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检索条件"任意字段=Proceedings of the 19th ACM Conference on Embedded Networked Sensor Systems"

共 791 条记录，以下是81-90 订阅

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Magnetic sensor based indoor positioning by multi-channel deep regression: Poster abstract 20

Magnetic sensor based indoor positioning by multi-channel de...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Antsfeld, Leonid Chidlovskii, Boris Borisov, Dmitrii Naver Labs Europe France Grenoble Alpes University France

ISBN: (纸本)9781450375900

Modern smartphones are equipped with built-in magnetometers that capture disturbances of the Earth's magnetic field induced by ferromagnetic objects. In indoor environment, using magnetic field data turns to be a strong alternative to conventional localization techniques as requiring no special infrastructure. We revise the state of the art methods based on landmark classification [5] and propose a novel approach. We represent magnetic data time series as image sequences and compose multi-channel input to a deep neural network. We use four methods, Recurrence plots, Gramian Angular Fields and Markov Transition Fields, to capture different patterns in magnetic data stream. We complete the landmark-based classification with deep regression on the user's position and combine convolutional and recurrent layers in the deep network. We evaluate our methods on the recently published MagPie dataset [3] and show that they outperform the state of the art methods. © 2020 acm.

关键词： Indoor positioning systems

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Demo Abstract: Perception vs. Reality - Never Believe in What You See 19

Demo Abstract: Perception vs. Reality - Never Believe in Wha...

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19th acm/IEEE International conference on Information Processing in sensor Networks (IPSN)

作者： Huang, Yunfeng Wu, Fang-Jing Hakert, Christian von der Brueggen, Georg Chen, Kuan-Hsun Chen, Jian-Jia Boecker, Patrick Chernikov, Petr Cruz, Luis Duan, Zeyi Gheith, Ahmed Gong, Yantao Gopalan, Anand Prakash, Karthik Tauqir, Ammar Wang, Yue Tu Dortmund Commun Network Inst Dortmund Germany Tu Dortmund Design Automat Embedded Syst Grp Dortmund Germany

ISBN: (纸本)9781728154978

the increasing availability of heterogeneous ambient sensing systems challenges the according information processing systems to analyse and compare a variety of different, systems in a single scenario. For instance, localization of objects can be performed by image processing systems as well as by radio based localization If such systems are utilized to localize the same objects, synergy of the outputs is important to enable comparable and meaningful analysis. this demo showcases the practical deployment, and challenges of such an example system.

关键词： data fusion localization computer vision radio perception

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Multi-Task Learning Based Blind Calibration for Low-Cost Air Quality sensor Deployments 22

Multi-Task Learning Based Blind Calibration for Low-Cost Air...

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proceedings of the 20th acm conference on embedded networked sensor systems

作者： Guodong Li Zhiyuan Wu Xinyu Liu Yue Wang Lin Zhang Tsinghua University

ISBN: (纸本)9781450398862

Air pollution problem has caught much attention globally. In addition to the national air quality monitoring stations deployed by the government, the number of low-cost air quality sensors increases rapidly as a supplement to support fine-grained monitoring. In-field calibration methods are necessary for these low-cost sensor nodes to assure the data quality. However, it is costly to collect enough reference data after deployment to train the in-field calibration model and many sensors even have no synchronized reference in the real application scenarios. To address the above challenge, we propose a multi-task learning based blind calibraiton method for air quality sensors after deployments. Our method introduces not only the reference data of the target location to formulate calibration task, but also reference measurements collected from highly accurate stations already deployed by the government in other geographical locations to formulate prediction task. To utilize the reference measurements which are not in the same location with our target sensors, e.g., in other cities, we combine the proposed calibration task and prediction task under a multi-task learning scheme. the introduced references in other locations alleviate our few-reference challenge. Furthermore, we elaborate on the choices of different tasks to have better effect of the target calibraiton task. Evaluations on the real-world collected datasets show that our proposed algorithm has better calibraiton effect.

关键词： multi-task learning air quality blind calibration

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Towards secure backscatter-based in-body sensor networks: Poster abstract 20

Towards secure backscatter-based in-body sensor networks: Po...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Voigt, thiemo Rohner, Christian Yan, Wenqing Joseph, Laya Hylamia, Sam Asan, Noor Badariah Mandal, Bappaditya Perez, Mauricio Augustine, Robin Uppsala University Sweden Universiti Teknikal Malaysia Melaka CeTRI Fkekk Malaysia

ISBN: (纸本)9781450375900

In the near future more and more people will have multiple implants to handle their diseases. the implants benefit from being connected using in-body sensor networks. We have previously shown that RF communication through human adipose (fat) tissue is feasible. In this poster, we argue why we believe that backscatter communication within this fat channel is possible. As security is of utmost importance for in-body communication, we also discuss how backscatter-based in-body networks can be secured. © 2020 acm.

关键词： Body sensor networks

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Exploring effectiveness of a predictive light control mechanism for wireless sensor networks: Poster abstract 20

Exploring effectiveness of a predictive light control mechan...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Takayama, Yuki Yokota, Yusuke Japan Women's University Graduate School of Science Tokyo Japan Japan Women's University Faculty of Science Tokyo Japan

ISBN: (纸本)9781450375900

Light management for buildings or streets is important for safety, security and comfort. However, it is not economical to keep the lights on in places without people. Although there is a lighting system with power-saving feature that lights up automatically when it detects moving objects, it has a problem that it does not light up unless the objects approach the immediate vicinity of the lights. In this work, we aim to realize the system that collects motion detection data of objects using a wireless sensor network, predicts the approach of a person from a position farther than before, and appropriately controls lighting on / off. In this system, data is exchanged between neighboring nodes, and each node autonomously judges lighting control. We discussed a predictive control method and made experiments for an adequacy confirmation. First, we constructed a system to verify the predictive control method and made a data acquisition experiment using PIR motion sensors. Next, we examined methods for a movement prediction and lighting control based on the acquired data, and confirmed that control results provide sufficient utility. © 2020 acm.

关键词： sensor nodes

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Zero-Wire: A Deterministic and Low-Latency Wireless Bus through Symbol-Synchronous Transmission of Optical Signals 20

Zero-Wire: A Deterministic and Low-Latency Wireless Bus Thro...

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18th conference on embedded networked sensor systems

作者： Oostvogels, Jonathan Yang, Fan Michiels, Sam Hughes, Danny Katholieke Univ Leuven IMEC DistriNet B-3001 Leuven Belgium

ISBN: (纸本)9781450375900

the performance dichotomy between wired and wireless networks for the Internet of things primarily arises from the inherent complexity and inefficiency of networking abstractions such as routing, medium access control and store-and-forward packet switching. this paper aims to enable a new class of latency-sensitive applications by breaking all three of these abstractions to deliver a performance envelope that resembles that of a wired bus in terms of deterministic latency and throughput. the essence of this approach is a novel networking paradigm for optical wireless communication, referred to as a symbol-synchronous bus, wherein a mesh of nodes concurrently transmit LED-based signals. this paper realises the paradigm within a platform called Zero-Wire and evaluates it on a 25-node testbed under laboratory conditions. Key end-to-end performance measurements on this physical prototype include 19 kbps of contention-agnostic goodput, interface-level latency under 1 ms for two-byte frames across four hops, jitter on the order of 10s of mu s, and a base reliability of 99%. these first results indicate a bright future for the under-explored area of optical wireless mesh networks in delivering ubiquitous connectivity through a simple and low-cost physical layer.

关键词： Internet of things wireless sensor networks cyber-physical systems visible light communication optical wireless communication concurrent transmission synchronous transmission cut-through forwarding bus networks

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Indoor Place Prediction on Smart Phones 22

Indoor Place Prediction on Smart Phones

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proceedings of the 20th acm conference on embedded networked sensor systems

作者： Pritam Sen Xiaopeng Jiang Qiong Wu Manoop Talasila Wen-Ling Hsu Cristian Borcea New Jersey Institute of Technology AT&T Research Lab

ISBN: (纸本)9781450398862

High-accuracy and low-latency indoor place prediction for mobile users is crucial to enable applications for assisted living, emergency services, smart homes, and augmented reality. Previous studies on indoor place prediction use complex infrastructure with multiple visual/wireless anchors or multiple wireless access points. these localization techniques are difficult to deploy, may negatively impact user privacy through location tracking, and their data collection is not suitable for personalized place prediction. To solve these challenges, this paper proposes GoPlaces, a novel app that fuses inertial sensor data with WiFi-RTT estimated distances to predict the future indoor places visited by a user. GoPlaces does not require any infrastructure, except for one cheap off-the-shelf WiFi access point that supports ranging with RTT. In addition, it enables personalized place naming and prediction through its on-the-phone data collection and protects users' location privacy because user's data never leaves the phone. GoPlaces uses an attention-based bidirectional long short-term memory model to detect user's current trajectory, which is then used together with historical information stored in a prediction tree to infer user's future places. We implemented GoPlaces in Android and evaluated it in several indoor spaces. the experimental results demonstrate prediction accuracy as high as 92%, low latency, and low resource consumption on the phones.

关键词： human mobility sensor fusion time series analysis indoor place prediction wifi-RTT deep learning smart phones

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Fishing activity sensing and visualization system using sensor-equipped fishing rod: Demo abstract 20

Fishing activity sensing and visualization system using sens...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Fukuda, Shuichi Choi, Hyuckjin Matsuda, Yuki Yasumoto, Keiichi Nara Institute of Science and Technology Ikoma Nara Japan

ISBN: (纸本)9781450375900

In recent years, many studies and development of Cyber Physical systems (CPS) have been carried out to feed back analysis results to human users in a physical space by using machine learning, aiming to analyze a huge amount of information obtained from physical space in a cyber space. To apply CPS to sports, a lot of studies have been conducted on sensing and recognizing actions and movements of athletes using machine learning. In this study, we focus on fishing as a sport, and propose a fishing CPS that recognizes anglers' actions in real-time and provides information on the past useful actions that are linked to fishing results depending on time and place as a decision support when the anglers do not make catch. In addition, this paper reports on the development of an IoT (Internet of things) device that acquires positional information, acceleration and gyroscope information, and a web system that displays results of real-time activity recognition along with the place and time by animation for realizing the fishing CPS. We have evaluated the developed IoT device and web system from the viewpoint of practical use. As a result, we have confirmed that the GPS and acceleration sensors, in the actual breakwater environment, were constantly transmitting data to a server via UDP communication for 4 hours and 40 minutes. © 2020 acm.

关键词： Internet of things

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LidarPhone: Acoustic eavesdropping using a lidar sensor: Poster abstract 20

LidarPhone: Acoustic eavesdropping using a lidar sensor: Pos...

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18th acm conference on embedded networked sensor systems, SenSys 2020

作者： Sami, Sriram Tan, Sean Rui Xiang Dai, Yimin Roy, Nirupam Han, Jun National University of Singapore Singapore University of Maryland College Park United States

ISBN: (纸本)9781450375900

Private conversations are an attractive target for malicious actors intending to conduct audio eavesdropping attacks. Previous works discovered unexpected vectors for these attacks, such as analyzing high-speed video of objects adjacent to sound sources, or using WiFi signal information. We propose LidarPhone, a novel side-channel attack that exploits the lidar sensors in commodity robot vacuum cleaners to perform acoustic eavesdropping attacks. LidarPhone is able to detect the minute vibrations induced on objects that are near audio sources, and extract meaningful signals from inherently noisy raw lidar returns. We evaluate a realistic scenario for potential victims: recovering privacy-sensitive digits (e.g., credit card numbers, social security numbers) emitted by computer speakers during teleconferencing calls. We implement LidarPhone on a Xiaomi Roborock vacuum cleaning robot and perform a comprehensive series of real-world experiments to determine its performance. LidarPhone achieves up to 91% accuracy for digit classification. © 2020 acm.

关键词： Security systems

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CIPhy: Causal Intervention with Physical Confounder from IoT sensor Data for Robust Occupant Information Inference 22

CIPhy: Causal Intervention with Physical Confounder from IoT...

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proceedings of the 20th acm conference on embedded networked sensor systems

作者： Zhizhang Hu Tong Yu Ruiyi Zhang Shijia Pan University of California Carnegie Mellon University Duke University

ISBN: (纸本)9781450398862

Occupant information inference with IoT sensor data enables many smart applications, such as patients'/older adults' in-home monitoring. the difficulty of collecting labeled real-world IoT sensor data often leads to reliability and scalability issues for those systems. Extensive prior works (e.g., domain adaptation) focus on the domain shift issues, i.e., the inconsistent data feature and label relationship, and dataset bias is often neglected. Dataset bias is commonly caused by limited and varied accessibility to labeled data for each class, and it is inevitable for real-world datasets. the model trained with a biased dataset fits into the bias, hence cannot further generalize to the testing data for accurate *** propose CIPhy, a causal intervention scheme with physical confounders measured from the sensor data to achieve robust occupant information inference. We model the dataset bias as a confounding problem. there exists a confounder directly impacts both data feature and label, and each class's accessibility to labeled data varies when the confounder's condition changes. the model trained with biased data learns a spurious feature-label correlation conditioned on the confounder's condition in the training data. When testing data has a different condition, i.e., confounding shift, this correlation can not be applied. By using the causal intervention, e.g., backdoor adjustment, the confounding shift's negative impact on the data-driven models can be mitigated. the CIPhy decouples the sensor data to measure the confounder, then conducts the causal intervention for a de-biased occupant information inference. We use a public dataset on occupant identification as a case study, to investigate the feasibility of applying causal intervention to resolve the dataset bias issue. From the experiment, CIPhy achieves up to 11.42% identification accuracy improvement compared to baselines given the biased training data and confounding shift.

关键词： occupant information inference dataset bias causal intervention

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