Satellite-terrestrial integrated networks based on edge computing can provide computation offloading service to terminal devices in remote areas. However, it faces various limitations, including satellite energy consumption, computation delay, and environmental dynamics, etc. In this paper, we propose a satellite-terrestrial integrated cloud and edge computing network (STCECN) architecture, including satellite layer, terrestrial layer and cloud center, where computing resources exist in multi-layer heterogeneous edge computing clusters. Optimization of system delay and energy consumption is defined as a mixed-integer programming problem. Moreover, we present a deep reinforcement learning-based computation offloading decision algorithm that can adapt to the dynamics and variability of satellite networks. A dual network computation offloading decision method is proposed for delay and energy consumption based on deep reinforcement learning offloading (DRLO), including deep convolutional network update method, quantization strategy, and bandwidth resource allocation. Meanwhile, the proposed method is based on previous experience and integrates deviation adjustment strategies for decision making to solve the problem of pseudo-patch loss caused by satellite network switching. The simulation results indicate that the proposed method performs almost consistently with traditional heuristic algorithms, with only 20% of the time consumption of the latter, and the number of pseudo packet loss also decreases to the original 10–20%.
{"title":"Dual Network Computation Offloading Based on DRL for Satellite-Terrestrial Integrated Networks","authors":"Dongbo Li;Yuchen Sun;Jielun Peng;Siyao Cheng;Zhisheng Yin;Nan Cheng;Jie Liu;Zhijun Li;Chenren Xu","doi":"10.1109/TMC.2024.3493388","DOIUrl":"https://doi.org/10.1109/TMC.2024.3493388","url":null,"abstract":"Satellite-terrestrial integrated networks based on edge computing can provide computation offloading service to terminal devices in remote areas. However, it faces various limitations, including satellite energy consumption, computation delay, and environmental dynamics, etc. In this paper, we propose a satellite-terrestrial integrated cloud and edge computing network (STCECN) architecture, including satellite layer, terrestrial layer and cloud center, where computing resources exist in multi-layer heterogeneous edge computing clusters. Optimization of system delay and energy consumption is defined as a mixed-integer programming problem. Moreover, we present a deep reinforcement learning-based computation offloading decision algorithm that can adapt to the dynamics and variability of satellite networks. A dual network computation offloading decision method is proposed for delay and energy consumption based on deep reinforcement learning offloading (DRLO), including deep convolutional network update method, quantization strategy, and bandwidth resource allocation. Meanwhile, the proposed method is based on previous experience and integrates deviation adjustment strategies for decision making to solve the problem of pseudo-patch loss caused by satellite network switching. The simulation results indicate that the proposed method performs almost consistently with traditional heuristic algorithms, with only 20% of the time consumption of the latter, and the number of pseudo packet loss also decreases to the original 10–20%.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2270-2284"},"PeriodicalIF":7.7,"publicationDate":"2024-11-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360988","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 : 2024-11-06DOI: 10.1109/TMC.2024.3492148
Liang Zhao;Chaojin Mao;Shaohua Wan;Ammar Hawbani;Ahmed Y. Al-Dubai;Geyong Min;Albert Y. Zomaya
As a promising vehicular communication technology, Cellular Vehicle-to-Everything (C-V2X) is expected to ensure the safety and convenience of Intelligent Transportation Systems (ITS) by providing global road information. However, it is difficult to obtain global road information in practical scenarios since there will still be many vehicles on the road without onboard units (OBUs) in the near future. Specifically, although C-V2X vehicles have sensors that can perceive their surroundings and broadcast their perceived information to the C-V2X system, their line-of-sight (LoS) is limited and obscured by the environment, such as other vehicles and terrain. Besides, vehicles without OBUs cannot share their perceived information. These two problems cause extensive areas with unperceived information in the C-V2X system, and whether vehicles are in these areas is unknown. Thus, extending the perceivable range of the limited scenario for C-V2X applications that require global road information is necessary. To this end, this paper pioneers investigating the scenario inpainting task problem in C-V2X. To solve this challenging problem, we propose an effiCient trAffic Scenario inpainTing (CAST) solution consisting of a generative architecture and knowledge distillation, simultaneously considering the inpainting precision and computation efficiency. Extensive experiments have been conducted to demonstrate the effectiveness of CAST in terms of Precise Inpaint Rate (PIR), Rough Inpaint Rate (RIR), Lane-Level Inpaint Rate (LLIR), and Inpaint Confidence Error (ICE), paving the way for novel solutions for the inpainting problem in more complex road scenarios.
{"title":"CAST: Efficient Traffic Scenario Inpainting in Cellular Vehicle-to-Everything Systems","authors":"Liang Zhao;Chaojin Mao;Shaohua Wan;Ammar Hawbani;Ahmed Y. Al-Dubai;Geyong Min;Albert Y. Zomaya","doi":"10.1109/TMC.2024.3492148","DOIUrl":"https://doi.org/10.1109/TMC.2024.3492148","url":null,"abstract":"As a promising vehicular communication technology, Cellular Vehicle-to-Everything (C-V2X) is expected to ensure the safety and convenience of Intelligent Transportation Systems (ITS) by providing global road information. However, it is difficult to obtain global road information in practical scenarios since there will still be many vehicles on the road without onboard units (OBUs) in the near future. Specifically, although C-V2X vehicles have sensors that can perceive their surroundings and broadcast their perceived information to the C-V2X system, their line-of-sight (LoS) is limited and obscured by the environment, such as other vehicles and terrain. Besides, vehicles without OBUs cannot share their perceived information. These two problems cause extensive areas with unperceived information in the C-V2X system, and whether vehicles are in these areas is unknown. Thus, extending the perceivable range of the limited scenario for C-V2X applications that require global road information is necessary. To this end, this paper pioneers investigating the scenario inpainting task problem in C-V2X. To solve this challenging problem, we propose an effi<underline>C</u>ient tr<underline>A</u>ffic <underline>S</u>cenario inpain<underline>T</u>ing (CAST) solution consisting of a generative architecture and knowledge distillation, simultaneously considering the inpainting precision and computation efficiency. Extensive experiments have been conducted to demonstrate the effectiveness of CAST in terms of Precise Inpaint Rate (PIR), Rough Inpaint Rate (RIR), Lane-Level Inpaint Rate (LLIR), and Inpaint Confidence Error (ICE), paving the way for novel solutions for the inpainting problem in more complex road scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2331-2345"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360909","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}
Federated learning (FL) is an efficient distributed learning method that facilitates collaborative model training among multiple edge devices (or clients). However, current research always assumes that clients have access to ground-truth data for training, which is unrealistic in practice because of a lack of expertise. Semi-supervised federated learning (SSFL) has been proposed in many existing works to address this problem, which always adopts a fixed model architecture for training, bringing two main problems with varying amounts of pseudo-labeled data. First, the shallow model cannot have the capability to fit the increasing pseudo-labeled data, leading to poor training performance. Second, the large model suffers from an overfitting problem when exploiting a few labeled data samples in SSFL, and also requires tremendous resource (e.g., computation and communication) costs. To tackle these problems, we propose a novel framework, called star, which adopts progressive training to enhance model training in SSFL. Specifically, star gradually increases the model depth through adding the sub-module (e.g., one or several layers) from a shallow model, and performs pseudo-labeling for unlabeled data with a specialized confidence threshold simultaneously. Then, we propose an efficient algorithm to determine the appropriate model depth for each client with varied resource budgets and the proper confidence threshold for pseudo-labeling in SSFL. The experimental results demonstrate the high effectiveness of STAR. For instance, star can reduce the bandwidth consumption by about 40%, and achieve an average accuracy improvement of around 9.8% compared with the baselines, on CIFAR10.
{"title":"Enhancing Semi-Supervised Federated Learning With Progressive Training in Heterogeneous Edge Computing","authors":"Jianchun Liu;Jun Liu;Hongli Xu;Yunming Liao;Zhiwei Yao;Min Chen;Chen Qian","doi":"10.1109/TMC.2024.3492140","DOIUrl":"https://doi.org/10.1109/TMC.2024.3492140","url":null,"abstract":"Federated learning (FL) is an efficient distributed learning method that facilitates collaborative model training among multiple edge devices (or clients). However, current research always assumes that clients have access to ground-truth data for training, which is unrealistic in practice because of a lack of expertise. Semi-supervised federated learning (SSFL) has been proposed in many existing works to address this problem, which always adopts a fixed model architecture for training, bringing two main problems with varying amounts of pseudo-labeled data. First, the shallow model cannot have the capability to fit the increasing pseudo-labeled data, leading to poor training performance. Second, the large model suffers from an overfitting problem when exploiting a few labeled data samples in SSFL, and also requires tremendous resource (e.g., computation and communication) costs. To tackle these problems, we propose a novel framework, called <sc>star</small>, which adopts progressive training to enhance model training in SSFL. Specifically, <sc>star</small> gradually increases the model depth through adding the sub-module (e.g., one or several layers) from a shallow model, and performs pseudo-labeling for unlabeled data with a specialized confidence threshold simultaneously. Then, we propose an efficient algorithm to determine the appropriate model depth for each client with varied resource budgets and the proper confidence threshold for pseudo-labeling in SSFL. The experimental results demonstrate the high effectiveness of STAR. For instance, <sc>star</small> can reduce the bandwidth consumption by about 40%, and achieve an average accuracy improvement of around 9.8% compared with the baselines, on CIFAR10.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2315-2330"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360991","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 : 2024-11-06DOI: 10.1109/TMC.2024.3492916
Zhenxiao Zhang;Zhidong Gao;Yuanxiong Guo;Yanmin Gong
Motivated by the drawbacks of cloud-based federated learning (FL), cooperative federated edge learning (CFEL) has been proposed to improve efficiency for FL over mobile edge networks, where multiple edge servers collaboratively coordinate the distributed model training across a large number of edge devices. However, CFEL faces critical challenges arising from dynamic and heterogeneous device properties, which slow down the convergence and increase resource consumption. This paper proposes a heterogeneity-aware CFEL scheme called Heterogeneity-Aware Cooperative Edge-based Federated Averaging (HCEF) that aims to maximize the model accuracy while minimizing the training time and energy consumption via adaptive computation and communication compression in CFEL. By theoretically analyzing how local update frequency and gradient compression affect the convergence error bound in CFEL, we develop an efficient online control algorithm for HCEF to dynamically determine local update frequencies and compression ratios for heterogeneous devices. Experimental results show that compared with prior schemes, the proposed HCEF scheme can maintain higher model accuracy while reducing training latency and improving energy efficiency simultaneously.
{"title":"Heterogeneity-Aware Cooperative Federated Edge Learning With Adaptive Computation and Communication Compression","authors":"Zhenxiao Zhang;Zhidong Gao;Yuanxiong Guo;Yanmin Gong","doi":"10.1109/TMC.2024.3492916","DOIUrl":"https://doi.org/10.1109/TMC.2024.3492916","url":null,"abstract":"Motivated by the drawbacks of cloud-based federated learning (FL), cooperative federated edge learning (CFEL) has been proposed to improve efficiency for FL over mobile edge networks, where multiple edge servers collaboratively coordinate the distributed model training across a large number of edge devices. However, CFEL faces critical challenges arising from dynamic and heterogeneous device properties, which slow down the convergence and increase resource consumption. This paper proposes a heterogeneity-aware CFEL scheme called <italic>Heterogeneity-Aware Cooperative Edge-based Federated Averaging</i> (HCEF) that aims to maximize the model accuracy while minimizing the training time and energy consumption via adaptive computation and communication compression in CFEL. By theoretically analyzing how local update frequency and gradient compression affect the convergence error bound in CFEL, we develop an efficient online control algorithm for HCEF to dynamically determine local update frequencies and compression ratios for heterogeneous devices. Experimental results show that compared with prior schemes, the proposed HCEF scheme can maintain higher model accuracy while reducing training latency and improving energy efficiency simultaneously.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2073-2084"},"PeriodicalIF":7.7,"publicationDate":"2024-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143361003","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 : 2024-11-05DOI: 10.1109/TMC.2024.3492002
Ning Chen;Tie Qiu;Weisheng Si;Dapeng Oliver Wu
Robust Topology is a key prerequisite to providing consistent connectivity for highly dynamic Internet-of-Things (IoT) applications that are suffering node failures. In this paper, we present a two-step approach to organizing the most robust IoT topology. First, we propose a novel robustness metric denoted as $I$, which is based on network motifs and is specifically designed to sensitively analyze the dynamic changes in topology resulting from node failures. Second, we introduce a Distributed duAl-layer collaborative competition optimization strategy based on Motif density (DAiMo). This strategy significantly expands the search space for optimal solutions and facilitates the identification of the optimal IoT topology. We utilize the motif density concept in the collaborative optimization process to efficiently search for the optimal topology. To support our approach, extensive mathematical proofs are provided to demonstrate the advantages of the metric $I$ in effectively perceiving changes in IoT topology and to establish the convergence of the DAiMo algorithm. Finally, we conduct comprehensive performance evaluations of DAiMo and investigate the influence of network motifs on the resilience and reliability of IoT topologies. Experimental results clearly indicate that the proposed method outperforms existing state-of-the-art topology optimization methods in terms of enhancing network robustness.
{"title":"DAiMo: Motif Density Enhances Topology Robustness for Highly Dynamic Scale-Free IoT","authors":"Ning Chen;Tie Qiu;Weisheng Si;Dapeng Oliver Wu","doi":"10.1109/TMC.2024.3492002","DOIUrl":"https://doi.org/10.1109/TMC.2024.3492002","url":null,"abstract":"Robust Topology is a key prerequisite to providing consistent connectivity for highly dynamic Internet-of-Things (IoT) applications that are suffering node failures. In this paper, we present a two-step approach to organizing the most robust IoT topology. First, we propose a novel robustness metric denoted as <inline-formula><tex-math>$I$</tex-math></inline-formula>, which is based on network motifs and is specifically designed to sensitively analyze the dynamic changes in topology resulting from node failures. Second, we introduce a Distributed duAl-layer collaborative competition optimization strategy based on Motif density (DAiMo). This strategy significantly expands the search space for optimal solutions and facilitates the identification of the optimal IoT topology. We utilize the motif density concept in the collaborative optimization process to efficiently search for the optimal topology. To support our approach, extensive mathematical proofs are provided to demonstrate the advantages of the metric <inline-formula><tex-math>$I$</tex-math></inline-formula> in effectively perceiving changes in IoT topology and to establish the convergence of the DAiMo algorithm. Finally, we conduct comprehensive performance evaluations of DAiMo and investigate the influence of network motifs on the resilience and reliability of IoT topologies. Experimental results clearly indicate that the proposed method outperforms existing state-of-the-art topology optimization methods in terms of enhancing network robustness.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2360-2375"},"PeriodicalIF":7.7,"publicationDate":"2024-11-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360907","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 : 2024-11-04DOI: 10.1109/TMC.2024.3490835
Feifei Zhang;Mao Li;Jidong Ge;Fenghui Tang;Sheng Zhang;Jie Wu;Bin Luo
With the development of large-scale artificial intelligence services, edge devices are becoming essential providers of data and computing power. However, these edge devices are not immune to malicious attacks. Federated learning (FL), while protecting privacy of decentralized data through secure aggregation, struggles to trace adversaries and lacks optimization for heterogeneity. We discover that FL augmented with Differentiable Architecture Search (DARTS) can improve resilience against backdoor attacks while compatible with secure aggregation. Based on this, we propose a federated neural architecture search (NAS) framwork named SLNAS. The architecture of SLNAS is built on three pivotal components: a server-side search space generation method that employs an evolutionary algorithm with dual encodings, a federated NAS process based on DARTS, and client-side architecture tuning that utilizes Gumbel softmax combined with knowledge distillation. To validate robustness, we adapt a framework that includes backdoor attacks based on trigger optimization, data poisoning, and model poisoning, targeting both model weights and architecture parameters. Extensive experiments demonstrate that SLNAS not only effectively counters advanced backdoor attacks but also handles heterogeneity, outperforming defense baselines across a wide range of backdoor attack scenarios.
{"title":"Privacy-Preserving Federated Neural Architecture Search With Enhanced Robustness for Edge Computing","authors":"Feifei Zhang;Mao Li;Jidong Ge;Fenghui Tang;Sheng Zhang;Jie Wu;Bin Luo","doi":"10.1109/TMC.2024.3490835","DOIUrl":"https://doi.org/10.1109/TMC.2024.3490835","url":null,"abstract":"With the development of large-scale artificial intelligence services, edge devices are becoming essential providers of data and computing power. However, these edge devices are not immune to malicious attacks. Federated learning (FL), while protecting privacy of decentralized data through secure aggregation, struggles to trace adversaries and lacks optimization for heterogeneity. We discover that FL augmented with Differentiable Architecture Search (DARTS) can improve resilience against backdoor attacks while compatible with secure aggregation. Based on this, we propose a federated neural architecture search (NAS) framwork named SLNAS. The architecture of SLNAS is built on three pivotal components: a server-side search space generation method that employs an evolutionary algorithm with dual encodings, a federated NAS process based on DARTS, and client-side architecture tuning that utilizes Gumbel softmax combined with knowledge distillation. To validate robustness, we adapt a framework that includes backdoor attacks based on trigger optimization, data poisoning, and model poisoning, targeting both model weights and architecture parameters. Extensive experiments demonstrate that SLNAS not only effectively counters advanced backdoor attacks but also handles heterogeneity, outperforming defense baselines across a wide range of backdoor attack scenarios.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2234-2252"},"PeriodicalIF":7.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360984","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}
While small drone video streaming systems create unprecedented video content, they also place a power burden exceeding 20% on the drone's battery, limiting flight endurance. We present ${sf Mighty}$, a hardware-software solution to minimize the power consumption of a drone's video streaming system by offloading power overheads associated with both video compression and transmission to a ground controller. ${sf Mighty}$ innovates a high performance co-design among: (1) a ring oscillator-based, ultra-low power backscatter radio; (2) a spectrally-efficient, non-linear, low-power physical layer modulation and multi-chain radio architecture; and (3) a lightweight video compression codec-bypassing software design. Our co-design exploits synergies among these components, resulting in joint throughput and range performance that pushes the known envelope. We prototype ${sf Mighty}$ on PCB board and conduct extensive field studies both indoors and outdoors. The power efficiency of ${sf Mighty}$ is about 16.6 nJ/bit. A head-to-head comparison with a DJI Mini2 drone's default video streaming system shows that ${sf Mighty}$ achieves similar throughput at a drone-to-controller distance of up to 150 meters, with 34–55× improvement of power efficiency than WiFi-based video streaming solutions.
{"title":"Mighty: Towards Long-Range and High-Throughput Backscatter for Drones","authors":"Xiuzhen Guo;Yuan He;Longfei Shangguan;Yande Chen;Chaojie Gu;Yuanchao Shu;Kyle Jamieson;Jiming Chen","doi":"10.1109/TMC.2024.3486993","DOIUrl":"https://doi.org/10.1109/TMC.2024.3486993","url":null,"abstract":"While <i>small</i> drone video streaming systems create unprecedented video content, they also place a power burden exceeding 20% on the drone's battery, limiting flight endurance. We present <inline-formula><tex-math>${sf Mighty}$</tex-math></inline-formula>, a hardware-software solution to minimize the power consumption of a drone's video streaming system by offloading power overheads associated with both video compression and transmission to a ground controller. <inline-formula><tex-math>${sf Mighty}$</tex-math></inline-formula> innovates a high performance co-design among: <b>(1)</b> a ring oscillator-based, ultra-low power backscatter radio; <b>(2)</b> a spectrally-efficient, non-linear, low-power physical layer modulation and multi-chain radio architecture; and <b>(3)</b> a lightweight video compression codec-bypassing software design. Our co-design exploits synergies among these components, resulting in joint throughput and range performance that pushes the known envelope. We prototype <inline-formula><tex-math>${sf Mighty}$</tex-math></inline-formula> on PCB board and conduct extensive field studies both indoors and outdoors. The power efficiency of <inline-formula><tex-math>${sf Mighty}$</tex-math></inline-formula> is about 16.6 nJ/bit. A head-to-head comparison with a <i>DJI Mini2</i> drone's default video streaming system shows that <inline-formula><tex-math>${sf Mighty}$</tex-math></inline-formula> achieves similar throughput at a drone-to-controller distance of up to 150 meters, with 34–55× improvement of power efficiency than WiFi-based video streaming solutions.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1833-1845"},"PeriodicalIF":7.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360886","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 : 2024-11-04DOI: 10.1109/TMC.2024.3491337
Yang Li;Dan Wu;Jiahe Chen;Weiyan Shi;Leye Wang;Lu Su;Wenwei Li;Daqing Zhang
The widespread deployment of WiFi infrastructure has facilitated the development of Time-of-Flight (ToF) based sensing applications. ToF estimation, however, is a challenging task due to the complexity of multipath effect. In this paper, we propose a phase difference based method for ToF estimation and uncover the potential of signal cancellation to mitigate the impact of multipath and noise on phase differences among subcarriers. To separate the moving target path from the complex multipath for ToF estimation, we suggest employing specific elimination methods tailored to the characteristics of different signal components. For dynamic multipath, we observe that when a given subcarrier propagates along two paths to the receiver, with path lengths differing by half a wavelength, the phase difference introduced by these two paths cancels each other out. Therefore, we propose two metrics to identify signals that satisfy this condition, utilizing both frequency diversity and spatial diversity. Additionally, we propose leveraging time diversity to eliminate the static multipath component and reduce the impact of noise. We implemented the methods with off-the-shelf WiFi devices and achieved mean errors of 15.36 cm and 21.05 cm for distance estimation in outdoor and indoor scenarios, outperforming state-of-the-art ToF estimation method by 50% error reduction.
{"title":"SigCan: Toward Reliable ToF Estimation Leveraging Multipath Signal Cancellation on Commodity WiFi Devices","authors":"Yang Li;Dan Wu;Jiahe Chen;Weiyan Shi;Leye Wang;Lu Su;Wenwei Li;Daqing Zhang","doi":"10.1109/TMC.2024.3491337","DOIUrl":"https://doi.org/10.1109/TMC.2024.3491337","url":null,"abstract":"The widespread deployment of WiFi infrastructure has facilitated the development of Time-of-Flight (ToF) based sensing applications. ToF estimation, however, is a challenging task due to the complexity of multipath effect. In this paper, we propose a phase difference based method for ToF estimation and uncover the potential of signal cancellation to mitigate the impact of multipath and noise on phase differences among subcarriers. To separate the moving target path from the complex multipath for ToF estimation, we suggest employing specific elimination methods tailored to the characteristics of different signal components. For dynamic multipath, we observe that when a given subcarrier propagates along two paths to the receiver, with path lengths differing by half a wavelength, the phase difference introduced by these two paths cancels each other out. Therefore, we propose two metrics to identify signals that satisfy this condition, utilizing both frequency diversity and spatial diversity. Additionally, we propose leveraging time diversity to eliminate the static multipath component and reduce the impact of noise. We implemented the methods with off-the-shelf WiFi devices and achieved mean errors of 15.36 cm and 21.05 cm for distance estimation in outdoor and indoor scenarios, outperforming state-of-the-art ToF estimation method by 50% error reduction.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"1895-1912"},"PeriodicalIF":7.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360890","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 low-orbit mega-constellation network (LMCN) is an important part of the space-air-ground integrated network system. An effective satellite-ground interconnection design can result in a stable constellation topology for LMCNs. A naïve solution is accessing the satellite with the longest remaining service time (LRST), which is widely used in previous designs. The Coordinated Satellite-Ground Interconnecting (CSGI), the state-of-the-art algorithm, coordinates the establishment of ground-satellite links (GSLs). Compared with existing solutions, it reduces latency by 19% and jitter by 70% on average. However, CSGI only supports the scenario where terminals access only one satellite, and cannot fully utilize the multi-access capabilities of terminals. Additionally, CSGI's high computational complexity poses deployment challenges. To overcome these problems, we propose the Classification-based Longest Remaining Service Time (C-LRST) algorithm. C-LRST supports the actual scenario with multi-access capabilities. It adds optional paths during routing with low computational complexity, improving end-to-end communications quality. We conduct our 1000 s simulation from Brazil to Lithuania on the open-source platform Hypatia. Experiment results show that compared with CSGI, C-LRST reduces the latency and increases the throughput by approximately 60% and 40%, respectively. In addition, C-LRST's GSL switchings number is 14, whereas CSGI is 23. C-LRST has better link stability than CSGI.
{"title":"Efficient Satellite-Ground Interconnection Design for Low-Orbit Mega-Constellation Topology","authors":"Wenhao Liu;Jiazhi Wu;Quanwei Lin;Handong Luo;Qi Zhang;Kun Qiu;Zhe Chen;Yue Gao","doi":"10.1109/TMC.2024.3490575","DOIUrl":"https://doi.org/10.1109/TMC.2024.3490575","url":null,"abstract":"The low-orbit mega-constellation network (LMCN) is an important part of the space-air-ground integrated network system. An effective satellite-ground interconnection design can result in a stable constellation topology for LMCNs. A naïve solution is accessing the satellite with the longest remaining service time (LRST), which is widely used in previous designs. The Coordinated Satellite-Ground Interconnecting (CSGI), the state-of-the-art algorithm, coordinates the establishment of ground-satellite links (GSLs). Compared with existing solutions, it reduces latency by 19% and jitter by 70% on average. However, CSGI only supports the scenario where terminals access only one satellite, and cannot fully utilize the multi-access capabilities of terminals. Additionally, CSGI's high computational complexity poses deployment challenges. To overcome these problems, we propose the Classification-based Longest Remaining Service Time (C-LRST) algorithm. C-LRST supports the actual scenario with multi-access capabilities. It adds optional paths during routing with low computational complexity, improving end-to-end communications quality. We conduct our 1000 s simulation from Brazil to Lithuania on the open-source platform Hypatia. Experiment results show that compared with CSGI, C-LRST reduces the latency and increases the throughput by approximately 60% and 40%, respectively. In addition, C-LRST's GSL switchings number is 14, whereas CSGI is 23. C-LRST has better link stability than CSGI.","PeriodicalId":50389,"journal":{"name":"IEEE Transactions on Mobile Computing","volume":"24 3","pages":"2098-2109"},"PeriodicalIF":7.7,"publicationDate":"2024-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143360918","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 : 2024-11-04DOI: 10.1109/TMC.2024.3491574
Yu Luo;Lina Pu;Jun Wang;Isaac L. Howard
This research contributes to long-term structural health monitoring (SHM) by exploring radio frequency energy-powered sensor nodes (RF-SNs) embedded in concrete. The RF-SN captures radio energy from a mobile radio transmitter for sensing and communication, which offers a cost-effective solution for consistent in-situ perception. To optimize the system performance across various situations, we’ve explored both active and passive communication methods. For the active RF-SN, we implement a specialized control circuit enabling the node to transmit data through ZigBee protocol at low incident power. For the passive RF-SN, radio energy is not only for power but also as a carrier signal, with data conveyed by modulating the amplitude of the backscattered radio wave. To address the challenge of significant attenuation of the backscattering signal in concrete, we utilize a square chirp-based modulation scheme for passive communication. This scheme allows the receiver to successfully decode the data even under a negative signal-to-noise ratio (SNR) condition. Performance modeling and optimization for both active and passive RF-SNs are provided in this study. The experimental results verify that an active RF-SN embedded in concrete at a depth of 13.5 cm can be effectively powered by a 915 MHz mobile radio transmitter with an effective isotropic radiated power (EIRP) of 32.5 dBm. This setup allows the RF-SN to send over 1 kB of data within 10 seconds, with an additional 1.7 kilobytes every 1.6 seconds of extra charging. For the passive RF-SN buried at the same depth, continuous data transmission at a rate of 224 bps with a 3% bit error rate (BER) is achieved when the EIRP of the transmitter is 23.6 dBm.
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