Pub Date : 2024-11-19DOI: 10.1109/TMC.2024.3502235
Xiaobo Zhou;Shuxin Ge;Tie Qiu;Xingwei Wang
Vehicle repositioning is widely used in Mobility on-Demand (MoD) systems to address supply-demand imbalances and improve order completion rates. Existing methods typically offer repositioning recommendations focused on enhancing vehicle coordination toward supply-demand re-balance. However, these methods often overlook the possibility that drivers may not follow these recommendations due to their personal preferences, leading to recommendation-decision inconsistency and further disrupting the supply-demand balance. To address this issue, we propose a preference-aware vehicle repositioning recommendation strategy for MoD systems, named FREE, which is based on a Coulomb Force directed approach. The core idea is to strike a balance between vehicle coordination and consistency between recommendations and driver decisions. First, we introduce a Coulomb force-based representation (CFR) to model coordination among vehicles. In this model, the interactions between vehicles and orders are represented as forces that drive the repositioning of vehicles. Next, we develop a driver preference learning model that accurately captures drivers’ preferences using triplet and consistency loss. We then integrate these preferences with the CFR into a multi-agent deep reinforcement learning (MADRL) based repositioning algorithm to generate optimal recommendations. Finally, we validate the effectiveness of FREE through simulations using real-world data, demonstrating its superiority over existing benchmarks.
{"title":"Preference-Aware Vehicle Repositioning Recommendation for MoD Systems: A Coulomb Force Directed Perspective","authors":"Xiaobo Zhou;Shuxin Ge;Tie Qiu;Xingwei Wang","doi":"10.1109/TMC.2024.3502235","DOIUrl":"https://doi.org/10.1109/TMC.2024.3502235","url":null,"abstract":"Vehicle repositioning is widely used in Mobility on-Demand (MoD) systems to address supply-demand imbalances and improve order completion rates. Existing methods typically offer repositioning recommendations focused on enhancing vehicle coordination toward supply-demand re-balance. However, these methods often overlook the possibility that drivers may not follow these recommendations due to their personal preferences, leading to recommendation-decision inconsistency and further disrupting the supply-demand balance. To address this issue, we propose a preference-aware vehicle repositioning recommendation strategy for MoD systems, named FREE, which is based on a Coulomb Force directed approach. The core idea is to strike a balance between vehicle coordination and consistency between recommendations and driver decisions. First, we introduce a Coulomb force-based representation (CFR) to model coordination among vehicles. In this model, the interactions between vehicles and orders are represented as forces that drive the repositioning of vehicles. Next, we develop a driver preference learning model that accurately captures drivers’ preferences using triplet and consistency loss. We then integrate these preferences with the CFR into a multi-agent deep reinforcement learning (MADRL) based repositioning algorithm to generate optimal recommendations. Finally, we validate the effectiveness of FREE through simulations using real-world data, demonstrating its superiority over existing benchmarks.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2847-2860"},"PeriodicalIF":7.7,"publicationDate":"2024-11-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564178","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}
This paper investigates a wireless powered mobile edge computing (WP-MEC) network with multiple hybrid access points (HAPs) in a dynamic environment, where wireless devices (WDs) harvest energy from radio frequency (RF) signals of HAPs, and then compute their computation data locally (i.e., local computing mode) or offload it to the chosen HAPs (i.e., edge computing mode). In order to pursue a green computing design, we formulate an optimization problem that minimizes the long-term energy provision of the WP-MEC network subject to the energy, computing delay and computation data demand constraints. The transmit power of HAPs, the duration of the wireless power transfer (WPT) phase, the offloading decisions of WDs, the time allocation for offloading and the CPU frequency for local computing are jointly optimized adapting to the time-varying generated computation data and wireless channels of WDs. To efficiently address the formulated non-convex mixed integer programming (MIP) problem in a distributed manner, we propose a Two-stage Multi-Agent deep reinforcement learning-based Distributed computation Offloading (TMADO) framework, which consists of a high-level agent and multiple low-level agents. The high-level agent residing in all HAPs optimizes the transmit power of HAPs and the duration of the WPT phase, while each low-level agent residing in each WD optimizes its offloading decision, time allocation for offloading and CPU frequency for local computing. Simulation results show the superiority of the proposed TMADO framework in terms of the energy provision minimization.
{"title":"Distributed Computation Offloading for Energy Provision Minimization in WP-MEC Networks With Multiple HAPs","authors":"Xiaoying Liu;Anping Chen;Kechen Zheng;Kaikai Chi;Bin Yang;Tarik Taleb","doi":"10.1109/TMC.2024.3502004","DOIUrl":"https://doi.org/10.1109/TMC.2024.3502004","url":null,"abstract":"This paper investigates a wireless powered mobile edge computing (WP-MEC) network with multiple hybrid access points (HAPs) in a dynamic environment, where wireless devices (WDs) harvest energy from radio frequency (RF) signals of HAPs, and then compute their computation data locally (i.e., local computing mode) or offload it to the chosen HAPs (i.e., edge computing mode). In order to pursue a green computing design, we formulate an optimization problem that minimizes the long-term energy provision of the WP-MEC network subject to the energy, computing delay and computation data demand constraints. The transmit power of HAPs, the duration of the wireless power transfer (WPT) phase, the offloading decisions of WDs, the time allocation for offloading and the CPU frequency for local computing are jointly optimized adapting to the time-varying generated computation data and wireless channels of WDs. To efficiently address the formulated non-convex mixed integer programming (MIP) problem in a distributed manner, we propose a <underline>T</u>wo-stage <underline>M</u>ulti-<underline>A</u>gent deep reinforcement learning-based <underline>D</u>istributed computation <underline>O</u>ffloading (TMADO) framework, which consists of a high-level agent and multiple low-level agents. The high-level agent residing in all HAPs optimizes the transmit power of HAPs and the duration of the WPT phase, while each low-level agent residing in each WD optimizes its offloading decision, time allocation for offloading and CPU frequency for local computing. Simulation results show the superiority of the proposed TMADO framework in terms of the energy provision minimization.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2673-2689"},"PeriodicalIF":7.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563963","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}
We focus on the transportation-aware trajectory recovery problem, which is distinct from the conventional vehicle-based trajectory recovery, facing three major challenges: heterogeneity, personalization and efficiency. For the heterogeneity, the velocity of the mobile object is intrinsically correlated with the specific transportation mode, containing inherent heterogeneity. For the personalization, the trajectory data is complicated by substantial variations in users, which are different in personalized behaviors. For the efficiency, previous works mostly employ sequence-to-sequence framework which limits their efficiency due to the auto-regressive inference pattern. To address these challenges, we design a novel efficient and effective multi-modal deep model, coined as PTrajRec, for transportation-aware trajectory recovery. Specifically, we initially embed location, behavior, and transportation mode modalities in distinct channels, which not only reflect spatio-temporal information encapsulated in location sequences but also introduce the heterogeneity and personalization characteristics associated with mode and behavior sequences. For further modeling these modalities, we employ the auto-correlation mechanism to learn periodic dependencies on the temporal dimension and the graph attention mechanism to learn road network dependencies on the spatial dimension. At last, we propose a dual-view constraint mechanism to assist the efficient trajectory recovery framework and design three auxiliary tasks to address the inherent heterogeneity and efficiency design. Extensive experimental results on two real-world datasets demonstrate the superiority of our proposed method compared to state-of-the-art baselines with reduced computation cost and excellent performance.
{"title":"Towards Effective Transportation Mode-Aware Trajectory Recovery: Heterogeneity, Personalization and Efficiency","authors":"Chenxing Wang;Fang Zhao;Haiyong Luo;Yuchen Fang;Haichao Zhang;Haoyu Xiong","doi":"10.1109/TMC.2024.3501280","DOIUrl":"https://doi.org/10.1109/TMC.2024.3501280","url":null,"abstract":"We focus on the transportation-aware trajectory recovery problem, which is distinct from the conventional vehicle-based trajectory recovery, facing three major challenges: heterogeneity, personalization and efficiency. For the heterogeneity, the velocity of the mobile object is intrinsically correlated with the specific transportation mode, containing inherent heterogeneity. For the personalization, the trajectory data is complicated by substantial variations in users, which are different in personalized behaviors. For the efficiency, previous works mostly employ sequence-to-sequence framework which limits their efficiency due to the auto-regressive inference pattern. To address these challenges, we design a novel efficient and effective multi-modal deep model, coined as PTrajRec, for transportation-aware trajectory recovery. Specifically, we initially embed location, behavior, and transportation mode modalities in distinct channels, which not only reflect spatio-temporal information encapsulated in location sequences but also introduce the heterogeneity and personalization characteristics associated with mode and behavior sequences. For further modeling these modalities, we employ the auto-correlation mechanism to learn periodic dependencies on the temporal dimension and the graph attention mechanism to learn road network dependencies on the spatial dimension. At last, we propose a dual-view constraint mechanism to assist the efficient trajectory recovery framework and design three auxiliary tasks to address the inherent heterogeneity and efficiency design. Extensive experimental results on two real-world datasets demonstrate the superiority of our proposed method compared to state-of-the-art baselines with reduced computation cost and excellent performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2832-2846"},"PeriodicalIF":7.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564175","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 : 2024-11-18DOI: 10.1109/TMC.2024.3501299
Wanguo Jiao;Changsheng Zhang;Wei Du;Shuai Ma
Human activity recognition based on Wi-Fi signals has become one part of integrated sensing and communications, which has promising application prospects. Detecting activities across different domains is an important and challenging problem. To reduce model complexity and improve recognition accuracy, we propose a novel approach to realize activity recognition across domains, named WiSDA. The proposed WiSDA contains two parts: data augmentation and a deep learning model. The recursive plots method is employed as the data augmentation to transform Wi-Fi channel state information into images, which can take advantage of the image recognition ability of the latter deep learning model. The proposed learning model utilizes weighted cosine similarity to align feature distributions among sub-domains activated by a deep network layer across different domains, thereby a domain-independent feature representation is generated. Based on this representation, WiSDA can make the recognition decision independent of domains, then the cross-domain recognition accuracy is increased. The numerical results illustrate that WiSDA achieves higher recognition accuracy and has lower complexity. The cross-domain recognition accuracy ranges from 89% to 93% with offline pre-training. Enhancing the pre-trained WiSDA with limited samples boosts cross-domain recognition accuracy to 97%.
{"title":"WiSDA: Subdomain Adaptation Human Activity Recognition Method Using Wi-Fi Signals","authors":"Wanguo Jiao;Changsheng Zhang;Wei Du;Shuai Ma","doi":"10.1109/TMC.2024.3501299","DOIUrl":"https://doi.org/10.1109/TMC.2024.3501299","url":null,"abstract":"Human activity recognition based on Wi-Fi signals has become one part of integrated sensing and communications, which has promising application prospects. Detecting activities across different domains is an important and challenging problem. To reduce model complexity and improve recognition accuracy, we propose a novel approach to realize activity recognition across domains, named WiSDA. The proposed WiSDA contains two parts: data augmentation and a deep learning model. The recursive plots method is employed as the data augmentation to transform Wi-Fi channel state information into images, which can take advantage of the image recognition ability of the latter deep learning model. The proposed learning model utilizes weighted cosine similarity to align feature distributions among sub-domains activated by a deep network layer across different domains, thereby a domain-independent feature representation is generated. Based on this representation, WiSDA can make the recognition decision independent of domains, then the cross-domain recognition accuracy is increased. The numerical results illustrate that WiSDA achieves higher recognition accuracy and has lower complexity. The cross-domain recognition accuracy ranges from 89% to 93% with offline pre-training. Enhancing the pre-trained WiSDA with limited samples boosts cross-domain recognition accuracy to 97%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2876-2888"},"PeriodicalIF":7.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563918","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}
Unlike traditional edge caching paradigms, similarity edge caching enables the retrieval of similar content from local caches to fulfill user requests, reducing reliance on remote data centers and improving system performance. Although several pioneering works have contributed to similarity edge caching, most focus on single-edge nodes and/or static environment settings, which are impractical for real-world applications. To address this gap, we investigate the similarity caching problem in dynamic cooperative edge networks, where a set of edge nodes cooperatively serve requests generated from arbitrary distributions with similar content over fluctuating transmission links. This presents a significant challenge, as it requires balancing content similarity with delivery latency over the transmission network and learning the environment in real-time to optimize caching policies. We frame this problem within an adversarial Multi-Armed Bandit framework to accommodate the continuously changing operational environment. To solve this, we propose an online learning-based approach named MABSCP, which dynamically updates caching policies based on real-time feedback to minimize the service cost of edge caching networks. To enhance implementation efficiency, we devise both an offline compact strategy construction method and an online Gibbs sampling method. Finally, trace-driven simulation results demonstrate that our proposed approach outperforms several existing methods in terms of system performance.
{"title":"Similarity Caching in Dynamic Cooperative Edge Networks: An Adversarial Bandit Approach","authors":"Liang Wang;Yaru Wang;Zhiwen Yu;Fei Xiong;Lianbo Ma;Huan Zhou;Bin Guo","doi":"10.1109/TMC.2024.3500132","DOIUrl":"https://doi.org/10.1109/TMC.2024.3500132","url":null,"abstract":"Unlike traditional edge caching paradigms, similarity edge caching enables the retrieval of similar content from local caches to fulfill user requests, reducing reliance on remote data centers and improving system performance. Although several pioneering works have contributed to similarity edge caching, most focus on single-edge nodes and/or static environment settings, which are impractical for real-world applications. To address this gap, we investigate the similarity caching problem in dynamic cooperative edge networks, where a set of edge nodes cooperatively serve requests generated from arbitrary distributions with similar content over fluctuating transmission links. This presents a significant challenge, as it requires balancing content similarity with delivery latency over the transmission network and learning the environment in real-time to optimize caching policies. We frame this problem within an adversarial Multi-Armed Bandit framework to accommodate the continuously changing operational environment. To solve this, we propose an online learning-based approach named MABSCP, which dynamically updates caching policies based on real-time feedback to minimize the service cost of edge caching networks. To enhance implementation efficiency, we devise both an offline compact strategy construction method and an online Gibbs sampling method. Finally, trace-driven simulation results demonstrate that our proposed approach outperforms several existing methods in terms of system performance.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2769-2782"},"PeriodicalIF":7.7,"publicationDate":"2024-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563925","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 : 2024-11-15DOI: 10.1109/TMC.2024.3499371
Kai Li;Jingjing Zheng;Wei Ni;Hailong Huang;Pietro Liò;Falko Dressler;Ozgur B. Akan
Fairness in Federated Learning (FL) is imperative not only for the ethical utilization of technology but also for ensuring that models provide accurate, equitable, and beneficial outcomes across varied user demographics and equipment. This paper proposes a new adversarial architecture, referred to as Adversarial Graph Attention Network (AGAT), which deliberately instigates fairness attacks with an aim to bias the learning process across the FL. The proposed AGAT is developed to synthesize malicious, biasing model updates, where the minimum of Kullback-Leibler (KL) divergence between the user's model update and the global model is maximized. Due to a limited set of labeled input-output biasing data samples, a surrogate model is created, which presents the behavior of a complex malicious model update. Moreover, a graph autoencoder (GAE) is designed within the AGAT architecture, which is trained together with sub-gradient descent to reconstruct manipulatively the correlations of the model updates, and maximize the reconstruction loss while keeping the malicious, biasing model updates undetectable. The proposed AGAT attack is implemented in PyTorch, showing experimentally that AGAT successfully increases the minimum value of KL divergence of benign model updates by 60.9% and bypasses the detection of existing defense models. The source code of the AGAT attack is released on GitHub.
{"title":"Biasing Federated Learning With a New Adversarial Graph Attention Network","authors":"Kai Li;Jingjing Zheng;Wei Ni;Hailong Huang;Pietro Liò;Falko Dressler;Ozgur B. Akan","doi":"10.1109/TMC.2024.3499371","DOIUrl":"https://doi.org/10.1109/TMC.2024.3499371","url":null,"abstract":"Fairness in Federated Learning (FL) is imperative not only for the ethical utilization of technology but also for ensuring that models provide accurate, equitable, and beneficial outcomes across varied user demographics and equipment. This paper proposes a new adversarial architecture, referred to as Adversarial Graph Attention Network (AGAT), which deliberately instigates fairness attacks with an aim to bias the learning process across the FL. The proposed AGAT is developed to synthesize malicious, biasing model updates, where the minimum of Kullback-Leibler (KL) divergence between the user's model update and the global model is maximized. Due to a limited set of labeled input-output biasing data samples, a surrogate model is created, which presents the behavior of a complex malicious model update. Moreover, a graph autoencoder (GAE) is designed within the AGAT architecture, which is trained together with sub-gradient descent to reconstruct manipulatively the correlations of the model updates, and maximize the reconstruction loss while keeping the malicious, biasing model updates undetectable. The proposed AGAT attack is implemented in PyTorch, showing experimentally that AGAT successfully increases the minimum value of KL divergence of benign model updates by 60.9% and bypasses the detection of existing defense models. The source code of the AGAT attack is released on GitHub.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2407-2421"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361324","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}
The unpredictability of the wireless channel has been used as a natural source of randomness to build physical-layer security primitives for shared key generation, authentication, access control, proximity verification, and other security properties. Compared to pseudo-random generators, it has the potential to achieve information-theoretic security. In sub-6 GHz frequencies, the randomness is harvested from the small-scale fading effects of RF signal propagation in rich scattering environments. However, the RF propagation characteristics follow sparse models with clustered paths when devices operate in millimeter-wave (mmWave) bands (5G and Next-Generation networks, Wi-Fi in 60GHz). Millimeter-wave transmissions are typically directional to increase the gain and combat high signal attenuation, leading to stable and more predictable channels. In this paper, we first demonstrate that state-of-the-art methods relying on channel state information or received signal strength measurements fail to produce high randomness. Accounting for the unique features of mmWave propagation, we propose a novel randomness extraction mechanism that exploits the random timing of channel blockage to harvest random bits. Compared with the prior art in CSI-based and context-based randomness extraction, our protocol remains secure against passive and active Man-in-the-Middle adversaries co-located with the legitimate devices. We demonstrate the security properties of our method in a 28 GHz mmWave testbed in an indoor setting.
{"title":"Harvesting Physical-Layer Randomness in Millimeter Wave Bands","authors":"Ziqi Xu;Jingcheng Li;Yanjun Pan;Ming Li;Loukas Lazos","doi":"10.1109/TMC.2024.3499876","DOIUrl":"https://doi.org/10.1109/TMC.2024.3499876","url":null,"abstract":"The unpredictability of the wireless channel has been used as a natural source of randomness to build physical-layer security primitives for shared key generation, authentication, access control, proximity verification, and other security properties. Compared to pseudo-random generators, it has the potential to achieve information-theoretic security. In sub-6 GHz frequencies, the randomness is harvested from the small-scale fading effects of RF signal propagation in rich scattering environments. However, the RF propagation characteristics follow sparse models with clustered paths when devices operate in millimeter-wave (mmWave) bands (5G and Next-Generation networks, Wi-Fi in 60GHz). Millimeter-wave transmissions are typically directional to increase the gain and combat high signal attenuation, leading to stable and more predictable channels. In this paper, we first demonstrate that state-of-the-art methods relying on channel state information or received signal strength measurements fail to produce high randomness. Accounting for the unique features of mmWave propagation, we propose a novel randomness extraction mechanism that exploits the random timing of channel blockage to harvest random bits. Compared with the prior art in CSI-based and context-based randomness extraction, our protocol remains secure against <italic>passive and active Man-in-the-Middle adversaries co-located with the legitimate devices</i>. We demonstrate the security properties of our method in a 28 GHz mmWave testbed in an indoor setting.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2285-2300"},"PeriodicalIF":7.7,"publicationDate":"2024-11-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360989","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 : 2024-11-14DOI: 10.1109/TMC.2024.3497934
Weilin Chen;Wei Yang;Mingjun Xiao;Lide Xue;Shaowei Wang
The exponential growth of data in the Internet of Vehicles (IoV) has created opportunities to improve traffic safety and efficiency through data trading. However, establishing trust among highly mobile and resource-constrained vehicles poses significant challenges for effective data trading in IoV. To address this issue, we propose a lightweight blockchain-based data trading scheme (LBDT), which ensures secure and efficient data trading in IoV. We introduce a proof-of-reputation (PoR) consensus mechanism to establish trustworthiness for data trading. Specifically, we use a progressive reputation mechainism to support the PoR consensus. LBDT utilizes a parallel-chain structure for the PoR consensus to minimize communication and storage costs while reducing transaction confirmation latency. Additionally, we adopt a double auction mechanism as an incentivizing strategy to encourage vehicle participation in data trading. We evaluate the performance of LBDT through extensive experiments. The experimental results demonstrate that LBDT is highly effective and secure, achieving a transaction latency of approximately 4 seconds. Moreover, LBDT successfully mitigates communication and storage overheads by over 90%, thus establishing its superiority over state-of-the-art solutions under comparable conditions.
{"title":"LBDT: A Lightweight Blockchain-Based Data Trading Scheme in Internet of Vehicles Using Proof-of-Reputation","authors":"Weilin Chen;Wei Yang;Mingjun Xiao;Lide Xue;Shaowei Wang","doi":"10.1109/TMC.2024.3497934","DOIUrl":"https://doi.org/10.1109/TMC.2024.3497934","url":null,"abstract":"The exponential growth of data in the Internet of Vehicles (IoV) has created opportunities to improve traffic safety and efficiency through data trading. However, establishing trust among highly mobile and resource-constrained vehicles poses significant challenges for effective data trading in IoV. To address this issue, we propose a lightweight blockchain-based data trading scheme (LBDT), which ensures secure and efficient data trading in IoV. We introduce a proof-of-reputation (PoR) consensus mechanism to establish trustworthiness for data trading. Specifically, we use a progressive reputation mechainism to support the PoR consensus. LBDT utilizes a parallel-chain structure for the PoR consensus to minimize communication and storage costs while reducing transaction confirmation latency. Additionally, we adopt a double auction mechanism as an incentivizing strategy to encourage vehicle participation in data trading. We evaluate the performance of LBDT through extensive experiments. The experimental results demonstrate that LBDT is highly effective and secure, achieving a transaction latency of approximately 4 seconds. Moreover, LBDT successfully mitigates communication and storage overheads by over 90%, thus establishing its superiority over state-of-the-art solutions under comparable conditions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2800-2816"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564176","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}
Radio sensing has emerged as a promising solution for monitoring vital signs in a contactless manner. However, most of the existing designs focus on stationary target and struggle with body motion interference. While some efforts have been made to address this issue, the lack of a physical explanation for the motion elimination principle makes them work as a blind signal separation way and thus leaves the body motion elimination problem still as an open challenge. In this paper, we reveal for the first time the existence of “dark pixels”–specific points on the same rigid body parts that share the same body movement but exhibit varying physiological motions, with these variations still preserving the physiological rhythm. By exploiting the inherent relationship between the dark pixels, we propose a cooperative sensing framework, Co-Sense, that can achieve robust radio sensing for non-stationary targets in an explainable way. Through extensive experiments, Co-Sense demonstrates its superiority over existing methods, achieving effective motion cancellation and breath sensing with a median absolute respiratory rate (RR) error of 0.36 respiration per minute (RPM) and breath wave correlation of 0.61 under non-stationary scenarios. The results indicate the great potential of Co-Sense in enhancing the accuracy of vital sign sensing with radio signals, especially in real-world environments where targets are rarely stationary.
{"title":"Co-Sense: Exploiting Cooperative Dark Pixels in Radio Sensing for Non-Stationary Target","authors":"Jinbo Chen;Dongheng Zhang;Ganlin Zhang;Haoyu Wang;Qibin Sun;Yan Chen","doi":"10.1109/TMC.2024.3498048","DOIUrl":"https://doi.org/10.1109/TMC.2024.3498048","url":null,"abstract":"Radio sensing has emerged as a promising solution for monitoring vital signs in a contactless manner. However, most of the existing designs focus on stationary target and struggle with body motion interference. While some efforts have been made to address this issue, the lack of a physical explanation for the motion elimination principle makes them work as a blind signal separation way and thus leaves the body motion elimination problem still as an open challenge. In this paper, we reveal for the first time the existence of “dark pixels”–specific points on the same rigid body parts that share the same body movement but exhibit varying physiological motions, with these variations still preserving the physiological rhythm. By exploiting the inherent relationship between the dark pixels, we propose a cooperative sensing framework, Co-Sense, that can achieve robust radio sensing for non-stationary targets in an explainable way. Through extensive experiments, Co-Sense demonstrates its superiority over existing methods, achieving effective motion cancellation and breath sensing with a median absolute respiratory rate (RR) error of 0.36 respiration per minute (RPM) and breath wave correlation of 0.61 under non-stationary scenarios. The results indicate the great potential of Co-Sense in enhancing the accuracy of vital sign sensing with radio signals, especially in real-world environments where targets are rarely stationary.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2783-2799"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563901","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}
Self-supervised learning (SSL) is a powerful approach that learns general semantic representations from large-scale unlabeled data to make downstream tasks solve easier, offering significant potential in enhancing downstream performance and alleviating the appetite for large-scale annotated data. However, existing SSL techniques, predominantly designed for natural images, may be prone to shortcuts when applied to RF signals. This study presents surprising empirical findings showing that SSL can indeed learn meaningful RF representations by employing simple group shuffle (GS) and asymmetry augmentation techniques. The GS augmentation is inspired by blind calibration tasks in Time-Interleaved Analog-to-Digital Converters (TIADC). By treating the original RF signal as a composite output from sub-ADCs, GS augmentation enriches RF signals while preserving their global semantics. We also provide a theoretical validation of the GS augmentation’s singular value consistency. Notably, we observe that the shortcut is essentially a domain gap between the pre-trained and the downstream task models. This issue can be mitigated by an asymmetry augmentation technique, which maximizes the similarity between an original RF signal and its augmented version, rather than between two augmentations of the same RF signal. By integrating group shuffle and asymmetry augmentation (GSAA) into an existing contrastive learning framework, we develop an effective contrastive learning approach for RF signals. Our evaluations, spanning seven downstream RF sensing tasks across two general RF devices (WiFi and radar), strongly demonstrate that GSAA plays a significant role in advancing SSL-based solutions in RF sensing.
{"title":"Unleashing the Potential of Self-Supervised RF Learning With Group Shuffle","authors":"Ruiyuan Song;Zhi Lu;Dongheng Zhang;Liang Fang;Zhi Wu;Yang Hu;Qibin Sun;Yan Chen","doi":"10.1109/TMC.2024.3497972","DOIUrl":"https://doi.org/10.1109/TMC.2024.3497972","url":null,"abstract":"Self-supervised learning (SSL) is a powerful approach that learns general semantic representations from large-scale unlabeled data to make downstream tasks solve easier, offering significant potential in enhancing downstream performance and alleviating the appetite for large-scale annotated data. However, existing SSL techniques, predominantly designed for natural images, may be prone to shortcuts when applied to RF signals. This study presents surprising empirical findings showing that SSL can indeed learn meaningful RF representations by employing simple group shuffle (GS) and asymmetry augmentation techniques. The GS augmentation is inspired by blind calibration tasks in Time-Interleaved Analog-to-Digital Converters (TIADC). By treating the original RF signal as a composite output from sub-ADCs, GS augmentation enriches RF signals while preserving their global semantics. We also provide a theoretical validation of the GS augmentation’s singular value consistency. Notably, we observe that the shortcut is essentially a domain gap between the pre-trained and the downstream task models. This issue can be mitigated by an asymmetry augmentation technique, which maximizes the similarity between an original RF signal and its augmented version, rather than between two augmentations of the same RF signal. By integrating <bold>g</b>roup <bold>s</b>huffle and <bold>a</b>symmetry <bold>a</b>ugmentation (GSAA) into an existing contrastive learning framework, we develop an effective contrastive learning approach for RF signals. Our evaluations, spanning seven downstream RF sensing tasks across two general RF devices (WiFi and radar), strongly demonstrate that GSAA plays a significant role in advancing SSL-based solutions in RF sensing.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 4","pages":"2612-2627"},"PeriodicalIF":7.7,"publicationDate":"2024-11-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143563962","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：