queues to achieve our objective. We prove that these policies achieve performance within a factor of two from the optimal. In addition, we prove queues are useful to the stationary randomized policies in highly unreliable or large network settings. Our analytical results are further validated by numerical experiments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3567-3578"},"PeriodicalIF":7.7,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777919","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

WiCamera: Vortex Electromagnetic Wave-Based WiFi Imaging

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-18 DOI: 10.1109/TMC.2024.3519623

Leiyang Xu;Xiaolong Zheng;Xinrun Du;Liang Liu;Huadong Ma

{"title":"WiCamera: Vortex Electromagnetic Wave-Based WiFi Imaging","authors":"Leiyang Xu;Xiaolong Zheng;Xinrun Du;Liang Liu;Huadong Ma","doi":"10.1109/TMC.2024.3519623","DOIUrl":"https://doi.org/10.1109/TMC.2024.3519623","url":null,"abstract":"Current WiFi imaging approaches focus on monitoring dynamic targets to facilitate easy object distinction and capture rich signal reflections for image construction. In static object imaging, massive antenna array or emulated antenna array is often necessary. We propose WiCamera, a novel WiFi imaging prototype that utilizes vortex electromagnetic waves (VEMWs) to monitor stationary human postures using commodity WiFi, by generating human silhouettes with only <inline-formula><tex-math>$3 times 3$</tex-math></inline-formula> MIMO. VEMWs possess a helical wavefront with different phase variations, enabling the imaging of stationary objects through different OAM (Orbital Angular Momentum) modes with time-division multiplexing. WiCamera emits three OAM modes waves from WiFi devices and utilizes their phase variations for imaging. By ray tracing the received signals to a target image plane, WiCamera generates a wavefront image. A generative adversarial network (GAN)-based model is further utilized to refine the wavefront image and create a high-resolution human silhouette. The system's output images are evaluated using metrics such as structural similarity index measure (SSIM) and Szymkiewicz-Simpson coefficient (SSC), comparing them to ground truth images captured by cameras. The evaluation shows that WiCamera performs consistently well in various environments and with different users, with an SSIM reaching up to 0.89 and an SSC reaching up to 0.93.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3633-3649"},"PeriodicalIF":7.7,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777867","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

Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-16 DOI: 10.1109/TMC.2024.3519060

Yu Zhang;Yanmin Gong;Lei Fan;Yu Wang;Zhu Han;Yuanxiong Guo

{"title":"Quantum-Assisted Online Task Offloading and Resource Allocation in MEC-Enabled Satellite-Aerial-Terrestrial Integrated Networks","authors":"Yu Zhang;Yanmin Gong;Lei Fan;Yu Wang;Zhu Han;Yuanxiong Guo","doi":"10.1109/TMC.2024.3519060","DOIUrl":"https://doi.org/10.1109/TMC.2024.3519060","url":null,"abstract":"In the era of Internet of Things (IoT), multi-access edge computing (MEC)-enabled satellite-aerial-terrestrial integrated network (SATIN) has emerged as a promising technology to provide massive IoT devices with seamless and reliable communication and computation services. This paper investigates the cooperation of low Earth orbit (LEO) satellites, high altitude platforms (HAPs), and terrestrial base stations (BSs) to provide relaying and computation services for vastly distributed IoT devices. Considering the uncertainty in dynamic SATIN systems, we formulate a stochastic optimization problem to minimize the time-average expected service delay by jointly optimizing resource allocation and task offloading while satisfying the energy constraints. To solve the formulated problem, we first develop a Lyapunov-based online control algorithm to decompose it into multiple one-slot problems. Since each one-slot problem is a large-scale mixed-integer nonlinear program (MINLP) that is intractable for classical computers, we further propose novel hybrid quantum-classical generalized Benders’ decomposition (HQCGBD) algorithms to solve the problem efficiently by leveraging quantum advantages in parallel computing. Numerical results validate the effectiveness of the proposed MEC-enabled SATIN schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3878-3889"},"PeriodicalIF":7.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777866","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-Empowered Federated Incremental Learning for Non-IID Privacy Data

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2024-12-16 DOI: 10.1109/TMC.2024.3517592

Qian Wang;Siguang Chen;Meng Wu;Xue Li

{"title":"Digital Twin-Empowered Federated Incremental Learning for Non-IID Privacy Data","authors":"Qian Wang;Siguang Chen;Meng Wu;Xue Li","doi":"10.1109/TMC.2024.3517592","DOIUrl":"https://doi.org/10.1109/TMC.2024.3517592","url":null,"abstract":"Federated learning (FL) has emerged as a compelling distributed learning paradigm without sharing local original data. However, with ubiquitous non-independent and identically distributed (non-IID) privacy data, the FL suffers from severe performance loss and the privacy leakage by inference attacks. Existing solutions lack a cohesive framework with theoretical support, and their performance optimization and privacy protection are inter-inhibitive or high-cost. In this paper, we propose a digital twin (DT)-empowered federated incremental learning method to tackle the above challenges. First, we construct a DT-empowered federated incremental learning model to achieve cooperative awareness of performance and privacy-preservation. Second, a diffusion model-based selective data synthesis method is designed to provide auxiliary data for FL, it can avoid unnecessary overhead while ensuring the quality of synthetic samples under non-IID. Besides, it alleviates the negative impact of non-IID by allocating a class-balanced sub-dataset to each DT with IID setting. Third, we develop a DT-empowered alternating incremental learning method initiatively, under the premise of ensuring the confidentiality of original dataset, it can achieve efficient FL performance under non-IID with a small amount of synthetic samples. Furthermore, in order to estimate the contribution of each local model accurately, we investigate a comentropy-based federated aggregation strategy, which can obtain a superior global model. By sufficient theoretical analysis, we prove that the proposed methodology can achieve consistent enhancement of performance and privacy-preservation. Simultaneously, the experiments demonstrate that our methodology has efficient privacy-preserving property, it also outperforms other benchmarks on the accuracy and stability of the global model, especially in highly heterogeneous scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 5","pages":"3860-3877"},"PeriodicalIF":7.7,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143777920","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