ACM Transactions on Sensor Networks最新文献_第10页

EchoSensor: Fine-Grained Ultrasonic Sensing for Smart Home Intrusion Detection EchoSensor：用于智能家居入侵检测的细粒度超声波传感

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-08-12 DOI: 10.1145/3615658

Jie Lian, Changlai Du, Jiadong Lou, Li Chen, Xu Yuan

{"title":"EchoSensor: Fine-Grained Ultrasonic Sensing for Smart Home Intrusion Detection","authors":"Jie Lian, Changlai Du, Jiadong Lou, Li Chen, Xu Yuan","doi":"10.1145/3615658","DOIUrl":"https://doi.org/10.1145/3615658","url":null,"abstract":"This paper presents the design and implementation of a novel intrusion detection system, called EchoSensor, which leverages speakers and microphones in smart home devices to capture human gait patterns for individual identification. EchoSensor harnesses the speaker to send inaudible acoustic signals (around 20kHz) and utilizes the microphone to capture the reflected signals. As the reflected signals have unique variations in the Doppler shift respective to the gaits of different people, EchoSensor is able to profile human gait patterns from the generated spectrograms. To mine the gait information, we first propose a two-stage interference cancellation scheme to remove the background noise and environmental interference, followed by a new method to detect the starting point of walking and estimate the gait cycle time. We then perform the fine-grained analysis of the spectrograms to extract a series of features. In the end, machine learning is employed to construct an identifier for individual recognition. We implement the EchoSensor system and deploy it under different household environments to conduct intrusion detection tasks. Extensive experimental results have demonstrated that EchoSensor can achieve the averaged Intruder Gait Detection Rate (IDR) and True Family Member Gait Detection Rate (TFR) of 92.7% and 91.9%, respectively.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47079341","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: 1

TFSemantic: A Time-Frequency Semantic GAN Framework for Imbalanced Classification Using Radio Signals 基于无线电信号的不平衡分类时频语义GAN框架

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-08-08 DOI: 10.1145/3614096

Peng Liao, Xuyu Wang, Lin An, Shiwen Mao, Tianya Zhao, Chao Yang

{"title":"TFSemantic: A Time-Frequency Semantic GAN Framework for Imbalanced Classification Using Radio Signals","authors":"Peng Liao, Xuyu Wang, Lin An, Shiwen Mao, Tianya Zhao, Chao Yang","doi":"10.1145/3614096","DOIUrl":"https://doi.org/10.1145/3614096","url":null,"abstract":"Recently, wireless sensing techniques have been widely used for Internet of Things (IoT) applications. Unlike traditional device-based sensing, wireless sensing is contactless, pervasive, low-cost, and non-invasive, making it highly suitable for relevant IoT applications. However, most existing methods are highly dependent on high-quality datasets, and the minority class will not achieve a satisfactory performance when suffering from a class imbalance problem. In this paper, we propose a time-frequency semantic generative adversarial network (GAN) framework (i.e., TFSemantic) to address the imbalanced classification problem in human activity recognition (HAR) using radio frequency (RF) signals. Specifically, the TFSemantic framework can learn semantic features from the minority classes and then generate high-quality signals to restore data balance. It includes a data pre-processing module, a semantic extraction module, a semantic distribution module, and a data augmenter module. In the data pre-processing module, we process four different RF datasets (i.e., WiFi, RFID, UWB, and mmWave). We also develop Fourier semantic feature convolution (SFC) and attention semantic feature embedding (SFE) methods for the semantic extraction module. A discrete wavelet transform (DWT) is utilized for reconstructed RF samples in the semantic distribution module. In data augmenter module, we design an associated loss function to achieve effective adversarial training. Finally, we validate the effectiveness of the proposed TFSemantic framework using different RF datasets, which outperforms several state-of-the-art methods.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45495804","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

VSSB-Raft:A Secure and Efficient Zero Trust Consensus Algorithm for Blockchain VSSB-Raft:一种安全高效的区块链零信任共识算法

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-08-08 DOI: 10.1145/3611308

Siben Tian, Fenhua Bai, Tao Shen, Chi Zhang, Gong Bei

引用次数: 0

Edge-assisted Object Segmentation using Multimodal Feature Aggregation and Learning 基于多模态特征聚合和学习的边缘辅助目标分割

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-08-04 DOI: 10.1145/3612922

Jianbo Li, Genji Yuan, Zheng Yang

{"title":"Edge-assisted Object Segmentation using Multimodal Feature Aggregation and Learning","authors":"Jianbo Li, Genji Yuan, Zheng Yang","doi":"10.1145/3612922","DOIUrl":"https://doi.org/10.1145/3612922","url":null,"abstract":"Object segmentation aims to perfectly identify objects embedded in the surrounding environment and has a wide range of applications. Most previous methods of object segmentation only use RGB images and ignore geometric information from disparity images. Making full use of heterogeneous data from different devices has proved to be a very effective strategy for improving segmentation performance. The key challenge of the multimodal fusion based object segmentation task lies in the learning, transformation, and fusion of multimodal information. In this paper, we focus on the transformation of disparity images and the fusion of multimodal features. We develop a multimodal fusion object segmentation framework, termed the Hybrid Fusion Segmentation Network (HFSNet). Specifically, HFSNet contains three key components, i.e., disparity convolutional sparse coding (DCSC), asymmetric dense projection feature aggregation (ADPFA) and multimodal feature fusion (MFF). The DCSC is designed based on convolutional sparse coding. It not only has better interpretability but also preserves the key geometric information of the object. ADPFA is designed to enhance texture and geometric information to fully exploit nonadjacent features. MFF is used to perform multimodal feature fusion. Extensive experiments show that our HFSNet outperforms existing state-of-the-art models on two challenging datasets.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"45512492","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

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-08-03 DOI: 10.1145/3613245

Zhihao Shen, Wan Du, Xi Zhao, Jianhua Zou

{"title":"Retrieving similar trajectories from cellular data of multiple carriers at city scale","authors":"Zhihao Shen, Wan Du, Xi Zhao, Jianhua Zou","doi":"10.1145/3613245","DOIUrl":"https://doi.org/10.1145/3613245","url":null,"abstract":"Retrieving similar trajectories aims to search for the trajectories that are close to a query trajectory in spatio-temporal domain from a large trajectory dataset. This is critical for a variety of applications, like transportation planning and mobility analysis. Unlike previous studies that perform similar trajectory retrieval on fine-grained GPS data or single cellular carrier, we investigate the feasibility of finding similar trajectories from cellular data of multiple carriers, which provide more comprehensive coverage of population and space. To handle the issues of spatial bias of cellular data from multiple carriers, coarse spatial granularity, and irregular sparse temporal sampling, we develop a holistic system cellSim. Specifically, to avoid the issue of spatial bias, we first propose a novel map matching approach, which transforms the cell tower sequences from multiple carriers to routes on a unified road map. Then, to address the issue of temporal sparse sampling, we generate multiple routes with different confidences to increases the probability of finding truly similar trajectories. Finally, a new trajectory similarity measure is developed for similar trajectory search by calculating the similarities between the irregularly-sampled trajectories. Extensive experiments on a large-scale cellular dataset from two carriers and real-world 1,701-km query trajectories reveal that cellSim provides state-of-the-art performance for similar trajectory retrieval.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48858119","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

Elder-oriented Active Learning for Adaptation of Perception Intelligence in Home Service Robots 面向老年人的主动学习用于家庭服务机器人感知智能的自适应

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-07-27 DOI: 10.1145/3607871

Qi Wang, Yan He, W. Sheng, Senlin Zhang, Meiqin Liu, Badong Chen

{"title":"Elder-oriented Active Learning for Adaptation of Perception Intelligence in Home Service Robots","authors":"Qi Wang, Yan He, W. Sheng, Senlin Zhang, Meiqin Liu, Badong Chen","doi":"10.1145/3607871","DOIUrl":"https://doi.org/10.1145/3607871","url":null,"abstract":"Active learning is a special case of machine learning in which a learning algorithm can interactively query a user to label new data points with the desired outputs. In robotics, active learning allows a robot to adapt its perception intelligence to a new environment with users’ help. This paper presents a new active learning method for elderly care robots to select data that is not only useful for learning but also easy for the elderly user to label. First, a series of image properties related to annotation difficulty are determined based on existing medical researches in human vision in elderly population. Based on that, a user study is conducted to determine the ground truth of annotation difficulty of images for the older adults. Second, a robust annotation difficulty predictor is developed using the results of the user study, and the difficulty prediction of an image is combined with three other active learning criteria to form an annotation difficulty-aware active learning metric, which facilitates the query data selection as the robot adapts its perception intelligence in a home environment. Third, we present an ablation study of the proposed active learning method through a simulation experiment. The experimental results validate the advantages of the proposed method.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44454923","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 Automatically Connecting IoT Devices with Vulnerabilities in the Wild 面向在野外自动连接具有漏洞的物联网设备

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-07-17 DOI: 10.1145/3608951

Jinke Song, Shangfeng Wan, Minfei Huang, Ji-Qiang Liu, Limin Sun, Qiang Li

{"title":"Towards Automatically Connecting IoT Devices with Vulnerabilities in the Wild","authors":"Jinke Song, Shangfeng Wan, Minfei Huang, Ji-Qiang Liu, Limin Sun, Qiang Li","doi":"10.1145/3608951","DOIUrl":"https://doi.org/10.1145/3608951","url":null,"abstract":"With the increasing number of Internet of Things (IoT) devices connected to the internet, the industry and research community have become increasingly concerned about their security impact. Adversaries or hackers often exploit public security flaws to compromise IoT devices and launch cyber attacks. However, despite this growing concern, little effort has been made to investigate the detection of IoT devices and their underlying risks. To address this gap, this paper proposes to automatically establish relationships between IoT devices and their vulnerabilities in the wild. Specifically, we construct a deep neural network (DNN) to extract semantic information from IoT packets and generate fine-grained fingerprints of IoT devices. This enables us to annotate IoT devices in cyberspace, including their device type, vendor, and product information. We collect vulnerability reports from various security sources and extract IoT device information from these reports to automatically match vulnerabilities with the fingerprints of IoT devices. We implemented a prototype system and conducted extensive experiments to validate the effectiveness of our approach. The results show that our DNN model achieved a 98% precision rate and a 95% recall rate in IoT device fingerprinting. Furthermore, we collected and analyzed over 13,063 IoT-related vulnerability reports and our method automatically built 5,458 connections between IoT device fingerprints and their vulnerabilities. These findings shed light on the ongoing threat of cyber-attacks on IoT systems as both IoT devices and disclosed vulnerabilities are targets for malicious attackers.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46447366","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: 1

Towards Automatically Connecting IoT Devices with Vulnerabilities in the Wild 实现自动连接存在漏洞的物联网设备

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-07-17 DOI: https://dl.acm.org/doi/10.1145/3608951

Jinke Song, Shangfeng Wan, Min Huang, Jiqiang Liu, Limin Sun, Qiang Li

{"title":"Towards Automatically Connecting IoT Devices with Vulnerabilities in the Wild","authors":"Jinke Song, Shangfeng Wan, Min Huang, Jiqiang Liu, Limin Sun, Qiang Li","doi":"https://dl.acm.org/doi/10.1145/3608951","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3608951","url":null,"abstract":"With the increasing number of Internet of Things (IoT) devices connected to the internet, the industry and research community have become increasingly concerned about their security impact. Adversaries or hackers often exploit public security flaws to compromise IoT devices and launch cyber attacks. However, despite this growing concern, little effort has been made to investigate the detection of IoT devices and their underlying risks. To address this gap, this paper proposes to automatically establish relationships between IoT devices and their vulnerabilities in the wild. Specifically, we construct a deep neural network (DNN) to extract semantic information from IoT packets and generate fine-grained fingerprints of IoT devices. This enables us to annotate IoT devices in cyberspace, including their device type, vendor, and product information. We collect vulnerability reports from various security sources and extract IoT device information from these reports to automatically match vulnerabilities with the fingerprints of IoT devices. We implemented a prototype system and conducted extensive experiments to validate the effectiveness of our approach. The results show that our DNN model achieved a 98% precision rate and a 95% recall rate in IoT device fingerprinting. Furthermore, we collected and analyzed over 13,063 IoT-related vulnerability reports and our method automatically built 5,458 connections between IoT device fingerprints and their vulnerabilities. These findings shed light on the ongoing threat of cyber-attacks on IoT systems as both IoT devices and disclosed vulnerabilities are targets for malicious attackers.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"28 1","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138517201","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

InferLoc: Hypothesis-based Joint Edge Inference and Localization in Sparse Sensor Networks 基于假设的稀疏传感器网络联合边缘推理与定位

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-07-12 DOI: https://dl.acm.org/doi/10.1145/3608477

Xuewei Bai, Yongcai Wang, Haodi Ping, Xiaojia Xu, Deying Li, Shuo Wang

{"title":"InferLoc: Hypothesis-based Joint Edge Inference and Localization in Sparse Sensor Networks","authors":"Xuewei Bai, Yongcai Wang, Haodi Ping, Xiaojia Xu, Deying Li, Shuo Wang","doi":"https://dl.acm.org/doi/10.1145/3608477","DOIUrl":"https://doi.org/https://dl.acm.org/doi/10.1145/3608477","url":null,"abstract":"Ranging-based localization is a fundamental problem in the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAV) networks. However, the nodes’ limited-ranging scope and users’ broad coverage purpose inevitably cause network sparsity or subnetwork sparsity. The performances of existing localization algorithms are extremely unsatisfactory in sparse networks. A crucial way to deal with the sparsity is to exploit the hidden knowledge provided by the unmeasured edges, which inspires this paper to propose a hypothesis-based Joint Edge Inference and Localization algorithm, i.e., InferLoc. InferLoc mines the Unmeasured but Inferable Edges (UIEs). Each UIE is an unmeasured edge, but it is restricted through other edges in the network to be inside a rigid component, so it has only a limited number of possible lengths. We propose an efficient method to detect UIEs and geometric approaches to infer possible lengths for UIEs in 2D and 3D networks. The inferred possible lengths of UIEs are then treated as multiple hypotheses to determine the node locations and the lengths of UIEs simultaneously through a joint graph optimization process. In the joint graph optimization model, to make the 0/1 decision variables for hypotheses selection differentiable, differentiable functions are proposed to relax the 0/1 selections, and rounding is applied to select the final length after the optimization converges. We also prove the condition when a UIE can contribute to sparse localization. Extensive experiments show remarkably better accuracy and efficiency performances of InferLoc than the state-of-the-art network localization algorithms. In particular, it reduces the localization errors by more than (90% ) and speeds up the convergence time over 100 times than the widely used G2O-based methods in sparse networks.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":"18 7","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138496484","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

InferLoc: Hypothesis-based Joint Edge Inference and Localization in Sparse Sensor Networks 基于假设的稀疏传感器网络联合边缘推理与定位

IF 4.1 4区计算机科学

ACM Transactions on Sensor Networks Pub Date : 2023-07-12 DOI: 10.1145/3608477

Xuewei Bai, Yongcai Wang, Haodi Ping, Xiaojia Xu, Deying Li, Shuo Wang

{"title":"InferLoc: Hypothesis-based Joint Edge Inference and Localization in Sparse Sensor Networks","authors":"Xuewei Bai, Yongcai Wang, Haodi Ping, Xiaojia Xu, Deying Li, Shuo Wang","doi":"10.1145/3608477","DOIUrl":"https://doi.org/10.1145/3608477","url":null,"abstract":"Ranging-based localization is a fundamental problem in the Internet of Things (IoT) and Unmanned Aerial Vehicles (UAV) networks. However, the nodes’ limited-ranging scope and users’ broad coverage purpose inevitably cause network sparsity or subnetwork sparsity. The performances of existing localization algorithms are extremely unsatisfactory in sparse networks. A crucial way to deal with the sparsity is to exploit the hidden knowledge provided by the unmeasured edges, which inspires this paper to propose a hypothesis-based Joint Edge Inference and Localization algorithm, i.e., InferLoc. InferLoc mines the Unmeasured but Inferable Edges (UIEs). Each UIE is an unmeasured edge, but it is restricted through other edges in the network to be inside a rigid component, so it has only a limited number of possible lengths. We propose an efficient method to detect UIEs and geometric approaches to infer possible lengths for UIEs in 2D and 3D networks. The inferred possible lengths of UIEs are then treated as multiple hypotheses to determine the node locations and the lengths of UIEs simultaneously through a joint graph optimization process. In the joint graph optimization model, to make the 0/1 decision variables for hypotheses selection differentiable, differentiable functions are proposed to relax the 0/1 selections, and rounding is applied to select the final length after the optimization converges. We also prove the condition when a UIE can contribute to sparse localization. Extensive experiments show remarkably better accuracy and efficiency performances of InferLoc than the state-of-the-art network localization algorithms. In particular, it reduces the localization errors by more than (90% ) and speeds up the convergence time over 100 times than the widely used G2O-based methods in sparse networks.","PeriodicalId":50910,"journal":{"name":"ACM Transactions on Sensor Networks","volume":" ","pages":""},"PeriodicalIF":4.1,"publicationDate":"2023-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46094730","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