Pub Date : 2025-06-16DOI: 10.1109/TMC.2025.3579960
Zhao Li;Lijuan Zhang;Siwei Le;Kang G. Shin;Jia Liu;Zheng Yan
Due to the broadcast nature of wireless communications, users’ data transmitted wirelessly is susceptible to security/privacy threats. The conventional modulation scheme “loads” all of the user’s transmitted information onto a physical signal. Then, as long as an adversary overhears and processes the signal, s/he may access the user’s information, hence breaching communication privacy. To counter this threat, we propose IRS-DMSC, a Distributed Modulation based Secure Communication (DMSC) scheme by exploiting Intelligent Reflecting Surface (IRS). Under IRS-DMSC, two sub-signals are employed to realize legitimate data transmission. Of these two signals, one is directly generated by the legitimate transmitter (Tx), while the other is obtained by modulating the phase of the direct signal and then reflecting it at the IRS in an indirect way. Both the direct and indirect signal components superimpose on each other at the legitimate receiver (Rx) to produce a waveform identical to that obtained under traditional centralized modulation (CM), so that the legitimate Rx can employ the conventional demodulation method to recover the desired data from the received signal. IRS-DMSC incorporates the characteristics of wireless channels into the modulation process, and hence can fully exploit the randomness of wireless channels to enhance transmission secrecy. However, due to the distribution and randomization of legitimate transmission, it becomes difficult or even impossible for an eavesdropper to wiretap the legitimate user’s information. Furthermore, in order to address the problem of decoding error incurred by the difference of two physical channels’ fading, we develop Relative Phase Calibration (RPC) and Constellation Point Calibration (CPC), to improve decoding correctness at the legitimate Rx. Our method design, experiment, and simulation have shown the proposed IRS-DMSC to prevent eavesdroppers from intercepting legitimate information while maintaining good performance of the legitimate transmission.
{"title":"Distributed Modulation Exploiting IRS for Secure Communications","authors":"Zhao Li;Lijuan Zhang;Siwei Le;Kang G. Shin;Jia Liu;Zheng Yan","doi":"10.1109/TMC.2025.3579960","DOIUrl":"https://doi.org/10.1109/TMC.2025.3579960","url":null,"abstract":"Due to the broadcast nature of wireless communications, users’ data transmitted wirelessly is susceptible to security/privacy threats. The conventional modulation scheme “loads” all of the user’s transmitted information onto a physical signal. Then, as long as an adversary overhears and processes the signal, s/he may access the user’s information, hence breaching communication privacy. To counter this threat, we propose <bold>IRS-DMSC</b>, a <italic>Distributed Modulation based Secure Communication</i> (DMSC) scheme by exploiting <italic>Intelligent Reflecting Surface</i> (IRS). Under IRS-DMSC, two sub-signals are employed to realize legitimate data transmission. Of these two signals, one is directly generated by the legitimate transmitter (Tx), while the other is obtained by modulating the phase of the direct signal and then reflecting it at the IRS in an indirect way. Both the direct and indirect signal components superimpose on each other at the legitimate receiver (Rx) to produce a waveform identical to that obtained under traditional centralized modulation (CM), so that the legitimate Rx can employ the conventional demodulation method to recover the desired data from the received signal. IRS-DMSC incorporates the characteristics of wireless channels into the modulation process, and hence can fully exploit the randomness of wireless channels to enhance transmission secrecy. However, due to the distribution and randomization of legitimate transmission, it becomes difficult or even impossible for an eavesdropper to wiretap the legitimate user’s information. Furthermore, in order to address the problem of decoding error incurred by the difference of two physical channels’ fading, we develop <italic>Relative Phase Calibration</i> (RPC) and <italic>Constellation Point Calibration</i> (CPC), to improve decoding correctness at the legitimate Rx. Our method design, experiment, and simulation have shown the proposed IRS-DMSC to prevent eavesdroppers from intercepting legitimate information while maintaining good performance of the legitimate transmission.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"11193-11208"},"PeriodicalIF":9.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021355","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 : 2025-06-16DOI: 10.1109/TMC.2025.3579692
Meixuan Ren;Haipeng Dai;Linglin Zhang;Tang Liu
With the maturation of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have been able to provide a continuous energy supply by scheduling a Mobile Charger (MC). However, traditional charging modes suffer from fixed charging areas that lack the ability to adapt to variable sensor distributions. This inflexibility yields a gap between energy supply and utilization, resulting in relatively low charging efficiency. To address this issue, we propose an adaptive charging mode that utilizes beam steering to dynamically adjust the charging area, thereby catering to different sensor distributions encountered during the charging process. First, we build a dual-symmetric steering charging model to describe the characteristics of dynamic beam steering, enabling precise manipulation of the charging area. Then, we develop a charging power discretization based on steering angle and charging distance to obtain a finite feasible charging strategy set for MC. We reformalize charging utility maximization under energy constraints as a submodular function maximization problem, and propose an approximate algorithm to solve it. Lastly, simulations and field experiments demonstrate that our scheme outperforms other algorithms by 43.9% on average.
{"title":"Adaptive Charging With Beam Steering","authors":"Meixuan Ren;Haipeng Dai;Linglin Zhang;Tang Liu","doi":"10.1109/TMC.2025.3579692","DOIUrl":"https://doi.org/10.1109/TMC.2025.3579692","url":null,"abstract":"With the maturation of wireless power transfer technology, Wireless Rechargeable Sensor Networks (WRSNs) have been able to provide a continuous energy supply by scheduling a Mobile Charger (MC). However, traditional charging modes suffer from fixed charging areas that lack the ability to adapt to variable sensor distributions. This inflexibility yields a gap between energy supply and utilization, resulting in relatively low charging efficiency. To address this issue, we propose an adaptive charging mode that utilizes beam steering to dynamically adjust the charging area, thereby catering to different sensor distributions encountered during the charging process. First, we build a dual-symmetric steering charging model to describe the characteristics of dynamic beam steering, enabling precise manipulation of the charging area. Then, we develop a charging power discretization based on steering angle and charging distance to obtain a finite feasible charging strategy set for MC. We reformalize charging utility maximization under energy constraints as a submodular function maximization problem, and propose an approximate algorithm to solve it. Lastly, simulations and field experiments demonstrate that our scheme outperforms other algorithms by 43.9% on average.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"11224-11240"},"PeriodicalIF":9.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021407","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}
Bluetooth Low Energy (BLE) technology supports various Internet-of-Things (IoT) applications. However, because of their limited transmission power and channel interference, their performance is deficient over weak links. Extending physical layer symbols or using error correction code to the link layer is effective somehow. Introducing excessive BLE bits to both respectively can also decrease the network throughput. To optimize the BLE technology performance, we propose CPL, a combining PHY and link layer optimization technology that adaptively allocates BLE bits to both the physical layer and link layer. Then we propose the Cross-Layer BLE Bits Dynamic Allocation Model that unifies the gain of BLE bits in different layers. Finally, we propose an Interference-Aware Controlled CFO Fine-Tuning Method that calibrates the model according to different interference patterns. We implement CPL on Commercial-Off-The-Shelf (COTS) BLE chips and SDR. The experiment results show that under various interference conditions, CPL achieves 50× and 32.16% throughput improvement over RSBLE and Symphony. CPL reduces energy consumption by 60.42% to 97.95% compared to RSBLE, and 11.04% to 25.15% compared to Symphony.
{"title":"Combating BLE Weak Links by Combining PHY Layer Symbol Extension and Link Layer Coding","authors":"Renjie Li;Yeming Li;Jiamei Lv;Hailong Lin;Yi Gao;Wei Dong","doi":"10.1109/TMC.2025.3579934","DOIUrl":"https://doi.org/10.1109/TMC.2025.3579934","url":null,"abstract":"Bluetooth Low Energy (BLE) technology supports various Internet-of-Things (IoT) applications. However, because of their limited transmission power and channel interference, their performance is deficient over weak links. Extending physical layer symbols or using error correction code to the link layer is effective somehow. Introducing excessive BLE bits to both respectively can also decrease the network throughput. To optimize the BLE technology performance, we propose <italic>CPL</i>, a combining PHY and link layer optimization technology that adaptively allocates BLE bits to both the physical layer and link layer. Then we propose the <italic>Cross-Layer BLE Bits Dynamic Allocation Model</i> that unifies the gain of BLE bits in different layers. Finally, we propose an <italic>Interference-Aware Controlled CFO Fine-Tuning Method</i> that calibrates the model according to different interference patterns. We implement <italic>CPL</i> on Commercial-Off-The-Shelf (COTS) BLE chips and SDR. The experiment results show that under various interference conditions, <italic>CPL</i> achieves 50× and 32.16% throughput improvement over RSBLE and Symphony. <italic>CPL</i> reduces energy consumption by 60.42% to 97.95% compared to RSBLE, and 11.04% to 25.15% compared to Symphony.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"11277-11291"},"PeriodicalIF":9.2,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021315","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}
Cooperative reconfigurable intelligent surfaces (RISs) are promising technologies for 6G networks to support a great number of users. Compared with the fixed RISs, the properly deployed RISs may improve the communication performance with less communication energy consumption, thereby improving the energy efficiency. In this paper, we consider a cooperative uncrewed aerial vehicle-mounted RISs (UAV-RISs)-assisted cellular network, where multiple RISs are carried and enhanced by UAVs to serve multiple ground users (GUs) simultaneously such that achieving the three-dimensional (3D) mobility and opportunistic deployment. Specifically, we formulate an energy-efficient communication problem based on multi-objective optimization framework (EEComm-MOF) to jointly consider the beamforming vector of base station (BS), the location deployment and the discrete phase shifts of UAV-RIS system so as to simultaneously maximize the minimum available rate over all GUs, maximize the total available rate of all GUs, and minimize the total energy consumption of the system, while the transmit power constraint of BS is considered. To comprehensively solve EEComm-MOF which is an NP-hard and non-convex problem with constraints, a non-dominated sorting genetic algorithm-II with a continuous solution processing mechanism, a discrete solution processing mechanism, and a complex solution processing mechanism (INSGA-II-CDC) is proposed. Simulations results demonstrate that the proposed INSGA-II-CDC can solve EEComm-MOF effectively and outperforms other benchmarks under different parameter settings. Moreover, the stability of INSGA-II-CDC and the effectiveness of the improved mechanisms are verified. Finally, the implementability analysis of the algorithm is given.
{"title":"Cooperative UAV-Mounted RISs-Assisted Energy-Efficient Communications","authors":"Hongyang Pan;Yanheng Liu;Geng Sun;Qingqing Wu;Tierui Gong;Pengfei Wang;Dusit Niyato;Chau Yuen","doi":"10.1109/TMC.2025.3579597","DOIUrl":"https://doi.org/10.1109/TMC.2025.3579597","url":null,"abstract":"Cooperative reconfigurable intelligent surfaces (RISs) are promising technologies for 6G networks to support a great number of users. Compared with the fixed RISs, the properly deployed RISs may improve the communication performance with less communication energy consumption, thereby improving the energy efficiency. In this paper, we consider a cooperative uncrewed aerial vehicle-mounted RISs (UAV-RISs)-assisted cellular network, where multiple RISs are carried and enhanced by UAVs to serve multiple ground users (GUs) simultaneously such that achieving the three-dimensional (3D) mobility and opportunistic deployment. Specifically, we formulate an energy-efficient communication problem based on multi-objective optimization framework (EEComm-MOF) to jointly consider the beamforming vector of base station (BS), the location deployment and the discrete phase shifts of UAV-RIS system so as to simultaneously maximize the minimum available rate over all GUs, maximize the total available rate of all GUs, and minimize the total energy consumption of the system, while the transmit power constraint of BS is considered. To comprehensively solve EEComm-MOF which is an NP-hard and non-convex problem with constraints, a non-dominated sorting genetic algorithm-II with a continuous solution processing mechanism, a discrete solution processing mechanism, and a complex solution processing mechanism (INSGA-II-CDC) is proposed. Simulations results demonstrate that the proposed INSGA-II-CDC can solve EEComm-MOF effectively and outperforms other benchmarks under different parameter settings. Moreover, the stability of INSGA-II-CDC and the effectiveness of the improved mechanisms are verified. Finally, the implementability analysis of the algorithm is given.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"11241-11258"},"PeriodicalIF":9.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021134","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 : 2025-06-12DOI: 10.1109/TMC.2025.3579505
Qichao Xu;Zhou Su;Haixia Peng;Yuan Wu;Ruidong Li
Recently, the promising unmanned aerial vehicle (UAV)-assisted wireless networks (UAWNs) have emerged by advocating the UAVs to provide wireless transmission services. However, owing to the ever-growing volume of data traffic and the untrusted network operation environment, efficiently and securely assigning limited bandwidth for high-quality wireless communication between UAVs and mobile users poses a significant challenge. To address this challenge, we propose a novel secure UAV-bandwidth allocation scheme to provision reliable wireless transmission services for mobile users in UAWNs. Specifically, we first introduce a novel blockchain-empowered framework for secure bandwidth allocation, designed to automate payment processes and deter malicious activities through the immutable logging of transactional and behavioral data. Wherein, a smart contract is designed to regulate the honest behaviors of both mobile users and UAVs during bandwidth allocation with a distributed manner. Besides, a delegated proof-of-stake (DPoS) with reputation consensus protocol is presented to ensure the authenticity and efficiency of the decision-making process. Further, we apply the Stackelberg game theory to model the dynamic of the bandwidth allocation between mobile users and UAVs. In this game, the UAVs act as game leaders to determine the bandwidth price, while each mobile user acts as a game follower, making decision on the bandwidth request. We utilize the backward induction method to derive the optimal strategies of both parties, culminating in the identification of the Stackelberg equilibrium of the formulated game. Finally, extensive simulations are carried out to show the superiority of the proposed scheme over conventional schemes in terms of security, efficiency, and fairness in bandwidth allocation.
{"title":"Blockchain-Empowered Game Theoretical Incentive for Secure Bandwidth Allocation in UAV-Assisted Wireless Networks","authors":"Qichao Xu;Zhou Su;Haixia Peng;Yuan Wu;Ruidong Li","doi":"10.1109/TMC.2025.3579505","DOIUrl":"https://doi.org/10.1109/TMC.2025.3579505","url":null,"abstract":"Recently, the promising unmanned aerial vehicle (UAV)-assisted wireless networks (UAWNs) have emerged by advocating the UAVs to provide wireless transmission services. However, owing to the ever-growing volume of data traffic and the untrusted network operation environment, efficiently and securely assigning limited bandwidth for high-quality wireless communication between UAVs and mobile users poses a significant challenge. To address this challenge, we propose a novel secure UAV-bandwidth allocation scheme to provision reliable wireless transmission services for mobile users in UAWNs. Specifically, we first introduce a novel blockchain-empowered framework for secure bandwidth allocation, designed to automate payment processes and deter malicious activities through the immutable logging of transactional and behavioral data. Wherein, a smart contract is designed to regulate the honest behaviors of both mobile users and UAVs during bandwidth allocation with a distributed manner. Besides, a delegated proof-of-stake (DPoS) with reputation consensus protocol is presented to ensure the authenticity and efficiency of the decision-making process. Further, we apply the Stackelberg game theory to model the dynamic of the bandwidth allocation between mobile users and UAVs. In this game, the UAVs act as game leaders to determine the bandwidth price, while each mobile user acts as a game follower, making decision on the bandwidth request. We utilize the backward induction method to derive the optimal strategies of both parties, culminating in the identification of the Stackelberg equilibrium of the formulated game. Finally, extensive simulations are carried out to show the superiority of the proposed scheme over conventional schemes in terms of security, efficiency, and fairness in bandwidth allocation.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"11209-11223"},"PeriodicalIF":9.2,"publicationDate":"2025-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021406","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}
Cloud-assisted edge computing scenarios can intelligently cache and update the content periodically, thereby enhancing users’ overall perception of service, which is called quality of experience (QoE). To maximize QoE in cloud-to-edge continuum, we formulate a multi-objective optimization problem, which optimizes the cache hit ratio while simultaneously minimizing traffic load and time latency. Particularly, we present an innovative algorithm named Hyperdimensional Transformer with Priority Experience Playback-based Agent Deep network (HT-PAD), which provides a complete solution for prediction and decision-making for proactive caching. First, to improve the prediction accuracy of cached content, we use the encoding layer in hyperdimensional (HD) computing to extract the information features. Second, HD-Transformer, as the prediction part of HT-PAD, is proposed to make predictions based on user preferences, historical information, and popular information. HD-Transformer uses deep neural networks to predict user preferences and process time series data by combining hyperdimensional computation with the Transformer. Third, to avoid errors in the prediction content, we employ PER-MADDPG as the decision-making part of HT-PAD, which consists of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Prioritized Experience Replay (PER). We use MADDPG to improve content decision-making and utilize PER to select appropriate training samples for PER-MADDPG. Finally, our experiments show that our proposed approach achieves strong performance in terms of edge hit ratio, latency, and traffic load, thus improving QoE.
云辅助边缘计算场景可以智能缓存和定期更新内容，从而增强用户对服务的整体感知，即体验质量（quality of experience, QoE）。为了在云到边缘连续体中最大化QoE，我们制定了一个多目标优化问题，在优化缓存命中率的同时最小化流量负载和时间延迟。特别地，我们提出了一种基于优先体验播放的Agent Deep network （HT-PAD）算法，它为主动缓存的预测和决策提供了一个完整的解决方案。首先，为了提高缓存内容的预测精度，我们使用了高维计算中的编码层来提取信息特征。其次，提出HD-Transformer作为HT-PAD的预测部分，基于用户偏好、历史信息和流行信息进行预测。HD-Transformer使用深度神经网络来预测用户偏好，并通过将超维计算与Transformer相结合来处理时间序列数据。第三，为了避免预测内容的误差，我们采用PER-MADDPG作为HT-PAD的决策部分，该部分由多智能体深度确定性策略梯度（madpg）和优先体验重放（PER）组成。我们使用MADDPG来改进内容决策，并利用PER来选择合适的PER-MADDPG训练样本。最后，我们的实验表明，我们提出的方法在边缘命中率、延迟和流量负载方面取得了较好的性能，从而提高了QoE。
{"title":"QoE-Driven Proactive Caching With DRL in Sustainable Cloud-to-Edge Continuum","authors":"Xiaoming He;Yunzhe Jiang;Huajun Cui;Yinqiu Liu;Mingkai Chen;Maher Guizani;Shahid Mumtaz","doi":"10.1109/TMC.2025.3577197","DOIUrl":"https://doi.org/10.1109/TMC.2025.3577197","url":null,"abstract":"Cloud-assisted edge computing scenarios can intelligently cache and update the content periodically, thereby enhancing users’ overall perception of service, which is called quality of experience (QoE). To maximize QoE in cloud-to-edge continuum, we formulate a multi-objective optimization problem, which optimizes the cache hit ratio while simultaneously minimizing traffic load and time latency. Particularly, we present an innovative algorithm named <underline>H</u>yperdimensional <underline>T</u>ransformer with <underline>P</u>riority Experience Playback-based <underline>A</u>gent <underline>D</u>eep network (HT-PAD), which provides a complete solution for prediction and decision-making for proactive caching. First, to improve the prediction accuracy of cached content, we use the encoding layer in hyperdimensional (HD) computing to extract the information features. Second, HD-Transformer, as the prediction part of HT-PAD, is proposed to make predictions based on user preferences, historical information, and popular information. HD-Transformer uses deep neural networks to predict user preferences and process time series data by combining hyperdimensional computation with the Transformer. Third, to avoid errors in the prediction content, we employ PER-MADDPG as the decision-making part of HT-PAD, which consists of Multi-Agent Deep Deterministic Policy Gradient (MADDPG) and Prioritized Experience Replay (PER). We use MADDPG to improve content decision-making and utilize PER to select appropriate training samples for PER-MADDPG. Finally, our experiments show that our proposed approach achieves strong performance in terms of edge hit ratio, latency, and traffic load, thus improving QoE.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"10992-11004"},"PeriodicalIF":9.2,"publicationDate":"2025-06-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021160","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}
In the above article [1], on page 13900, right column, there is an empty reference citation “[?]” in the sentence “By applying Model-Agnostic Meta-Learning (MAML) to fingerprint localization, MetaLoc [?] enables the model to quickly adapt to new environments based on the obtained meta-parameters, thus reducing human labor costs.” The missing reference is listed below as [2].
{"title":"Corrections to “Learning Domain-Invariant Model for WiFi-Based Indoor Localization”","authors":"Guanzhong Wang;Dongheng Zhang;Tianyu Zhang;Shuai Yang;Qibin Sun;Yan Chen","doi":"10.1109/TMC.2025.3539443","DOIUrl":"https://doi.org/10.1109/TMC.2025.3539443","url":null,"abstract":"In the above article [1], on page 13900, right column, there is an empty reference citation “[?]” in the sentence “By applying Model-Agnostic Meta-Learning (MAML) to fingerprint localization, MetaLoc [?] enables the model to quickly adapt to new environments based on the obtained meta-parameters, thus reducing human labor costs.” The missing reference is listed below as [2].","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6718-6718"},"PeriodicalIF":7.7,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11026061","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219697","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}
Pub Date : 2025-06-05DOI: 10.1109/TMC.2025.3565860
Jakub Žádník;Michel Kieffer;Anthony Trioux;Markku Mäkitalo;Pekka Jääskeläinen
In the above article [1], on page 1151, eq. (6), there is an error in the equation. The correct equation is: begin{equation*} min.,,D,,,text{s.t.} sumlimits_{k = 1}^K {{{lambda }_k}beta _k^2 leqslant P.} tag{6} end{equation*} min.D,s.t.∑k=1Kλkβk2⩽P.(6)
{"title":"Correction to “CV-Cast: Computer Vision–Oriented Linear Coding and Transmission”","authors":"Jakub Žádník;Michel Kieffer;Anthony Trioux;Markku Mäkitalo;Pekka Jääskeläinen","doi":"10.1109/TMC.2025.3565860","DOIUrl":"https://doi.org/10.1109/TMC.2025.3565860","url":null,"abstract":"In the above article [1], on page 1151, eq. (6), there is an error in the equation. The correct equation is: begin{equation*} min.,,D,,,text{s.t.} sumlimits_{k = 1}^K {{{lambda }_k}beta _k^2 leqslant P.} tag{6} end{equation*} min.D,s.t.∑k=1Kλkβk2⩽P.(6)","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 7","pages":"6719-6719"},"PeriodicalIF":7.7,"publicationDate":"2025-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11026062","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144219610","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}
Digital twins (DT) offer a low-overhead evaluation platform and the ability to generate rich datasets for training machine learning (ML) models before actual deployment. Specifically, for the scenario of ML-aided millimeter wave (mmWave) links between moving vehicles to roadside units, we show how DT can create an accurate replica of the real world for model training and testing. The contributions of this paper are twofold: First, we propose a framework to create a multimodal Digital Twin (DT), where synthetic images and LiDAR data for the deployment location are generated along with RF propagation measurements obtained via ray-tracing. Second, to ensure effective domain adaptation, we leverage meta-learning, specifically Model-Agnostic Meta-Learning (MAML), with transfer learning (TL) serving as a baseline validation approach. The proposed framework is validated using a comprehensive dataset containing both real and synthetic LiDAR and image data for mmWave V2X beam selection. It also enables the investigation of how each sensor modality impacts domain adaptation, taking into account the unique requirements of mmWave beam selection. Experimental results show that models trained on synthetic data using transfer learning and meta-learning, followed by minimal fine-tuning with real-world data, achieve up to 4.09× and 14.04× improvements in accuracy, respectively. These findings highlight the potential of synthetic data and meta-learning to bridge the domain gap and adapt rapidly to real-world beamforming challenges.
{"title":"SMART: Sim2Real Meta-Learning-Based Training for mmWave Beam Selection in V2X Networks","authors":"Divyadharshini Muruganandham;Suyash Pradhan;Jerry Gu;Torsten Braun;Debashri Roy;Kaushik Chowdhury","doi":"10.1109/TMC.2025.3576203","DOIUrl":"https://doi.org/10.1109/TMC.2025.3576203","url":null,"abstract":"Digital twins (DT) offer a low-overhead evaluation platform and the ability to generate rich datasets for training machine learning (ML) models before actual deployment. Specifically, for the scenario of ML-aided millimeter wave (mmWave) links between moving vehicles to roadside units, we show how DT can create an accurate replica of the real world for model training and testing. The contributions of this paper are twofold: First, we propose a framework to create a multimodal Digital Twin (DT), where synthetic images and LiDAR data for the deployment location are generated along with RF propagation measurements obtained via ray-tracing. Second, to ensure effective domain adaptation, we leverage <italic>meta-learning</i>, specifically <italic>Model-Agnostic Meta-Learning</i> (MAML), with <italic>transfer learning</i> (TL) serving as a baseline validation approach. The proposed framework is validated using a comprehensive dataset containing both real and synthetic LiDAR and image data for mmWave V2X beam selection. It also enables the investigation of how each sensor modality impacts domain adaptation, taking into account the unique requirements of mmWave beam selection. Experimental results show that models trained on synthetic data using transfer learning and meta-learning, followed by minimal fine-tuning with real-world data, achieve up to 4.09× and 14.04× improvements in accuracy, respectively. These findings highlight the potential of synthetic data and meta-learning to bridge the domain gap and adapt rapidly to real-world beamforming challenges.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"11076-11091"},"PeriodicalIF":9.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021370","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}
Mobile Edge Computing (MEC)-enabled vehicular networks have emerged as a promising approach to enhancing the performance and efficiency of the Internet-of-Vehicles (IoV) applications. By leveraging some vehicles to act as transmission relays, multi-hop task offloading addresses the problem of intermittent connectivity between vehicles and edge servers to cope with the issues of network congestion or obstacles. However, two critical issues, i.e., uncooperative behaviors of selfish vehicles and network resource dynamics, resulting from multi-vehicle concurrent offloading are not fully considered in the existing work. To fill this gap, this paper proposes a novel and efficient task offloading scheme, namely MMTO, that exploits the multi-hop computational resources to maximize the system-wide profit, and supports incentive compatibility of vehicular users and concurrent offloading. Specifically, an iterative hierarchical estimation algorithm is designed to estimate the offloading delay and energy cost in order to iteratively optimize the offloading decisions. An energy-efficient routing approach is then proposed to schedule the transmission paths for the offloading vehicles. Furthermore, an effective reward-driven auction-based incentive mechanism is designed for incentivizing relayers and calculators to engage in collaboration. Both simulation and field experiments are conducted; extensive results demonstrate that MMTO outperforms the state-of-the-art approaches in terms of the system-wide profit improvement and overall task delay reduction.
{"title":"MMTO: Multi-Vehicle Multi-Hop Task Offloading in MEC-Enabled Vehicular Networks","authors":"Wenjie Huang;Zhiwei Zhao;Geyong Min;Yang Wang;Zheng Chang","doi":"10.1109/TMC.2025.3576154","DOIUrl":"https://doi.org/10.1109/TMC.2025.3576154","url":null,"abstract":"Mobile Edge Computing (MEC)-enabled vehicular networks have emerged as a promising approach to enhancing the performance and efficiency of the Internet-of-Vehicles (IoV) applications. By leveraging some vehicles to act as transmission relays, multi-hop task offloading addresses the problem of intermittent connectivity between vehicles and edge servers to cope with the issues of network congestion or obstacles. However, two critical issues, i.e., uncooperative behaviors of selfish vehicles and network resource dynamics, resulting from multi-vehicle concurrent offloading are not fully considered in the existing work. To fill this gap, this paper proposes a novel and efficient task offloading scheme, namely MMTO, that exploits the multi-hop computational resources to maximize the system-wide profit, and supports incentive compatibility of vehicular users and concurrent offloading. Specifically, an iterative hierarchical estimation algorithm is designed to estimate the offloading delay and energy cost in order to iteratively optimize the offloading decisions. An energy-efficient routing approach is then proposed to schedule the transmission paths for the offloading vehicles. Furthermore, an effective reward-driven auction-based incentive mechanism is designed for incentivizing relayers and calculators to engage in collaboration. Both simulation and field experiments are conducted; extensive results demonstrate that MMTO outperforms the state-of-the-art approaches in terms of the system-wide profit improvement and overall task delay reduction.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 10","pages":"11125-11136"},"PeriodicalIF":9.2,"publicationDate":"2025-06-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145021225","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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