The spotlight on autonomous aerial vehicles (AAVs) is to enhance wireless communications while ignoring the potential risk of AAVs acting as adversaries. Due to their mobility and flexibility, AAV eavesdroppers pose an immeasurable threat to legitimate wireless transmissions. However, the existing fixed jamming scheme without cooperation cannot counter the flexible and dynamic AAV eavesdropping. In this article, a cooperative intelligent jamming scheme is proposed, authorizing ground jammers (GJs) to interfere with AAV eavesdroppers, generating specific jamming shields between AAV eavesdroppers and legitimate users. Toward this end, we formulate a secrecy capacity maximization problem and model the problem as a decentralized partially observable Markov decision process (Dec-POMDP). To address the challenge of the huge state space and action space with network dynamics, we leverage a deep reinforcement learning (DRL) algorithm with a dueling network and double-Q learning (i.e., dueling double deep Q-network) to train policy networks. Then, we propose a multi-agent mixing network framework (QMIX)-based collaborative jamming algorithm to enable GJs to independently make decisions without sharing local information. Additionally, we perform extensive simulations to validate the superiority of our proposed scheme and present useful insights into practical implementation by elucidating the relationship between the deployment settings of GJs and the instantaneous secrecy capacity.
{"title":"Smart Shield: Prevent Aerial Eavesdropping via Cooperative Intelligent Jamming Based on Multi-Agent Reinforcement Learning","authors":"Qubeijian Wang;Shiyue Tang;Wen Sun;Yin Zhang;Geng Sun;Hong-Ning Dai;Mohsen Guizani","doi":"10.1109/TMC.2024.3505206","DOIUrl":"https://doi.org/10.1109/TMC.2024.3505206","url":null,"abstract":"The spotlight on autonomous aerial vehicles (AAVs) is to enhance wireless communications while ignoring the potential risk of AAVs acting as adversaries. Due to their mobility and flexibility, AAV eavesdroppers pose an immeasurable threat to legitimate wireless transmissions. However, the existing fixed jamming scheme without cooperation cannot counter the flexible and dynamic AAV eavesdropping. In this article, a cooperative intelligent jamming scheme is proposed, authorizing ground jammers (GJs) to interfere with AAV eavesdroppers, generating specific jamming shields between AAV eavesdroppers and legitimate users. Toward this end, we formulate a secrecy capacity maximization problem and model the problem as a decentralized partially observable Markov decision process (Dec-POMDP). To address the challenge of the huge state space and action space with network dynamics, we leverage a deep reinforcement learning (DRL) algorithm with a dueling network and double-Q learning (i.e., dueling double deep Q-network) to train policy networks. Then, we propose a multi-agent mixing network framework (QMIX)-based collaborative jamming algorithm to enable GJs to independently make decisions without sharing local information. Additionally, we perform extensive simulations to validate the superiority of our proposed scheme and present useful insights into practical implementation by elucidating the relationship between the deployment settings of GJs and the instantaneous secrecy capacity.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2995-3011"},"PeriodicalIF":7.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563919","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}
Pub Date : 2024-11-28DOI: 10.1109/TMC.2024.3508260
Gang Li;Jun Cai;Jianfeng Lu;Hongming Chen
In the operational context of a cross-device federated learning (FL), the efficient allocation of resources, such as transmission powers, channels, and computation resources, significantly impacts overall performance. Existing research in cross-device FL has predominantly concentrated on either resource allocation to enhance training accuracy or incentivizing participation, while ignoring their integrated designs for further improving the performance in cross-device FL. Different from existing work, in this paper, we jointly integrate the power allocation, channel assignment, user selection, and allocation of computation frequency into the design of incentive mechanism, where each mobile user plays a dual role as both a buyer and a seller. Because of complex resource allocation, truthfulness guarantee in a dual role scenario, and unavailable prior information, the considered mechanism design problem is challenging. To tackle such combinatorial problem, we propose a Reinforcement Auction Mechanism (RAM), comprising two layers. The upper layer features a Hybrid Action Reinforcement Learning scheme to learn the outcomes of user selection and payments. In the lower layer, each selected mobile user optimizes its resources to maximize its utility. Theoretical analyses affirm that our proposed RAM ensures individual rationality and truthfulness. Extensive simulations have been conducted to validate the effectiveness of the proposed RAM.
{"title":"Incentive Mechanism Design for Cross-Device Federated Learning: A Reinforcement Auction Approach","authors":"Gang Li;Jun Cai;Jianfeng Lu;Hongming Chen","doi":"10.1109/TMC.2024.3508260","DOIUrl":"https://doi.org/10.1109/TMC.2024.3508260","url":null,"abstract":"In the operational context of a cross-device federated learning (FL), the efficient allocation of resources, such as transmission powers, channels, and computation resources, significantly impacts overall performance. Existing research in cross-device FL has predominantly concentrated on either resource allocation to enhance training accuracy or incentivizing participation, while ignoring their integrated designs for further improving the performance in cross-device FL. Different from existing work, in this paper, we jointly integrate the power allocation, channel assignment, user selection, and allocation of computation frequency into the design of incentive mechanism, where each mobile user plays a dual role as both a buyer and a seller. Because of complex resource allocation, truthfulness guarantee in a dual role scenario, and unavailable prior information, the considered mechanism design problem is challenging. To tackle such combinatorial problem, we propose a Reinforcement Auction Mechanism (RAM), comprising two layers. The upper layer features a Hybrid Action Reinforcement Learning scheme to learn the outcomes of user selection and payments. In the lower layer, each selected mobile user optimizes its resources to maximize its utility. Theoretical analyses affirm that our proposed RAM ensures individual rationality and truthfulness. Extensive simulations have been conducted to validate the effectiveness of the proposed RAM.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3059-3075"},"PeriodicalIF":7.7,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563961","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}
Pub Date : 2024-11-27DOI: 10.1109/TMC.2024.3507286
Xinyi Hu;Zihan Chen;Chenyuan Feng;Geyong Min;Tony Q. S. Quek;Howard H. Yang
Federated Learning (FL) stands as a privacy-preserving machine learning paradigm that enables collaborative training of a global model across multiple clients. However, the practical implementation of FL models often confronts challenges arising from data heterogeneity and limited communication resources. To address the aforementioned issues simultaneously, we develop a Sparsified Random Partial Update framework for personalized Federated Learning (SRP-pFed), which builds upon the foundation of dynamic partial model updates. Specifically, we decouple the local model into personal and shared parts to achieve personalization. For each client, the ratio of its personal part associated with the local model, referred to as the update rate, is regularly renewed over the training procedure via a random walk process endowed with reinforced memory. In each global iteration, clients are clustered into different groups where the ones in the same group share a common update rate. Benefiting from such design, SRP-pFed realizes model personalization while substantially reducing communication costs in the uplink transmissions. We conduct extensive experiments on various training tasks with diverse heterogeneous data settings. The results demonstrate that the SRP-pFed consistently outperforms the state-of-the-art methods in test accuracy and communication efficiency.
{"title":"Sparsified Random Partial Model Update for Personalized Federated Learning","authors":"Xinyi Hu;Zihan Chen;Chenyuan Feng;Geyong Min;Tony Q. S. Quek;Howard H. Yang","doi":"10.1109/TMC.2024.3507286","DOIUrl":"https://doi.org/10.1109/TMC.2024.3507286","url":null,"abstract":"Federated Learning (FL) stands as a privacy-preserving machine learning paradigm that enables collaborative training of a global model across multiple clients. However, the practical implementation of FL models often confronts challenges arising from data heterogeneity and limited communication resources. To address the aforementioned issues simultaneously, we develop a Sparsified Random Partial Update framework for personalized Federated Learning (<monospace>SRP-pFed</monospace>), which builds upon the foundation of dynamic partial model updates. Specifically, we decouple the local model into personal and shared parts to achieve personalization. For each client, the ratio of its personal part associated with the local model, referred to as the update rate, is regularly renewed over the training procedure via a random walk process endowed with reinforced memory. In each global iteration, clients are clustered into different groups where the ones in the same group share a common update rate. Benefiting from such design, <monospace>SRP-pFed</monospace> realizes model personalization while substantially reducing communication costs in the uplink transmissions. We conduct extensive experiments on various training tasks with diverse heterogeneous data settings. The results demonstrate that the <monospace>SRP-pFed</monospace> consistently outperforms the state-of-the-art methods in test accuracy and communication efficiency.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3076-3091"},"PeriodicalIF":7.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563896","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}
Pub Date : 2024-11-27DOI: 10.1109/TMC.2024.3507153
Yang Xu;Hangfan Li;Cheng Zhang;Zhiqing Tang;Xiaoxiong Zhong;Ju Ren;Hongbo Jiang;Yaoxue Zhang
As mobile devices proliferate and mobile applications diversify, Mobile Edge Computing (MEC) has become widely adopted to efficiently allocate computing resources at the network edge and alleviate network congestion. In the MEC initial phase, the absence of vital information presents challenges in devising task-offloading policies, and identifying malicious devices responsible for providing inaccurate feedback is complex. To fill in such gaps, we introduce a consortium blockchain-enabled Committee Voting based Task Offloading Model (CVTOM) to collaboratively formulate resource allocation policies and establish deterrence against malicious servers producing erroneous results intentionally. Different voting principle mechanisms of each committee member are first designed in a Blockchain-enabled system which helps to represent the system's resource status. Additionally, we propose a Multi-armed Bandits related Thompson Sampling based Adaptive Preference Optimization (TSAPO) algorithm for task-offloading policy, enhancing the timely identification of potent edge servers to improve computing resource utilization which first considers dynamic edge server space and parallel computing scenarios. The solid proof process greatly contributes to the theoretical analysis of the TSAPO. The simulation experiments demonstrate the delay and budget can be reduced by around 25% and 10% respectively, showcasing the superior performance of our approach.
{"title":"Blockchain-Enabled Multiple Sensitive Task-Offloading Mechanism for MEC Applications","authors":"Yang Xu;Hangfan Li;Cheng Zhang;Zhiqing Tang;Xiaoxiong Zhong;Ju Ren;Hongbo Jiang;Yaoxue Zhang","doi":"10.1109/TMC.2024.3507153","DOIUrl":"https://doi.org/10.1109/TMC.2024.3507153","url":null,"abstract":"As mobile devices proliferate and mobile applications diversify, Mobile Edge Computing (MEC) has become widely adopted to efficiently allocate computing resources at the network edge and alleviate network congestion. In the MEC initial phase, the absence of vital information presents challenges in devising task-offloading policies, and identifying malicious devices responsible for providing inaccurate feedback is complex. To fill in such gaps, we introduce a consortium blockchain-enabled <underline>C</u>ommittee <underline>V</u>oting based <underline>T</u>ask <underline>O</u>ffloading <underline>M</u>odel (CVTOM) to collaboratively formulate resource allocation policies and establish deterrence against malicious servers producing erroneous results intentionally. Different voting principle mechanisms of each committee member are first designed in a Blockchain-enabled system which helps to represent the system's resource status. Additionally, we propose a Multi-armed Bandits related <underline>T</u>hompson <underline>S</u>ampling based <underline>A</u>daptive <underline>P</u>reference <underline>O</u>ptimization (TSAPO) algorithm for task-offloading policy, enhancing the timely identification of potent edge servers to improve computing resource utilization which first considers dynamic edge server space and parallel computing scenarios. The solid proof process greatly contributes to the theoretical analysis of the TSAPO. The simulation experiments demonstrate the delay and budget can be reduced by around 25% and 10% respectively, showcasing the superior performance of our approach.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3241-3255"},"PeriodicalIF":7.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564042","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}
Pub Date : 2024-11-27DOI: 10.1109/TMC.2024.3507035
Zhaohui Li;Yongmin Zhang;Lin Cai;Yaoxue Zhang
Indoor Sound Source Localization (ISSL) is under growing focus with the rapid development of smart IOT intelligence. The predominant approaches typically involve constructing large microphone (Mic) array systems or extracting multiple angles of arrival (AOAs). However, the performance of these solutions is often constrained by the physical size of the array. Besides, there has been limited focus on 3D localization with a single small-sized Mic array. In this paper, we propose HearLoc, an ISSL system that can directly locate 3D sources with a ten-$cm$ Mic array. We demonstrate that the localization ability and dimensional capability can be significantly enhanced by incorporating the time differences of arrival (TDOAs) between the line-of-sight (LOS) and ECHO signals from nearby reflective surfaces. Our approach involves a localization method that selectively sums the correlation powers at useful TDOAs induced by each location. We also design a data processing pipeline with interpolation, normalization and pruning techniques to improve system accuracy and efficiency. To further enhance scalability, we design an iterative algorithm for the ISSL problem with multiple sources and an array location calibration scheme. Experiments demonstrate that the HearLoc can effectively locate sound sources, exhibiting $2times$/$3.7times$ improvements in accuracy for 2D and 3D localization, respectively, and a $4times$ increase in efficiency compared to the existing AOA-based ISSL solutions.
{"title":"HearLoc: Locating Unknown Sound Sources in 3D With a Small-Sized Microphone Array","authors":"Zhaohui Li;Yongmin Zhang;Lin Cai;Yaoxue Zhang","doi":"10.1109/TMC.2024.3507035","DOIUrl":"https://doi.org/10.1109/TMC.2024.3507035","url":null,"abstract":"Indoor Sound Source Localization (ISSL) is under growing focus with the rapid development of smart IOT intelligence. The predominant approaches typically involve constructing large microphone (Mic) array systems or extracting multiple angles of arrival (AOAs). However, the performance of these solutions is often constrained by the physical size of the array. Besides, there has been limited focus on 3D localization with a single small-sized Mic array. In this paper, we propose HearLoc, an ISSL system that can directly locate 3D sources with a ten-<inline-formula><tex-math>$cm$</tex-math></inline-formula> Mic array. We demonstrate that the localization ability and dimensional capability can be significantly enhanced by incorporating the time differences of arrival (TDOAs) between the line-of-sight (LOS) and ECHO signals from nearby reflective surfaces. Our approach involves a localization method that selectively sums the correlation powers at useful TDOAs induced by each location. We also design a data processing pipeline with interpolation, normalization and pruning techniques to improve system accuracy and efficiency. To further enhance scalability, we design an iterative algorithm for the ISSL problem with multiple sources and an array location calibration scheme. Experiments demonstrate that the HearLoc can effectively locate sound sources, exhibiting <inline-formula><tex-math>$2times$</tex-math></inline-formula>/<inline-formula><tex-math>$3.7times$</tex-math></inline-formula> improvements in accuracy for 2D and 3D localization, respectively, and a <inline-formula><tex-math>$4times$</tex-math></inline-formula> increase in efficiency compared to the existing AOA-based ISSL solutions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3163-3177"},"PeriodicalIF":7.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563898","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}
Pub Date : 2024-11-27DOI: 10.1109/TMC.2024.3507051
Wenjun Yang;Lin Cai;Shengjie Shu;Amir Sepahi;Zhiming Huang;Jianping Pan
Video streaming performance may degrade substantially in a mobile environment due to fast-changing wireless links. On the other hand, to provide ubiquitous services, heterogeneous static and mobile access and backbone networks will be integrated in the sixth-generation (6G) systems, so mobile users can take advantage of multiple access options for better services. Multi-path transport-layer protocols like Multi-Path QUIC (MPQUIC) show promise in utilizing multiple access links to address the impact of mobility. However, the optimal link selection that aims to provide statistical QoS guarantee for video streaming in a mobile environment with both user mobility and network mobility remains an open issue. In this paper, based on a lightweight Multi-Armed Bandit (MAB) technique, we develop a QoS-driven Contextual MAB (QC-MAB) framework for MPQUIC, which makes an intelligent access network selection and adaptively enables FEC coding to trade off delay, reliability and goodput. Extensive simulation results with ns-3 show that the proposed QC-MAB framework can outperform the state-of-the-art solutions. It achieves up to ten times lower video interruption ratio and three times higher goodput in highly dynamic mobile environments.
{"title":"QoS-Driven Contextual MAB for MPQUIC Supporting Video Streaming in Mobile Networks","authors":"Wenjun Yang;Lin Cai;Shengjie Shu;Amir Sepahi;Zhiming Huang;Jianping Pan","doi":"10.1109/TMC.2024.3507051","DOIUrl":"https://doi.org/10.1109/TMC.2024.3507051","url":null,"abstract":"Video streaming performance may degrade substantially in a mobile environment due to fast-changing wireless links. On the other hand, to provide ubiquitous services, heterogeneous static and mobile access and backbone networks will be integrated in the sixth-generation (6G) systems, so mobile users can take advantage of multiple access options for better services. Multi-path transport-layer protocols like Multi-Path QUIC (MPQUIC) show promise in utilizing multiple access links to address the impact of mobility. However, the optimal link selection that aims to provide statistical QoS guarantee for video streaming in a mobile environment with both user mobility and network mobility remains an open issue. In this paper, based on a lightweight Multi-Armed Bandit (MAB) technique, we develop a <underline>Q</u>oS-driven <underline>C</u>ontextual <underline>MAB</u> (QC-MAB) framework for MPQUIC, which makes an intelligent access network selection and adaptively enables FEC coding to trade off delay, reliability and goodput. Extensive simulation results with ns-3 show that the proposed QC-MAB framework can outperform the state-of-the-art solutions. It achieves up to ten times lower video interruption ratio and three times higher goodput in highly dynamic mobile environments.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3274-3287"},"PeriodicalIF":7.7,"publicationDate":"2024-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143583272","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}
Pub Date : 2024-11-25DOI: 10.1109/TMC.2024.3505268
Genglin Wang;Zheng Shi;Yanni Yang;Zhenlin An;Guoming Zhang;Pengfei Hu;Xiuzhen Cheng;Jiannong Cao
Recent studies have demonstrated the feasibility of eavesdropping on audio via radio frequency signals or videos, which capture physical surface vibrations from surrounding objects. However, these methods are inadequate for intercepting internally transmitted audio through wired media. In this work, we introduce radio-frequency retroreflector attack (RFRA) and bridge this gap by proposing an RFRA-based eavesdropping system, RF-Parrot${}^{mathbf {2}}$, capable of wirelessly capturing audio signals transmitted through earphone wires. Our system entails embedding a tiny field-effect transistor within the wire to establish a battery-free retroreflector, whose reflective efficiency is correlated with the amplitude of the audio signal. To preserve the details of audio signals, we designed a unique retroreflector using a depletion-mode MOSFET (D-MOSFET). This MOSFET can be triggered by any voltage level present in the audio signals, thus guaranteeing no information loss during activation. However, the D-MOSFET introduces a nonlinear convolution operation on the original audio, resulting in distorted audio eavesdropping. Thus, we devised an engineering solution which utilized a novel convolutional neural network in conjunction with an efficient Parallel WaveGAN vocoder to reconstruct the original audio. Our comprehensive experiments demonstrate a strong similarity between the reconstructed audio and the original, achieving an impressive 95% accuracy in speech command recognition.
{"title":"Wireless Eavesdropping on Wired Audio With Radio-Frequency Retroreflector Attack","authors":"Genglin Wang;Zheng Shi;Yanni Yang;Zhenlin An;Guoming Zhang;Pengfei Hu;Xiuzhen Cheng;Jiannong Cao","doi":"10.1109/TMC.2024.3505268","DOIUrl":"https://doi.org/10.1109/TMC.2024.3505268","url":null,"abstract":"Recent studies have demonstrated the feasibility of eavesdropping on audio via radio frequency signals or videos, which capture physical surface vibrations from surrounding objects. However, these methods are inadequate for intercepting internally transmitted audio through wired media. In this work, we introduce radio-frequency retroreflector attack (RFRA) and bridge this gap by proposing an RFRA-based eavesdropping system, <small>RF-Parrot</small><inline-formula><tex-math>${}^{mathbf {2}}$</tex-math></inline-formula>, capable of wirelessly capturing audio signals transmitted through earphone wires. Our system entails embedding a tiny field-effect transistor within the wire to establish a battery-free retroreflector, whose reflective efficiency is correlated with the amplitude of the audio signal. To preserve the details of audio signals, we designed a unique retroreflector using a depletion-mode MOSFET (D-MOSFET). This MOSFET can be triggered by any voltage level present in the audio signals, thus guaranteeing no information loss during activation. However, the D-MOSFET introduces a nonlinear convolution operation on the original audio, resulting in distorted audio eavesdropping. Thus, we devised an engineering solution which utilized a novel convolutional neural network in conjunction with an efficient Parallel WaveGAN vocoder to reconstruct the original audio. Our comprehensive experiments demonstrate a strong similarity between the reconstructed audio and the original, achieving an impressive 95% accuracy in speech command recognition.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"3178-3195"},"PeriodicalIF":7.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563954","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}
Pub Date : 2024-11-25DOI: 10.1109/TMC.2024.3506221
Xiangchun Chen;Jiannong Cao;Yuvraj Sahni;Mingjin Zhang;Zhixuan Liang;Lei Yang
Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute tasks from end devices. Task offloading is a fundamental problem in CEC that decides when and where tasks are executed upon the arrival of tasks. However, the mobility of users often results in unstable connections, leading to network failures and resource underutilization. Existing works have not adequately addressed joint mobility-aware dependent task offloading and network flow scheduling, resulting in network congestion and suboptimal performance. To address this, we formulate an online joint mobility-aware dependent task offloading and bandwidth allocation problem, to improve the quality of service by reducing task completion time and energy consumption. We introduce a Mobility-aware Digital Twin-assisted Deep Reinforcement Learning (MDT-DRL) algorithm. Our digital twin model equips the reinforcement learning process by providing future states of mobile users, enabling efficient offloading plans for adapting to the mobile CEC system. Experimental results on real-world and synthetic datasets show that MDT-DRL surpasses state-of-the-art baselines on average task completion time and energy consumption.
{"title":"Mobility-Aware Dependent Task Offloading in Edge Computing: A Digital Twin-Assisted Reinforcement Learning Approach","authors":"Xiangchun Chen;Jiannong Cao;Yuvraj Sahni;Mingjin Zhang;Zhixuan Liang;Lei Yang","doi":"10.1109/TMC.2024.3506221","DOIUrl":"https://doi.org/10.1109/TMC.2024.3506221","url":null,"abstract":"Collaborative edge computing (CEC) has emerged as a promising paradigm, enabling edge nodes to collaborate and execute tasks from end devices. Task offloading is a fundamental problem in CEC that decides when and where tasks are executed upon the arrival of tasks. However, the mobility of users often results in unstable connections, leading to network failures and resource underutilization. Existing works have not adequately addressed joint mobility-aware dependent task offloading and network flow scheduling, resulting in network congestion and suboptimal performance. To address this, we formulate an online joint mobility-aware dependent task offloading and bandwidth allocation problem, to improve the quality of service by reducing task completion time and energy consumption. We introduce a Mobility-aware Digital Twin-assisted Deep Reinforcement Learning (MDT-DRL) algorithm. Our digital twin model equips the reinforcement learning process by providing future states of mobile users, enabling efficient offloading plans for adapting to the mobile CEC system. Experimental results on real-world and synthetic datasets show that MDT-DRL surpasses state-of-the-art baselines on average task completion time and energy consumption.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2979-2994"},"PeriodicalIF":7.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563923","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}
Pub Date : 2024-11-25DOI: 10.1109/TMC.2024.3504271
Ruihan Hu;Haochen Yuan;Daimin Tan;Zhongjie Wang
With the rapid growth of distributed learning and workflow orchestration, Federated Edge Learning has emerged as a solution, enabling multiple edge devices to collaboratively train a large model without the need for sharing raw data. Beyond considering bandwidth and computational resource limitations in the Internet of Things (IoT) environment, it is crucial to address the issue of IoT devices often collecting data that lacks timely annotations, which can lead to latency and label deficiency issues. In most Federated Edge Learning mechanisms, clients’ weights are selected for offloading to the server. In this paper, we propose a solution for dynamic edge selection and wireless network allocation under semi-supervised and privacy protection settings, termed Semi-supervised Scheduling and Allocation Optimization for Federated Edge Learning (SSAFL). SSAFL is designed to adapt to various scenarios, including channel state variations, device heterogeneity, resource incentives, deadline control, label deficiencies, and Non-IID data distributions. This adaptability is achieved through the utilization of an Incentive Optimization framework that encompasses bandwidth allocation and device scheduling policies. Within SSAFL, we introduce the concept of a weighted bipartite graph network to tackle the Incentive Optimization problem and achieve a balance in large-scale optimization of device selection. Additionally, to address the label deficiency issue, we devise a Dynamic Timer for deadline control for each client. Comprehensive and confidential results demonstrate that our proposed approach significantly outperforms other Federated Edge Learning baselines.
{"title":"Device Selection and Resource Allocation With Semi-Supervised Method for Federated Edge Learning","authors":"Ruihan Hu;Haochen Yuan;Daimin Tan;Zhongjie Wang","doi":"10.1109/TMC.2024.3504271","DOIUrl":"https://doi.org/10.1109/TMC.2024.3504271","url":null,"abstract":"With the rapid growth of distributed learning and workflow orchestration, Federated Edge Learning has emerged as a solution, enabling multiple edge devices to collaboratively train a large model without the need for sharing raw data. Beyond considering bandwidth and computational resource limitations in the Internet of Things (IoT) environment, it is crucial to address the issue of IoT devices often collecting data that lacks timely annotations, which can lead to latency and label deficiency issues. In most Federated Edge Learning mechanisms, clients’ weights are selected for offloading to the server. In this paper, we propose a solution for dynamic edge selection and wireless network allocation under semi-supervised and privacy protection settings, termed Semi-supervised Scheduling and Allocation Optimization for Federated Edge Learning (SSAFL). SSAFL is designed to adapt to various scenarios, including channel state variations, device heterogeneity, resource incentives, deadline control, label deficiencies, and Non-IID data distributions. This adaptability is achieved through the utilization of an Incentive Optimization framework that encompasses bandwidth allocation and device scheduling policies. Within SSAFL, we introduce the concept of a weighted bipartite graph network to tackle the Incentive Optimization problem and achieve a balance in large-scale optimization of device selection. Additionally, to address the label deficiency issue, we devise a Dynamic Timer for deadline control for each client. Comprehensive and confidential results demonstrate that our proposed approach significantly outperforms other Federated Edge Learning baselines.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2740-2754"},"PeriodicalIF":7.7,"publicationDate":"2024-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563950","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}
Pub Date : 2024-11-25DOI: 10.1109/TMC.2024.3506161
Yuhang Wang;Ying He;F. Richard Yu;Kaishun Wu;Shanzhi Chen
Intelligent transportation systems demand efficient resource allocation and task offloading to ensure low-latency, high-bandwidth vehicular services. The dynamic nature of vehicular environments, characterized by high mobility and extensive interactions among vehicles, necessitates considering time-varying statistical regularities, especially in scenarios with sharp variations. Despite the widespread use of traditional reinforcement learning for resource allocation, its limitations in generalization and interpretability are evident. To overcome these challenges, we propose an Intelligence-based Reinforcement Learning (IRL) algorithm. This algorithm utilizes active inference to infer the real world and maintain an internal model by minimizing free energy. Enhancing the efficiency of active inference, we incorporate prior knowledge as macro guidance, ensuring more accurate and efficient training. By constructing an intelligence-based model, we eliminate the need for designing reward functions, aligning better with human thinking, and providing a method to reflect the learning, information transmission and intelligence accumulation processes. This approach also allows for quantifying intelligence to a certain extent. Considering the dynamic and uncertain nature of vehicular scenarios, we apply the IRL algorithm to environments with constantly changing parameters. Extensive simulations confirm the effectiveness of IRL, significantly improving the generalization and interpretability of intelligent models in vehicular networks.
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