ACM Transactions on Sensor Networks最新文献

Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment 在人工智能赋能的边缘计算环境中，为资源受限的设备提供模型剪枝功能的联合拆分学习

IF 3.9 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-08-10 DOI: 10.1145/3687478

Yongzhe Jia, Bowen Liu, Xuyun Zhang, Fei Dai, Arif Khan, Lianyong Qi, Wanchun Dou

{"title":"Model Pruning-enabled Federated Split Learning for Resource-constrained Devices in Artificial Intelligence Empowered Edge Computing Environment","authors":"Yongzhe Jia, Bowen Liu, Xuyun Zhang, Fei Dai, Arif Khan, Lianyong Qi, Wanchun Dou","doi":"10.1145/3687478","DOIUrl":"https://doi.org/10.1145/3687478","url":null,"abstract":"Distributed Collaborative Machine Learning (DCML) has emerged in artificial intelligence-empowered edge computing environments, such as the Industrial Internet of Things (IIoT), to process tremendous data generated by smart devices. However, parallel DCML frameworks require resource-constrained devices to update the entire Deep Neural Network (DNN) models and are vulnerable to reconstruction attacks. Concurrently, the serial DCML frameworks suffer from training efficiency problems due to their serial training nature. In this paper, we propose a Model Pruning-enabled Federated Split Learning framework (MP-FSL) to reduce resource consumption with a secure and efficient training scheme. Specifically, MP-FSL compresses DNN models by adaptive channel pruning and splits each compressed model into two parts that are assigned to the client and the server. Meanwhile, MP-FSL adopts a novel aggregation algorithm to aggregate the pruned heterogeneous models. We implement MP-FSL with a real FL platform to evaluate its performance. The experimental results show that MP-FSL outperforms the state-of-the-art frameworks in model accuracy by up to 1.35%, while concurrently reducing storage and computational resource consumption by up to 32.2% and 26.73%, respectively. These results demonstrate that MP-FSL is a comprehensive solution to the challenges faced by DCML, with superior performance in both reduced resource consumption and enhanced model performance.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141921099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Adaptive Weighting via Federated Evaluation Mechanism for Domain Adaptation with Edge Devices 通过联合评估机制进行自适应加权，实现边缘设备的领域适应性

IF 3.9 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-07-25 DOI: 10.1145/3669903

Rui Zhao, Xiao Yang, Peng Zhi, Rui Zhou, Qingguo Zhou, Qun Jin

{"title":"Adaptive Weighting via Federated Evaluation Mechanism for Domain Adaptation with Edge Devices","authors":"Rui Zhao, Xiao Yang, Peng Zhi, Rui Zhou, Qingguo Zhou, Qun Jin","doi":"10.1145/3669903","DOIUrl":"https://doi.org/10.1145/3669903","url":null,"abstract":"Federated Learning is an emerging application paradigm of edge computing in smart cities. On the one hand, it enables efficient, private, and secure processing of sensitive data. On the other hand, it alleviates the burden of centralized data processing for the smart city. However, in real-world scenarios, performance degradation caused by domain adaptation has become a bottleneck that limits the widespread application of federated learning. Most existing approaches tackle the issue by designing novel local learning approaches to transfer knowledge among different domains while ignoring the optimization for global model aggregation. To address this issue, we propose a novel approach that leverages the label-free adversarial learning technique to evaluate the representations learned by the different domains under the global model. With the constraints of the federated setting, we minimize the discrepancy by aligning each distribution to the global distribution. Additionally, we have developed a fast detector to enhance the quality of the generated images. Through extensive experiments on image classification tasks, we have demonstrated promising results and shown that our approach can serve as a robust plugin for other local optimizers in Federated Learning.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141803768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Leveraging Attention-reinforced UWB Signals to Monitor Respiration During Sleep 利用注意力强化型超宽带波长信号监测睡眠呼吸

IF 3.9 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-07-24 DOI: 10.1145/3680550

Siheng Li, Beihong Jin, Zhi Wang, Fusang Zhang, Xiaoyong Ren, Haiqin Liu

{"title":"Leveraging Attention-reinforced UWB Signals to Monitor Respiration During Sleep","authors":"Siheng Li, Beihong Jin, Zhi Wang, Fusang Zhang, Xiaoyong Ren, Haiqin Liu","doi":"10.1145/3680550","DOIUrl":"https://doi.org/10.1145/3680550","url":null,"abstract":"The respiration state during overnight sleep is an important indicator of human health. However, existing contactless solutions for sleep respiration monitoring either perform in controlled environments and have low usability in practical scenarios, or only provide coarse-grained respiration rates, being unable to accurately detect abnormal events in patients. In this paper, we propose Respnea, a non-intrusive sleep respiration monitoring system using an ultra-wideband (UWB) device. Particularly, we propose a profiling algorithm, which can locate the sleep positions in non-controlled environments and identify different subject states. Further, we construct a deep learning model that adopts a multi-head self-attention mechanism and learns the patterns implicit in the respiration signals to distinguish sleep respiration events at a granularity of seconds. To improve the generalization of the model, we propose a contrastive learning strategy to learn a robust representation of the respiration signals. We deploy our system in hospital and home scenarios and conduct experiments on data from healthy subjects and patients with sleep disorders. The experimental results show that Respnea achieves high temporal coverage and low errors (a median error of 0.27 bpm) in respiration rate estimation and reaches an accuracy of 94.44% on diagnosing the severity of sleep apnea-hypopnea syndrome (SAHS).","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141807773","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Smartphone-based 3D Hand Pose Reconstruction Using Acoustic Signals 利用声学信号实现基于智能手机的 3D 手部姿势重建

IF 3.9 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-07-16 DOI: 10.1145/3677122

Shiyang Wang, Xingchen Wang, Wenjun Jiang, Chenglin Miao, Qiming Cao, Haoyu Wang, Ke Sun, Hongfei Xue, Lu Su

{"title":"Towards Smartphone-based 3D Hand Pose Reconstruction Using Acoustic Signals","authors":"Shiyang Wang, Xingchen Wang, Wenjun Jiang, Chenglin Miao, Qiming Cao, Haoyu Wang, Ke Sun, Hongfei Xue, Lu Su","doi":"10.1145/3677122","DOIUrl":"https://doi.org/10.1145/3677122","url":null,"abstract":"Accurately reconstructing 3D hand poses is a pivotal element for numerous Human-Computer Interaction applications. In this work, we propose SonicHand, the first Smartphone-based 3D Hand Pose Reconstruction system using purely inaudible acoustic signals. SonicHand incorporates signal processing techniques and a deep learning framework to address a series of challenges. Firstly, it encodes the topological information of the hand skeleton as prior knowledge and utilizes a deep learning model to realistically and smoothly reconstruct the hand poses. Secondly, the system employs adversarial training to enhance the generalization ability of our system to be deployed in a new environment or for a new user. Thirdly, we adopt a hand tracking method based on channel impulse response (CIR) estimation. It enables our system to handle the scenario where the hand performs gestures while moving arbitrarily as a whole. We conduct extensive experiments on a smartphone testbed to demonstrate the effectiveness and robustness of our system from various dimensions. The experiments involve 10 subjects performing up to 12 different hand gestures in 3 distinctive environments. When the phone is held in one of the user’s hand, the proposed system can track joints with an average error of 18.64 mm.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Self-Supervised EEG Representation Learning for Robust Emotion Recognition 用于鲁棒性情绪识别的自我监督脑电图表征学习

IF 3.9 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-07-05 DOI: 10.1145/3674975

Huan Liu, Yuzhe Zhang, Xuxu Chen, Dalin Zhang, Rui Li, Tao Qin

{"title":"Self-Supervised EEG Representation Learning for Robust Emotion Recognition","authors":"Huan Liu, Yuzhe Zhang, Xuxu Chen, Dalin Zhang, Rui Li, Tao Qin","doi":"10.1145/3674975","DOIUrl":"https://doi.org/10.1145/3674975","url":null,"abstract":"Emotion recognition based on electroencephalography (EEG) is becoming a growing concern of researchers due to its various applications and portable devices. Existing methods are mainly dedicated to EEG feature representation and have made impressive progress. However, the problem of scarce labels restricts their further promotion. In light of this, we propose a self-supervised framework with contrastive learning for robust EEG-based emotion recognition, which can effectively leverage both readily available unlabeled EEG signals and labeled ones to learn highly discriminative EEG features. Firstly, we construct a specific pretext task according to the sequential non-stationarity of emotional EEG signals for contrastive learning, which aims to extract pseudo-label information from all EEG data. Meanwhile, we propose a novel negative segment selection algorithm to reduce the noise of unlabeled data during the contrastive learning process. Secondly, to mitigate the overfitting issue induced by a small number of labeled samples during learning, we originate a loss function with label smoothing regularization that can guide the model to learn generalizable features. Extensive experiments over three benchmark datasets demonstrate the effectiveness and superiority of our model on EEG-based emotion recognition task. Besides, the generalization and robustness of the model have also been proved through sufficient experiments.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141676435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Towards Distributed Flow Scheduling in IEEE 802.1Qbv Time-Sensitive Networks 在 IEEE 802.1Qbv 时间敏感型网络中实现分布式流量调度

IF 3.9 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-07-04 DOI: 10.1145/3676848

Miao Guo, Shibo He, Chaojie Gu, Xiuzhen Guo, Jiming Chen, Tao Gao, Tongtong Wang

{"title":"Towards Distributed Flow Scheduling in IEEE 802.1Qbv Time-Sensitive Networks","authors":"Miao Guo, Shibo He, Chaojie Gu, Xiuzhen Guo, Jiming Chen, Tao Gao, Tongtong Wang","doi":"10.1145/3676848","DOIUrl":"https://doi.org/10.1145/3676848","url":null,"abstract":"Flow scheduling plays a pivotal role in enabling Time-Sensitive Networking (TSN) applications. Current flow scheduling mainly adopts a centralized scheme, posing challenges in adapting to dynamic network conditions and scaling up for larger networks. To address these challenges, we first thoroughly analyze the flow scheduling problem and find the inherent locality nature of time scheduling tasks. Leveraging this insight, we introduce the first distributed framework for IEEE 802.1Qbv TSN flow scheduling. In this framework, we further propose a multi-agent flow scheduling method by designing Deep Reinforcement Learning (DRL)-based route and time agents for route and time planning tasks. The time agents are deployed on field devices to schedule flows in a distributed way. Evaluations in dynamic scenarios validate the effectiveness and scalability of our proposed method. It enhances the scheduling success rate by 20.31% compared to state-of-the-art methods and achieves substantial cost savings, reducing transmission costs by 410 × in large-scale networks. Additionally, we validate our approach on edge devices and a TSN testbed, highlighting its lightweight nature and ease of deployment.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":3.9,"publicationDate":"2024-07-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141678284","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on Blockchain 通过基于区块链的去中心化自适应聚合实现公平、稳健的联合学习

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-06-17 DOI: 10.1145/3673656

Du Bowen, Wang Haiquan, Li Yuxuan, Jiejie Zhao, Yanbo Ma, Huang Runhe

{"title":"Fair and Robust Federated Learning via Decentralized and Adaptive Aggregation based on Blockchain","authors":"Du Bowen, Wang Haiquan, Li Yuxuan, Jiejie Zhao, Yanbo Ma, Huang Runhe","doi":"10.1145/3673656","DOIUrl":"https://doi.org/10.1145/3673656","url":null,"abstract":"As an emerging learning paradigm, Federated Learning (FL) enables data owners to collaborate training a model while keeps data locally. However, classic FL methods are susceptible to model poisoning attacks and Byzantine failures. Despite several defense methods proposed to mitigate such concerns, it is challenging to balance adverse effects while allowing that each credible node contributes to the learning process. To this end, a Fair and Robust FL method is proposed for defense against model poisoning attack from malicious nodes, namely FRFL. FRFL can learn a high-quality model even if some nodes are malicious. In particular, we first classify each participant into three categories: training node, validation node, and blockchain node. Among these, blockchain nodes replace the central server in classic FL methods while enabling secure aggregation. Then, a fairness-aware role rotation method is proposed to periodically alter the sets of training and validation nodes in order to utilize the valuable information included in local datasets of credible nodes. Finally, a decentralized and adaptive aggregation mechanism cooperating with blockchain nodes is designed to detect and discard malicious nodes and produce a high-quality model. The results show the effectiveness of FRFL in enhancing model performance while defending against malicious nodes.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141549747","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Energy and QoE Optimization for Mobile Video Streaming with Adaptive Brightness Scaling 利用自适应亮度调节优化移动视频流的能耗和 QoE

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-06-12 DOI: 10.1145/3670999

Daibo Liu, Chao Qian, Huigui Rong, Siwang Zhou, Chaocan Xiang, Hongbo Jiang

{"title":"Energy and QoE Optimization for Mobile Video Streaming with Adaptive Brightness Scaling","authors":"Daibo Liu, Chao Qian, Huigui Rong, Siwang Zhou, Chaocan Xiang, Hongbo Jiang","doi":"10.1145/3670999","DOIUrl":"https://doi.org/10.1145/3670999","url":null,"abstract":"Brightness scaling (BS) is an emerging and promising technique with outstanding energy efficiency on mobile video streaming. However, existing BS-based approaches totally neglect the inherent interaction effect between BS factor, video bitrate and environment context. Their combined impact on user’s visual perception in mobile scenario, leading to inharmonious between energy consumption and user’s quality of experience (QoE). In this paper, we propose PEO, a novel user-Perception-based video Experience Optimization for energy-constrained mobile video streaming, by jointly considering the inherent connection between device’s state of motion, video quality and the resulting user-perceived quality. Specifically, by capturing the motion of on-the-run device, PEO first infers the optimal bitrate and BS factor, therefore avoiding bitrate-inefficiency for energy saving while guaranteeing the user-perceived QoE. On that basis, we formulate the device motion-aware and user perception-aware video streaming as an optimization problem where we present an optimal algorithm to maximize the object function and adapt to user preference, and thus propose an online bitrate selection algorithm. Our evaluation (based on trace analysis and user study) shows that, compared with state-of-the-art techniques, PEO can raise the perceived quality by 23.8%-41.3% and save up to 25.2% energy consumption.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141353744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PnA: Robust Aggregation Against Poisoning Attacks to Federated Learning for Edge Intelligence PnA：针对中毒攻击的稳健聚合到边缘智能的联合学习

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-06-01 DOI: 10.1145/3669902

Jingkai Liu, Xiaoting Lyu, Li Duan, Yongzhong He, Jiqiang Liu, Hongliang Ma, Bin Wang, Chunhua Su, Wei Wang

{"title":"PnA: Robust Aggregation Against Poisoning Attacks to Federated Learning for Edge Intelligence","authors":"Jingkai Liu, Xiaoting Lyu, Li Duan, Yongzhong He, Jiqiang Liu, Hongliang Ma, Bin Wang, Chunhua Su, Wei Wang","doi":"10.1145/3669902","DOIUrl":"https://doi.org/10.1145/3669902","url":null,"abstract":"Federated learning (FL), which holds promise for use in edge intelligence applications for smart cities, enables smart devices collaborate in training a global model by exchanging local model updates instead of sharing local training data. However, the global model can be corrupted by malicious clients conducting poisoning attacks, resulting in the failure of converging the global model, incorrect predictions on the test set, or the backdoor embedded. Although some aggregation algorithms can enhance the robustness of FL against malicious clients, our work demonstrates that existing stealthy poisoning attacks can still bypass these defense methods. In this work, we propose a robust aggregation mechanism, called Parts and All (PnA), to protect the global model of FL by filtering out malicious local model updates throughout the detection of poisoning attacks at layers of local model updates. We conduct comprehensive experiments on three representative datasets. The experimental results demonstrate that our proposed PnA is more effective than existing robust aggregation algorithms against state-of-the-art poisoning attacks. Besides, PnA has a stable performance against poisoning attacks with different poisoning settings.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141197958","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

HCCNet: Hybrid Coupled Cooperative Network for Robust Indoor Localization HCCNet：用于稳健室内定位的混合耦合合作网络

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2024-05-27 DOI: 10.1145/3665645

Li Zhang, Xu Zhou, Danyang Li, Zheng Yang

{"title":"HCCNet: Hybrid Coupled Cooperative Network for Robust Indoor Localization","authors":"Li Zhang, Xu Zhou, Danyang Li, Zheng Yang","doi":"10.1145/3665645","DOIUrl":"https://doi.org/10.1145/3665645","url":null,"abstract":"Accurate localization of unmanned aerial vehicle (UAV) is critical for navigation in GPS-denied regions, which remains a highly challenging topic in recent research. This paper describes a novel approach to multi-sensor hybrid coupled cooperative localization network (HCCNet) system that combines multiple types of sensors including camera, ultra-wideband (UWB), and inertial measurement unit (IMU) to address this challenge. The camera and IMU can automatically determine the position of UAV based on the perception of surrounding environments and their own measurement data. The UWB node and the UWB wireless sensor network (WSN) in indoor environments jointly determine the global position of UAV, and the proposed dynamic random sample consensus (D-RANSAC) algorithm can optimize UWB localization accuracy. To fully exploit UWB localization results, we provide a HCCNet system which combines the local pose estimator of visual inertial odometry (VIO) system with global constraints from UWB localization results. Experimental results show that the proposed D-RANSAC algorithm can achieve better accuracy than other UWB-based algorithms. The effectiveness of the proposed HCCNet method is verified by a mobile robot in real world and some simulation experiments in indoor environments.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":null,"pages":null},"PeriodicalIF":4.1,"publicationDate":"2024-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141167566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0