Pub Date : 2024-09-06DOI: 10.1109/TMC.2024.3455331
Anran Li;Guangjing Wang;Ming Hu;Jianfei Sun;Lan Zhang;Luu Anh Tuan;Han Yu
Federated Learning (FL) enables massive local data owners to collaboratively train a deep learning model without disclosing their private data. The importance of local data samples from various data owners to FL models varies widely. This is exacerbated by the presence of noisy data that exhibit large losses similar to important (hard) samples. Currently, there lacks an FL approach that can effectively distinguish hard samples (which are beneficial) from noisy samples (which are harmful). To bridge this gap, we propose the joint Federated Meta-Weighting based Client and Sample Selection (FedMW-CSS) approach to simultaneously mitigate label noise and hard sample selection. It is a bilevel optimization approach for FL client-and-sample selection and global model construction to achieve hard sample-aware noise-robust learning in a privacy preserving manner. It performs meta-learning based online approximation to iteratively update global FL models, select the most positively influential samples and deal with training data noise. To utilize both the instance-level information and class-level information for better performance improvements, FedMW-CSS efficiently learns a class-level weight by manipulating gradients at the class level, e.g., it performs a gradient descent step on class-level weights, which only relies on intermediate gradients. Theoretically, we analyze the privacy guarantees and convergence of FedMW-CSS. Extensive experiments comparison against eight state-of-the-art baselines on six real-world datasets in the presence of data noise and heterogeneity shows that FedMW-CSS achieves up to 28.5% higher test accuracy, while saving communication and computation costs by at least 49.3% and 1.2%, respectively.
{"title":"Joint Client-and-Sample Selection for Federated Learning via Bi-Level Optimization","authors":"Anran Li;Guangjing Wang;Ming Hu;Jianfei Sun;Lan Zhang;Luu Anh Tuan;Han Yu","doi":"10.1109/TMC.2024.3455331","DOIUrl":"10.1109/TMC.2024.3455331","url":null,"abstract":"Federated Learning (FL) enables massive local data owners to collaboratively train a deep learning model without disclosing their private data. The importance of local data samples from various data owners to FL models varies widely. This is exacerbated by the presence of noisy data that exhibit large losses similar to important (hard) samples. Currently, there lacks an FL approach that can effectively distinguish hard samples (which are beneficial) from noisy samples (which are harmful). To bridge this gap, we propose the joint Federated Meta-Weighting based Client and Sample Selection (FedMW-CSS) approach to simultaneously mitigate label noise and hard sample selection. It is a bilevel optimization approach for FL client-and-sample selection and global model construction to achieve hard sample-aware noise-robust learning in a privacy preserving manner. It performs meta-learning based online approximation to iteratively update global FL models, select the most positively influential samples and deal with training data noise. To utilize both the instance-level information and class-level information for better performance improvements, FedMW-CSS efficiently learns a class-level weight by manipulating gradients at the class level, e.g., it performs a gradient descent step on class-level weights, which only relies on intermediate gradients. Theoretically, we analyze the privacy guarantees and convergence of FedMW-CSS. Extensive experiments comparison against eight state-of-the-art baselines on six real-world datasets in the presence of data noise and heterogeneity shows that FedMW-CSS achieves up to 28.5% higher test accuracy, while saving communication and computation costs by at least 49.3% and 1.2%, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180553","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 : 2024-09-05DOI: 10.1109/tmc.2024.3455107
Changqi Zuo, Xu Zhang, Liang Yan, Zuyu Zhang
{"title":"GUGEN: Global User Graph Enhanced Network for Next POI Recommendation","authors":"Changqi Zuo, Xu Zhang, Liang Yan, Zuyu Zhang","doi":"10.1109/tmc.2024.3455107","DOIUrl":"https://doi.org/10.1109/tmc.2024.3455107","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180563","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 : 2024-09-04DOI: 10.1109/TMC.2024.3454634
Panlong Wu;Kangshuo Li;Ting Wang;Yanjie Dong;Victor C. M. Leung;Fangxin Wang
Foundation models (FMs) have shown great success in natural language processing, computer vision, and multimodal tasks. FMs have a large number of model parameters, thus requiring a substantial amount of data to help optimize the model during the training. Federated learning has revolutionized machine learning by enabling collaborative learning from decentralized data while still preserving clients’ data privacy. Despite the great benefits foundation models can have empowered by federated learning, their bulky model parameters cause severe communication challenges for modern networks and computation challenges especially for edge devices. Moreover, the data distribution of different clients can be different thus inducing statistical challenges. In this paper, we propose a novel two-stage federated learning algorithm called FedFMSL. A global expert is trained in the first stage and a local expert is trained in the second stage to provide better personalization. We construct a Mixture of Foundation Models ( MoFM ) with these two experts and design a gate neural network with an inserted gate adapter that joins the aggregation every communication round in the second stage. To further adapt to edge computing scenarios with limited computational resources, we design a novel Sparsely Activated LoRA ( SAL ) algorithm that freezes the pre-trained foundation model parameters inserts low-rank adaptation matrices into transformer blocks, and activates them progressively during the training. We employ extensive experiments to verify the effectiveness of FedFMSL, results show that FedFMSL outperforms other SOTA baselines by up to 59.19% in default settings while tuning less than 0.3% parameters of the foundation model.
{"title":"FedFMSL: Federated Learning of Foundation Models With Sparsely Activated LoRA","authors":"Panlong Wu;Kangshuo Li;Ting Wang;Yanjie Dong;Victor C. M. Leung;Fangxin Wang","doi":"10.1109/TMC.2024.3454634","DOIUrl":"10.1109/TMC.2024.3454634","url":null,"abstract":"Foundation models (FMs) have shown great success in natural language processing, computer vision, and multimodal tasks. FMs have a large number of model parameters, thus requiring a substantial amount of data to help optimize the model during the training. Federated learning has revolutionized machine learning by enabling collaborative learning from decentralized data while still preserving clients’ data privacy. Despite the great benefits foundation models can have empowered by federated learning, their bulky model parameters cause severe communication challenges for modern networks and computation challenges especially for edge devices. Moreover, the data distribution of different clients can be different thus inducing statistical challenges. In this paper, we propose a novel two-stage federated learning algorithm called FedFMSL. A global expert is trained in the first stage and a local expert is trained in the second stage to provide better personalization. We construct a Mixture of Foundation Models (\u0000<monospace>MoFM</monospace>\u0000) with these two experts and design a gate neural network with an inserted gate adapter that joins the aggregation every communication round in the second stage. To further adapt to edge computing scenarios with limited computational resources, we design a novel Sparsely Activated LoRA (\u0000<monospace>SAL</monospace>\u0000) algorithm that freezes the pre-trained foundation model parameters inserts low-rank adaptation matrices into transformer blocks, and activates them progressively during the training. We employ extensive experiments to verify the effectiveness of FedFMSL, results show that FedFMSL outperforms other SOTA baselines by up to 59.19% in default settings while tuning less than 0.3% parameters of the foundation model.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180554","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 : 2024-09-03DOI: 10.1109/TMC.2024.3453657
Yudong Zhang;Xu Wang;Pengkun Wang;Binwu Wang;Zhengyang Zhou;Yang Wang
Spatio-temporal mobility modeling plays a pivotal role in the advancement of mobile computing. Nowadays, data is frequently held by various distributed silos, which are isolated from each other and confront limitations on data sharing. Given this, there have been some attempts to introduce federated learning into spatio-temporal mobility modeling. Meanwhile, the distributional heterogeneity inherent in the spatio-temporal data also puts forward requirements for model personalization. However, the existing methods tackle personalization in a model-centric manner and fail to explore the data characteristics in various data silos, thus ignoring the fact that the fundamental cause of insufficient personalization in the model is the heterogeneous distribution of data. In this paper, we propose a novel distribution-oriented personalized Fed erated learning framework for Cro ss-silo S patio- T emporal mobility modeling (named FedCroST ), that leverages learnable spatio-temporal prompts to implicitly represent the local data distribution patterns of data silos and guide the local models to learn the personalized information. Specifically, we focus on the potential characteristics within temporal distribution and devise a conditional diffusion module to generate temporal prompts that serve as guidance for the evolution of the time series. Simultaneously, we emphasize the structure distribution inherent in node neighborhoods and propose adaptive spatial structure partition to construct the spatial prompts, augmenting the spatial information representation. Furthermore, we introduce a denoising autoencoder to effectively harness the learned multi-view spatio-temporal features and obtain personalized representations adapted to local tasks. Our proposal highlights the significance of latent spatio-temporal data distributions in enabling personalized federated spatio-temporal learning, providing new insights into modeling spatio-temporal mobility in data silo scenarios. Extensive experiments conducted on real-world datasets demonstrate that FedCroST outperforms the advanced baselines by a large margin in diverse cross-silo spatio-temporal mobility modeling tasks.
{"title":"Modeling Spatio-Temporal Mobility Across Data Silos via Personalized Federated Learning","authors":"Yudong Zhang;Xu Wang;Pengkun Wang;Binwu Wang;Zhengyang Zhou;Yang Wang","doi":"10.1109/TMC.2024.3453657","DOIUrl":"10.1109/TMC.2024.3453657","url":null,"abstract":"Spatio-temporal mobility modeling plays a pivotal role in the advancement of mobile computing. Nowadays, data is frequently held by various distributed silos, which are isolated from each other and confront limitations on data sharing. Given this, there have been some attempts to introduce federated learning into spatio-temporal mobility modeling. Meanwhile, the distributional heterogeneity inherent in the spatio-temporal data also puts forward requirements for model personalization. However, the existing methods tackle personalization in a model-centric manner and fail to explore the data characteristics in various data silos, thus ignoring the fact that the fundamental cause of insufficient personalization in the model is the heterogeneous distribution of data. In this paper, we propose a novel distribution-oriented personalized \u0000<underline>Fed</u>\u0000erated learning framework for \u0000<underline>Cro</u>\u0000ss-silo \u0000<underline>S</u>\u0000patio-\u0000<underline>T</u>\u0000emporal mobility modeling (named \u0000<italic>FedCroST</i>\u0000), that leverages learnable spatio-temporal prompts to implicitly represent the local data distribution patterns of data silos and guide the local models to learn the personalized information. Specifically, we focus on the potential characteristics within temporal distribution and devise a conditional diffusion module to generate temporal prompts that serve as guidance for the evolution of the time series. Simultaneously, we emphasize the structure distribution inherent in node neighborhoods and propose adaptive spatial structure partition to construct the spatial prompts, augmenting the spatial information representation. Furthermore, we introduce a denoising autoencoder to effectively harness the learned multi-view spatio-temporal features and obtain personalized representations adapted to local tasks. Our proposal highlights the significance of latent spatio-temporal data distributions in enabling personalized federated spatio-temporal learning, providing new insights into modeling spatio-temporal mobility in data silo scenarios. Extensive experiments conducted on real-world datasets demonstrate that FedCroST outperforms the advanced baselines by a large margin in diverse cross-silo spatio-temporal mobility modeling tasks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180555","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 : 2024-09-03DOI: 10.1109/TMC.2024.3453250
Jingwen Tong;Xinran Li;Liqun Fu;Jun Zhang;Khaled B. Letaief
Restless multi-armed bandits (RMABs) have been widely utilized to address resource allocation problems with Markov reward processes (MRPs). Existing works often assume that the dynamics of MRPs are known prior, which makes the RMAB problem solvable from an optimization perspective. Nevertheless, an efficient learning-based solution for RMABs with unknown system dynamics remains an open problem. In this paper, we fill this gap by investigating a cooperative resource allocation problem with unknown system dynamics of MRPs. This problem can be modeled as a multi-agent online RMAB problem, where multiple agents collaboratively learn the system dynamics while maximizing their accumulated rewards. We devise a federated online RMAB framework to mitigate the communication overhead and data privacy issue by adopting the federated learning paradigm. Based on this framework, we put forth a Federated Thompson Sampling-enabled Whittle Index (FedTSWI) algorithm to solve this multi-agent online RMAB problem. The FedTSWI algorithm enjoys a high communication and computation efficiency, and a privacy guarantee. Moreover, we derive a regret upper bound for the FedTSWI algorithm. Finally, we demonstrate the effectiveness of the proposed algorithm on the case of online multi-user multi-channel access. Numerical results show that the proposed algorithm achieves a fast convergence rate of $mathcal {O}(sqrt{Tlog (T)})$ and better performance compared with baselines. More importantly, its sample complexity reduces sublinearly with the number of agents.
{"title":"A Federated Online Restless Bandit Framework for Cooperative Resource Allocation","authors":"Jingwen Tong;Xinran Li;Liqun Fu;Jun Zhang;Khaled B. Letaief","doi":"10.1109/TMC.2024.3453250","DOIUrl":"10.1109/TMC.2024.3453250","url":null,"abstract":"Restless multi-armed bandits (RMABs) have been widely utilized to address resource allocation problems with Markov reward processes (MRPs). Existing works often assume that the dynamics of MRPs are known prior, which makes the RMAB problem solvable from an optimization perspective. Nevertheless, an efficient learning-based solution for RMABs with unknown system dynamics remains an open problem. In this paper, we fill this gap by investigating a cooperative resource allocation problem with unknown system dynamics of MRPs. This problem can be modeled as a multi-agent online RMAB problem, where multiple agents collaboratively learn the system dynamics while maximizing their accumulated rewards. We devise a federated online RMAB framework to mitigate the communication overhead and data privacy issue by adopting the federated learning paradigm. Based on this framework, we put forth a Federated Thompson Sampling-enabled Whittle Index (FedTSWI) algorithm to solve this multi-agent online RMAB problem. The FedTSWI algorithm enjoys a high communication and computation efficiency, and a privacy guarantee. Moreover, we derive a regret upper bound for the FedTSWI algorithm. Finally, we demonstrate the effectiveness of the proposed algorithm on the case of online multi-user multi-channel access. Numerical results show that the proposed algorithm achieves a fast convergence rate of \u0000<inline-formula><tex-math>$mathcal {O}(sqrt{Tlog (T)})$</tex-math></inline-formula>\u0000 and better performance compared with baselines. More importantly, its sample complexity reduces sublinearly with the number of agents.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180556","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}
Indoor localization availability is still sporadic in industry, especially at the black-hole, i.e., there only exist cellular signals, no GPS or WiFi signals. Based on our 2-year observations at the DiDi ride-hailing platform in China, there are $ 68,text{k}$ orders everyday created at black-hole. In this paper, we present TransparentLoc , a large-scale cellular localization system for pickup position recommendation of the DiDi platform. Specifically, we design a CNN model for real-time localization based on a crowdsourcing fingerprint set constructed by outdoor trajectories and abnormal cell tower detection. Then we leverage a DeepFM model to recommend an optimal pickup position for passengers. We share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, temporal variations, and abnormal cell towers in terms of four major service metrics, i.e., pickup position error, over-30-meters ratio, cancel ratio, and call ratio. The large-scale evaluations show that our system achieves a $ 0.54,text{m}$ lower median pickup position error compared to the iOS built-in cellular localization system, regardless of environmental changes, smartphone brands/models, time, and cellular providers. Additionally, the over-30-meters ratio, cancel ratio, and call ratio have significant reductions of 0.88%, 0.88%, and 5.13%, respectively.
{"title":"Real-World Large-Scale Cellular Localization for Pickup Position Recommendation at Black-Hole","authors":"Ruipeng Gao;Shuli Zhu;Lingkun Li;Xuyu Wang;Yuqin Jiang;Naiqiang Tan;Hua Chai;Peng Qi;Jiqiang Liu;Dan Tao","doi":"10.1109/TMC.2024.3453596","DOIUrl":"10.1109/TMC.2024.3453596","url":null,"abstract":"Indoor localization availability is still sporadic in industry, especially at the black-hole, i.e., there only exist cellular signals, no GPS or WiFi signals. Based on our 2-year observations at the DiDi ride-hailing platform in China, there are \u0000<inline-formula><tex-math>$ 68,text{k}$</tex-math></inline-formula>\u0000 orders everyday created at black-hole. In this paper, we present \u0000<i>TransparentLoc</i>\u0000, a large-scale cellular localization system for pickup position recommendation of the DiDi platform. Specifically, we design a CNN model for real-time localization based on a crowdsourcing fingerprint set constructed by outdoor trajectories and abnormal cell tower detection. Then we leverage a DeepFM model to recommend an optimal pickup position for passengers. We share our 2-year experience with 50 million orders across 13 million devices in 4541 cities to address practical challenges including sparse cell towers, unbalanced user fingerprints, temporal variations, and abnormal cell towers in terms of four major service metrics, i.e., pickup position error, over-30-meters ratio, cancel ratio, and call ratio. The large-scale evaluations show that our system achieves a \u0000<inline-formula><tex-math>$ 0.54,text{m}$</tex-math></inline-formula>\u0000 lower median pickup position error compared to the iOS built-in cellular localization system, regardless of environmental changes, smartphone brands/models, time, and cellular providers. Additionally, the over-30-meters ratio, cancel ratio, and call ratio have significant reductions of 0.88%, 0.88%, and 5.13%, respectively.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180592","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 : 2024-09-03DOI: 10.1109/TMC.2024.3453412
Feiyu Han;Panlong Yang;Yuanhao Feng;Haohua Du;Xiang-Yang Li
As earable devices have become indispensable smart devices in people's lives, earable-based user authentication has gradually attracted widespread attention. In our work, we explore novel in-ear breathing biometrics and design an earable-based authentication approach, named BreathSign , which takes advantage of inward-facing microphones on commercial earphones to capture in-ear breathing sounds for passive authentication. To expand the differences among individuals, we model the process of breathing sound generation, transmission, and reception. Based on that, we derive hard-to-forge physical-level features from in-ear breathing sounds as biometrics. Furthermore, to eliminate the impact of breathing behavioral patterns (e.g., duration and intensity), we design a triple network model to extract breathing behavior-independent features and design an online user template update mechanism for long-term authentication. Extensive experiments with 35 healthy subjects have been conducted to evaluate the performance of BreathSign . The results show that our system achieves the average authentication accuracy of 93.15%, 98.06%, and 99.74% via one, five, and nine breathing cycles, respectively. Regarding the resistance of spoofing attacks, BreathSign could achieve an average EER of approximately 3.5%. Compared with other behavior-based authentication schemes, BreathSign does not require users to perform complex movements or postures but only effortless breathing for authentication and can be easily implemented on commercial earphones with high usability and enhanced security.
{"title":"Exploring Earable-Based Passive User Authentication via Interpretable In-Ear Breathing Biometrics","authors":"Feiyu Han;Panlong Yang;Yuanhao Feng;Haohua Du;Xiang-Yang Li","doi":"10.1109/TMC.2024.3453412","DOIUrl":"10.1109/TMC.2024.3453412","url":null,"abstract":"As earable devices have become indispensable smart devices in people's lives, earable-based user authentication has gradually attracted widespread attention. In our work, we explore novel in-ear breathing biometrics and design an earable-based authentication approach, named \u0000<italic>BreathSign</i>\u0000, which takes advantage of inward-facing microphones on commercial earphones to capture in-ear breathing sounds for passive authentication. To expand the differences among individuals, we model the process of breathing sound generation, transmission, and reception. Based on that, we derive hard-to-forge physical-level features from in-ear breathing sounds as biometrics. Furthermore, to eliminate the impact of breathing behavioral patterns (e.g., duration and intensity), we design a triple network model to extract breathing behavior-independent features and design an online user template update mechanism for long-term authentication. Extensive experiments with 35 healthy subjects have been conducted to evaluate the performance of \u0000<italic>BreathSign</i>\u0000. The results show that our system achieves the average authentication accuracy of 93.15%, 98.06%, and 99.74% via one, five, and nine breathing cycles, respectively. Regarding the resistance of spoofing attacks, \u0000<italic>BreathSign</i>\u0000 could achieve an average EER of approximately 3.5%. Compared with other behavior-based authentication schemes, \u0000<italic>BreathSign</i>\u0000 does not require users to perform complex movements or postures but only effortless breathing for authentication and can be easily implemented on commercial earphones with high usability and enhanced security.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-09-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180568","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 : 2024-09-02DOI: 10.1109/tmc.2024.3453069
Chenyang Wang, Hao Yu, Xiuhua Li, Fei Ma, Xiaofei Wang, Tarik Taleb, Victor C. M. Leung
{"title":"Dependency-Aware Microservice Deployment for Edge Computing: A Deep Reinforcement Learning Approach with Network Representation","authors":"Chenyang Wang, Hao Yu, Xiuhua Li, Fei Ma, Xiaofei Wang, Tarik Taleb, Victor C. M. Leung","doi":"10.1109/tmc.2024.3453069","DOIUrl":"https://doi.org/10.1109/tmc.2024.3453069","url":null,"abstract":"","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.9,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180561","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 : 2024-09-02DOI: 10.1109/TMC.2024.3453042
Jiwen Wang;Rongrong Zhang;Jihong Yu;Ju Ren;Yun Li
The lightweight and energy-efficient Frame Slotted Aloha (FSA) protocol has become a promising MAC protocol in large-scale IoT systems. Existing work on minimizing the age of information (AoI) of FSA protocol cannot significantly benefit from frequent packet generations when the packet generation rate $lambda$ exceeds its throughput $e^{-1}$ . To fill this gap, this paper proposes two age threshold-based algorithms to reduce the AoI of FSA systems for $lambda > e^{-1}$ , namely TF and TF+. Their core ideas are to only allow the nodes with age gain over the configured thresholds to send their packets so that the FSA systems are slimmed to a stable one with $lambda < e^{-1}$ and a polling system, respectively. Technically, we design the threshold configuration rules for the two algorithms and characterize the normalized average AoI. We also conduct simulation and the results show that TF and TF+ achieve lower AoI than the prior works.
{"title":"Age-Efficient Random Access With Load Adaptation","authors":"Jiwen Wang;Rongrong Zhang;Jihong Yu;Ju Ren;Yun Li","doi":"10.1109/TMC.2024.3453042","DOIUrl":"10.1109/TMC.2024.3453042","url":null,"abstract":"The lightweight and energy-efficient Frame Slotted Aloha (FSA) protocol has become a promising MAC protocol in large-scale IoT systems. Existing work on minimizing the age of information (AoI) of FSA protocol cannot significantly benefit from frequent packet generations when the packet generation rate \u0000<inline-formula><tex-math>$lambda$</tex-math></inline-formula>\u0000 exceeds its throughput \u0000<inline-formula><tex-math>$e^{-1}$</tex-math></inline-formula>\u0000. To fill this gap, this paper proposes two age threshold-based algorithms to reduce the AoI of FSA systems for \u0000<inline-formula><tex-math>$lambda > e^{-1}$</tex-math></inline-formula>\u0000, namely TF and TF+. Their core ideas are to only allow the nodes with age gain over the configured thresholds to send their packets so that the FSA systems are slimmed to a stable one with \u0000<inline-formula><tex-math>$lambda < e^{-1}$</tex-math></inline-formula>\u0000 and a polling system, respectively. Technically, we design the threshold configuration rules for the two algorithms and characterize the normalized average AoI. We also conduct simulation and the results show that TF and TF+ achieve lower AoI than the prior works.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":null,"pages":null},"PeriodicalIF":7.7,"publicationDate":"2024-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142180559","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}
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