IEEE Transactions on Mobile Computing最新文献_第5页

OACR$^{2}$2: Online Admission Control and Resource Reservation for 5G Slice Networks With Deep Reinforcement Learning 基于深度强化学习的5G切片网络在线准入控制和资源预留

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-06 DOI: 10.1109/TMC.2025.3548767

Fang Li;Yijun Hao;Shusen Yang;Peng Zhao

{"title":"OACR$^{2}$2: Online Admission Control and Resource Reservation for 5G Slice Networks With Deep Reinforcement Learning","authors":"Fang Li;Yijun Hao;Shusen Yang;Peng Zhao","doi":"10.1109/TMC.2025.3548767","DOIUrl":"https://doi.org/10.1109/TMC.2025.3548767","url":null,"abstract":"Network slicing architecture is expected to fulfill network applications with heterogeneous requirements through efficient slice admission control (SAC) policies. Existing SAC approaches entirely rely on current limited observations to make admission decisions, ignoring the potential impact of future demands. The short-sighted behaviors lead to poor service performance and infrastructure providers’ (InPs’) revenue in practice. In this paper, we propose OACR<inline-formula><tex-math>$^{2}$</tex-math></inline-formula>, an online SAC approach based on deep reinforcement learning (DRL) that can exploit predictable future requests to make more precise admission control decisions for the long-term revenue, and reserve proper resources accordingly. Specifically, we design three novel schemes: (i) a requirement predictor based on long short-term memory (LSTM) and a novel input-output way to predict future unforeseen requests, (ii) a DRL admission controller based on the partially observable Markov decision process model to make precise admission decisions without accurate future request information, with the convergence strictly proved, and (iii) a decision defender to guarantee decision reliability. Extensive experiments on real-world traces demonstrate that compared to the No-wait, Wait-queue, and Wait-earliest time approaches, OACR<inline-formula><tex-math>$^{2}$</tex-math></inline-formula> improves InPs’ revenue and acceptance ratio by up to 40.9% and 16.7%, respectively, without sacrificing online inference time (within 0.9 milliseconds).","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7360-7376"},"PeriodicalIF":7.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550529","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

H-STEP: Heuristic Stable Edge Service Entity Placement for Mobile Virtual Reality Systems H-STEP：移动虚拟现实系统的启发式稳定边缘服务实体布局

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-06 DOI: 10.1109/TMC.2025.3548703

Xuejian Chi;Honglong Chen;Zhichen Ni;Haiyang Sun;Peng Sun;Dongxiao Yu

{"title":"H-STEP: Heuristic Stable Edge Service Entity Placement for Mobile Virtual Reality Systems","authors":"Xuejian Chi;Honglong Chen;Zhichen Ni;Haiyang Sun;Peng Sun;Dongxiao Yu","doi":"10.1109/TMC.2025.3548703","DOIUrl":"https://doi.org/10.1109/TMC.2025.3548703","url":null,"abstract":"Virtual reality (VR) technology, as a latency-sensitive application, can achieve real-time response to enhance the user’s quality of experience (QoE) on edge devices. However, edge servers, unlike internally managed cloud servers, are prone to hardware failures, software abnormalities, and network attacks. Most prior studies have focused on reducing service delay and improving user coverage through service entity (SE) placement, often neglecting the critical impact of edge server malfunctions on user QoE. In this work, we design a stable service entity placement framework that connects users on faulty servers to collaborative edge servers, ensuring seamless task completion. This framework presents two primary challenges: determining the grouping of collaborative edge services and the placement of SEs. To address these challenges, we introduce a heuristic stable service entity placement (H-STEP) scheme. This scheme first determines the grouping of collaborative edge servers using an iterative search algorithm and then places SEs on suitable edge servers via a fast non-dominated sorting genetic placement algorithm. This approach balances stability benefits with total cost, enhancing the system’s economic benefits. We theoretically analyze the performance of H-STEP and derive the performance gap between H-STEP and the optimal scheme. Extensive real-data-driven simulations demonstrate that H-STEP’s performance closely approximates that of the optimal scheme and surpasses existing schemes.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7377-7388"},"PeriodicalIF":7.7,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550607","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

Learning Adaptive Multi-Timescale Scheduling for Mobile Edge Computing 基于移动边缘计算的学习自适应多时间尺度调度

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-05 DOI: 10.1109/TMC.2025.3548533

Yijun Hao;Shusen Yang;Fang Li;Yifan Zhang;Shibo Wang;Xuebin Ren

{"title":"Learning Adaptive Multi-Timescale Scheduling for Mobile Edge Computing","authors":"Yijun Hao;Shusen Yang;Fang Li;Yifan Zhang;Shibo Wang;Xuebin Ren","doi":"10.1109/TMC.2025.3548533","DOIUrl":"https://doi.org/10.1109/TMC.2025.3548533","url":null,"abstract":"In mobile edge computing (MEC), resource scheduling is crucial to task requests’ performance and service providers’ cost, involving multi-layer heterogeneous scheduling decisions. Existing MEC schedulers typically adopt static-timescale scheduling, where scheduling decisions are updated regularly at fixed intervals for all layers. The inflexible updating timescales lead to poor performance in the production networks. In this paper, we propose EdgeTimer, an unprecedented approach that automatically and adaptively determines respective updating timescales of multiple scheduling layers to achieve a better trade-off between the operation cost and service performance. Specifically, we design (i) a three-layer hierarchical deep reinforcement learning (DRL) framework for efficient learning of tightly coupled policies, (ii) a tailored multi-agent DRL algorithm for decentralized scheduling, with the convergence strictly proved, and (iii) a lightweight system defender for deterministic reliability assurance. Furthermore, we apply EdgeTimer to a wide range of Kubernetes scheduling rules, and evaluate it using production traces with different workload patterns. Through extensive trace-driven experiments, we demonstrate that EdgeTimer can significantly decrease the operation cost for service providers without sacrificing the delay performance, thereby improving overall profits, compared with the state-of-the-art approaches.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7297-7311"},"PeriodicalIF":7.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550163","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

Adaptive Stabilization Control by Deep Reinforcement Learning for Hovering Drone Surveillance 基于深度强化学习的悬停无人机自适应稳定控制

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-05 DOI: 10.1109/TMC.2025.3548421

Chao-Yang Lee;Ang-Hsun Tsai;Li-Chun Wang

{"title":"Adaptive Stabilization Control by Deep Reinforcement Learning for Hovering Drone Surveillance","authors":"Chao-Yang Lee;Ang-Hsun Tsai;Li-Chun Wang","doi":"10.1109/TMC.2025.3548421","DOIUrl":"https://doi.org/10.1109/TMC.2025.3548421","url":null,"abstract":"This paper proposes an adaptive stabilization control mechanism by using deep reinforcement learning (DRL) for hovering drones that have to execute a surveillance task for a long time. For long-endurance flights, we design and implement a buoyancy-aided autonomous aerial vehicle (AAV) that can use buoyancy lift to decrease the weight and increase the battery capacity so that the flight time can be significantly extended. However, the balloons of the buoyancy-aided AAV can cause “an inverted pendulum effect” and an instability issue on the drone attitude because the increased surface is easily affected by the gusty wind. We propose a buoyancy-aided adaptive stabilization control (BAASC) method with the DRL to stabilize the attitude and extend the flight time of the quadrotor-based buoyancy-aided AAV. This proposed model can immediately control the speeds of all rotors to balance the attitude based on the current state of the drone. Therefore, the degree of swing can be stabilized, and the inverted pendulum effect can be eliminated. The experimental results reveal that the designed buoyancy-aided AAV with the proposed BAASC scheme can effectively stabilize the attitude to extend the flight time by 112.8% compared with a nonbuoyancy-aided AAV under a gusty wind disturbance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"6720-6733"},"PeriodicalIF":7.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10912732","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550346","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Offloading Decision, User Association, and Resource Allocation in Hierarchical Aerial Computing: Collaboration of UAVs and HAP 分层空中计算中的联合卸载决策、用户关联与资源分配：无人机与HAP的协同

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-05 DOI: 10.1109/TMC.2025.3548668

Ahmadun Nabi;Sangman Moh

{"title":"Joint Offloading Decision, User Association, and Resource Allocation in Hierarchical Aerial Computing: Collaboration of UAVs and HAP","authors":"Ahmadun Nabi;Sangman Moh","doi":"10.1109/TMC.2025.3548668","DOIUrl":"https://doi.org/10.1109/TMC.2025.3548668","url":null,"abstract":"In recent years, applications are becoming increasingly computation-intensive and delay-sensitive owing to the rapid growth of Internet of Things (IoT) devices among ground users (GUs). Mobile edge computing (MEC) presents crucial computational support, but conventional MEC services often fail in remote areas and in disaster scenarios. This study presents a hierarchical aerial computing platform leveraging uncrewed aerial vehicles (UAVs) and high-altitude platform (HAP) to meet the computation demands and latency requirements of various IoT applications for GUs. We propose a joint offloading decision, user association, and resource allocation (JOUR) scheme, utilizing binary offloading from GUs to UAVs and partial offloading from UAVs to HAP. The proposed scheme minimizes the energy consumption and latency while maximizing the load balancing. A matching game-based algorithm addresses the GUs offloading decision and GUs-UAVs association, followed by an enhanced soft actor-critic (ESAC) algorithm for UAV partial offloading decision, UAV computation resource allocation, and HAP computation resource allocation. Our simulation results demonstrate the effectiveness of the JOUR scheme in reducing the energy consumption and latency, while improving the load balancing and task completion rates. This demonstrates its potential for optimizing the hierarchical aerial computing platforms in dynamic IoT environments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7267-7282"},"PeriodicalIF":7.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550593","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

Device Power Saving With Time-Frequency Adaptation: Joint BWP-DRX Design With BWP Switching Delay Considered 具有时频自适应的器件节能：考虑BWP切换延迟的BWP- drx联合设计

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-04 DOI: 10.1109/TMC.2025.3547978

Cheng-Wei Tsai;Kuang-Hsun Lin;Hung-Yu Wei

{"title":"Device Power Saving With Time-Frequency Adaptation: Joint BWP-DRX Design With BWP Switching Delay Considered","authors":"Cheng-Wei Tsai;Kuang-Hsun Lin;Hung-Yu Wei","doi":"10.1109/TMC.2025.3547978","DOIUrl":"https://doi.org/10.1109/TMC.2025.3547978","url":null,"abstract":"In today's ever-growing data traffic landscape, optimizing network power efficiency and performance has become crucial. Discontinuous Reception (DRX) and Bandwidth Parts (BWP) are two key technologies that fulfill this pursuit. DRX is a time-domain power-saving technology that allows user equipment (UE) to switch off their radio frequency module. BWP switching is a frequency domain operation that allows UE to operate on only partial bandwidth for power saving. Investigating the interaction and trade-off between DRX and BWP is a must to optimize network efficiency and enhance network performance. This work proposed a novel BWP-DRX joint mechanism and its analytical model that leverages the concept of “Detect time” with the consideration of BWP switching delay. The model reduces packet loss rate by 50%, packet delay by 36% and increases the energy efficiency rate by 50% when arrival rate is high with the trade-off of 12% power efficiency reduction when arrival rate is low compared to the model without Detect time. The influence of each parameter is further analyzed to reach the best network efficiency under different traffic conditions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6613-6627"},"PeriodicalIF":7.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219695","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

Multi-Agent Moth-Flame Reinforcement Learning Based Broadcast Beam Optimization 基于多智能体蛾焰强化学习的广播波束优化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-04 DOI: 10.1109/TMC.2025.3547946

Shan Huang;Haipeng Yao;Tianle Mai;Di Wu;Jiaqi Xu;F. Richard Yu

{"title":"Multi-Agent Moth-Flame Reinforcement Learning Based Broadcast Beam Optimization","authors":"Shan Huang;Haipeng Yao;Tianle Mai;Di Wu;Jiaqi Xu;F. Richard Yu","doi":"10.1109/TMC.2025.3547946","DOIUrl":"https://doi.org/10.1109/TMC.2025.3547946","url":null,"abstract":"Currently, beamforming antenna array technologies are of utmost importance in 5G communication systems. These technologies are essential for optimizing the coverage and signal quality of the cellular network. However, the optimization of broadcast beams presents significant challenges due to the complex strategy profile space. Each beam can be configured with different widths and heights, making it difficult for conventional algorithms to handle. To address this issue, we propose a novel approach called Multi-Agent Moth-Flame Reinforcement Learning (MAMF-RL) algorithm for broadcast beam optimization. MAMF-RL combines reinforcement learning and moth-flame optimization algorithms to interactively search for the optimal broadcast beams. By decomposing the problem into multiple single-sector antenna configuration problems, MAMF-RL effectively reduces the algorithm complexity. We conducted experiments utilizing real data in an 18-sector wireless coverage area. To evaluate the performance of our proposed method, we compared it with traditional methods such as the particle swarm algorithm. The results demonstrate that our MAMF-RL model achieves an average coverage rate of 1.82% higher and a 13.74% lower overlapping coverage rate compared to traditional methods.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7223-7236"},"PeriodicalIF":7.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550535","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

An AI-Assisted All-in-One Integrated Coronary Artery Disease Diagnosis System Using a Portable Heart Sound Sensor With an On-Board Executable Lightweight Model 一种人工智能辅助的一体化冠状动脉疾病诊断系统，该系统使用便携式心音传感器和机载可执行的轻量级模型

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-04 DOI: 10.1109/TMC.2025.3547842

Haojie Zhang;Fuze Tian;Yang Tan;Lin Shen;Jingyu Liu;Jie Liu;Kun Qian;Yalei Han;Gong Su;Bin Hu;Björn W. Schuller;Yoshiharu Yamamoto

{"title":"An AI-Assisted All-in-One Integrated Coronary Artery Disease Diagnosis System Using a Portable Heart Sound Sensor With an On-Board Executable Lightweight Model","authors":"Haojie Zhang;Fuze Tian;Yang Tan;Lin Shen;Jingyu Liu;Jie Liu;Kun Qian;Yalei Han;Gong Su;Bin Hu;Björn W. Schuller;Yoshiharu Yamamoto","doi":"10.1109/TMC.2025.3547842","DOIUrl":"https://doi.org/10.1109/TMC.2025.3547842","url":null,"abstract":"Heart sounds play a crucial role in assessing Coronary Artery Disease (CAD). The advancement of Artificial Intelligence (AI) technologies has given rise to Computer Audition (CA)-based methods for CAD detection. However, previous research has focused primarily on analyzing and modeling heart sound data, overlooking practical application scenarios. In this work, we design a pervasive heart sound collection device used for high-quality heart sound data acquisition. Moreover, we introduce an on-board executable lightweight network tailored for the designed portable device, referred to as TYKDModel. Further, heart sound data from 41 CAD patients and 22 non-CAD healthy controls are collected using the developed device. Experimental results show that the TYKDModel exhibits low-computational complexity, with 52.16 K parameters and 5.03 M Floating-Point Operations (FLOPs). When deployed on the board, it requires only 1.10 MB of Random Access Memory (RAM) and 236.27 KB of Read-Only Memory (ROM), and takes around 1.72 seconds to perform a classification. Despite the low computational and spatial complexity, the TYKDModel achieves a notable classification accuracy of 85.2%, specificity of 88.6%, and sensitivity of 82.8% on the board. These results indicate the promising potential of AI-assisted all-in-one integrated system for the diagnosis of heart sound-assisted CAD.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7252-7266"},"PeriodicalIF":7.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550604","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Joint Optimization for IRS-Assisted Self-Powered IoT in 5G mmWave Networks 5G毫米波网络中irs辅助自供电物联网的联合优化

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-04 DOI: 10.1109/TMC.2025.3547790

Tao Liu;Suiwen Zhang;Xiaomei Qu;Lijun Yang;Chengjie Li;Yihong Chen

{"title":"Joint Optimization for IRS-Assisted Self-Powered IoT in 5G mmWave Networks","authors":"Tao Liu;Suiwen Zhang;Xiaomei Qu;Lijun Yang;Chengjie Li;Yihong Chen","doi":"10.1109/TMC.2025.3547790","DOIUrl":"https://doi.org/10.1109/TMC.2025.3547790","url":null,"abstract":"Harvesting energy from ambient energy source is the key technology for self-powered Internet of Things (IoT) devices to maintain continuous operation without an external power supply. Motivated by the expansion and popularity of 5G networks, we propose a novel solution for IoT devices which are self-powered via harvesting energy from the millimeter-wave (mmWave) communications in 5G mmWave networks. For overcoming the high path loss in mmWave communications, directional narrow-beam transmission is adopted to provide sufficient link budget between transceivers through beamforming technology, which however makes IoT devices difficult to scavenge energy from the mmWave signals. Hence, we employ multiple intelligent reflecting surfaces (IRSs) to assist in energy harvesting at the IoT devices and data transmission at the 5G users. Considering beam codebook design for 5G mmWave networks, this paper jointly optimizes the Discrete Fourier transform (DFT) codebook-based transmit codevectors at the 5G base station (BS) and the phase shifts of IRS's reflective elements for minimizing BS's transmit power, while satisfying the Signal to Interference plus Noise Ratio (SINR) constraints at users and energy harvesting constraints of IoT devices. Nevertheless, owing to the intricate coupling of variables and discrete constraints, this joint optimization problem is extremely non-convex and non-linear. To address such challenges, we propose a penalty dual-decomposition (PDD)-based algorithm which combines the penalty-based augmented Lagrangian method and block coordinate descent method. It explores the structure of the mmWave channel and performs a double iterations in which the joint optimization problem is decomposed into several simplified subproblems. Simulation results reveal that the above algorithm enhances the energy efficiency as compared to other algorithms.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7092-7106"},"PeriodicalIF":7.7,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550188","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

Towards Efficient, Robust, and Privacy-Preserving Incentives for Crowdsensing via Blockchain 通过b区块链实现高效、稳健和保护隐私的众感激励

IF 7.7 2区计算机科学

IEEE Transactions on Mobile Computing Pub Date : 2025-03-03 DOI: 10.1109/TMC.2025.3546941

Yuanhang Zhou;Fei Tong;Chunming Kong;Shibo He;Guang Cheng

{"title":"Towards Efficient, Robust, and Privacy-Preserving Incentives for Crowdsensing via Blockchain","authors":"Yuanhang Zhou;Fei Tong;Chunming Kong;Shibo He;Guang Cheng","doi":"10.1109/TMC.2025.3546941","DOIUrl":"https://doi.org/10.1109/TMC.2025.3546941","url":null,"abstract":"With the explosive development of mobile devices, mobile crowdsensing (MCS) has emerged as a promising approach for large-scale sensing data collection. In the research of MCS, blockchain technology has been widely adopted to decentralize the traditional mobile crowdsensing and tackle the problem of single point of failure. Incentive mechanisms are devised to boost participation with fairness and truthfulness. However, to better determine the incentive strategy, participants’ privacy can be disclosed on top of the blockchain and obtained by adversaries during the transmission and execution of user data, leading to serious security issues. In this paper, we propose a two-stage incentive scheme with efficiency, robustness and privacy preservation considered based on the combination of blockchain technology and Trusted Execution Environment (TEE). Detailedly, we design two kinds of smart contracts, where on-chain public contracts support the procedure of general crowdsensing interactions, and off-chain private ones enabled by TEE complete the privacy-preserving computations, including an online incentive mechanism for worker recruitment decisions and a truth discovery algorithm for data aggregation. Recovery mechanism and hash check mechanism are introduced to avoid TEE provider failures and TEE providers’ attacks, respectively. Our scheme is proved to be theoretically secure in terms of private information protection, worker participation anonymity, and data aggregation privacy. Experimental results also verify the feasibility and superiority of our incentive scheme.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 8","pages":"7136-7151"},"PeriodicalIF":7.7,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144550430","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