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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31-40 订阅

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NFCapsule: An Ingestible sensor Pill for Eosinophilic Esophagitis Detection Based on Near-field Coupling 20

NFCapsule: An Ingestible Sensor Pill for Eosinophilic Esopha...

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Zhang, Junbo Balakrishnan, Gaurav Srinidhi, Sruti Bhat, Arnav Kumar, Swarun Bettinger, Christopher Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450398862

this paper presents NFCapsule, a light-weight, battery-free, and ingestible biomedical sensor that can potentially enable non-invasive detection of active eosinophilic esophagitis (EoE). EoE is an allergen-induced inflammatory condition of the esophagus;its diagnosis generally involves invasive, wired, and time-consuming endoscopy. In contrast, NFCapsule aims to wirelessly detect active EoE by tracking tissue impedance through an ingestible pill that the patient swallows. Specifically, recent biomedical research has shown that active EoE induces observable changes in the electrochemical impedance of the esophagus tissue due to an increase in its intercellular spacing. We design the NFCapsule pill based on RLC resonant circuits and model the target tissue as an impedance component that changes the resonant properties of the pill circuit. Further, the NFCapsule reader identifies the resonant properties of the pill by consistently monitoring the amount of energy transferred to the pill as it goes through the esophagus, and converts this information to estimates of bio-impedance. We implement NFCapsule pill prototypes with flexible polyimide PCBs and gelatin capsules (27 mm in height and 10 mm in diameter) and evaluated NFCapsule with both ionic agarose hydrogel models and ex vivo porcine esophageal tissues (no human patients involved). We show that NFCapsule maintains high classification accuracy under various practical scenarios (e.g., blockage, bending, movement, etc.) and achieves 85% average accuracy between healthy and unhealthy tissue samples.

关键词： near-field inductive coupling resonant circuit ingestible electronics wireless systems

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Indoor Place Prediction on Smart Phones 20

Indoor Place Prediction on Smart Phones

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Sen, Pritam Jiang, Xiaopeng Wu, Qiong Talasila, Manoop Hsu, Wen-Ling Borcea, Cristian New Jersey Inst Technol Newark NJ 07102 USA AT&T Res Lab Bedminster NJ USA

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： Indoor place prediction sensor fusion WiFi-RTT Time series analysis Deep learning Human mobility Smart phones

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CycleGAN based Unsupervised Domain Adaptation for Machine Fault Diagnosis 20

CycleGAN based Unsupervised Domain Adaptation for Machine Fa...

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Pattnaik, Naibedya Vemula, Uday Sai Kumar, Kriti Kumar, A. Anil Majumdar, Angshul Chandra, M. Girish Pal, Arpan TCS Res Embedded Devices & Intelligent Syst Bangalore Karnataka India Indian Inst Technol Madras Tamil Nadu India Indraprastha Inst Informat Technol Delhi India

ISBN: (纸本)9781450398862

Fault diagnosis plays a vital role in ensuring the normal operation of the machine and safe production. In recent years, data-driven techniques have gained a lot of popularity for machine fault diagnosis. But most of these techniques assume the training and test data have the same distribution. However, in most practical application scenarios, domain discrepancy can be observed between the training (source) and test (target) data due to different factors like changes in the operating conditions, different sensor locations, etc. Classical approaches fail to address such domain discrepancy, which leads to poor performance. the problem becomes more challenging when the target is completely unlabeled. To address this scenario, domain adaptation techniques are used to transfer the knowledge learned from the labeled source domain to the unlabeled target domain. Recently, adversarial network based domain adaptation has been extensively explored for fault diagnosis. But the adversarial loss alone does not guarantee the translation of the source to the desired target domain (class consistent). Here, we propose to use cycle-consistency loss employing 1D-CycleGAN for learning the source to target mapping for unsupervised adaptation for bearing fault diagnosis. the proposed method is evaluated for two different scenarios, with the source and target from (i) same machine but different working conditions and (ii) different but related machines. Experimental results show that while the proposed method performs comparable to the best-performing benchmark for the first case, it significantly outperforms all the state-of-the-art methods for the challenging second case.

关键词： Domain adaptation Machine fault diagnosis CycleGAN Unsupervised learning

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Algorithm for Distributed Duty Cycle Adherence in Multi-Hop RPL Networks 21

Algorithm for Distributed Duty Cycle Adherence in Multi-Hop ...

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19th acm conference on embedded networked sensor systems, SenSys 2021

作者： Van Leemput, Dries Naessens, Armand Elsas, Robbe Hoebeke, Jeroen Poorter, Eli De IDLab Department of Information Technology Ghent University - Imec Ghent Belgium

ISBN: (纸本)9781450390972

Wireless sensor Networks (WSNs) operating in unlicensed frequency bands or employing battery-less devices, require a Duty Cycle (DC) limit to ensure fair spectrum access or limit energy consumption. However, in multi-hop networks, it is up to the network protocol to ensure that all devices comply with such DC restrictions. We therefore developed a distributed DC adherence algorithm that limits the DC of all devices without introducing any additional packet overhead. this paper presents a brief description of the algorithm and evaluates its performance through simulation. Our results show that the algorithm can limit the DC of all devices to ensure no devices must switch off. Our algorithm therefore provides a solution for WSNs where nodes must operate below a DC limit. © 2021 Owner/Author.

关键词： Wireless sensor networks

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CIPhy: Causal Intervention with Physical Confounder from IoT sensor Data for Robust Occupant Information Inference 20

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

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Hu, Zhizhang Yu, Tong Zhang, Ruiyi Pan, Shijia Univ Calif Merced Merced CA 95343 USA Carnegie Mellon Univ Pittsburgh PA USA Duke Univ Durham NC USA

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 inference. We 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 Causal Intervention Dataset Bias

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Leveraging networked sensors to Improve Apple Orchard Irrigation: A Lab Prototype 19

Leveraging Networked Sensors to Improve Apple Orchard Irriga...

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19th Annual International conference on Distributed Computing in Smart systems and the Internet of things, DCOSS-IoT 2023

作者： Tumanyan, Arthur Grigoryan, Greta Raptis, theofanis P. Yerevan State University Yerevan Armenia Institute of Informatics and Telematics National Research Council Pisa Italy

ISBN: (纸本)9798350346497

Research on Internet of things (IoT) technology in agriculture has gained significant attention in recent years, as it offers the potential to revolutionise traditional farming practices by enabling more efficient resource management, increased crop yields, and reduced environmental impact. Similarly, there has been a growing interest in the application of IoT technology specifically for apple orchards;research in this respect needs to be tailored to the unique requirements and constraints of this vertical. this paper proposes a smart irrigation solution for apple orchards using networked sensors that aims to reduce watering costs and efficiently address related practical agricultural constraints. After exploring the various requirements of the apple orchards watering problem, we design a lab prototype for the networked smart irrigation system and conduct measurements to evaluate its effectiveness. As the fieldwork is not possible at the moment of the paper writing due to seasonal limitations, we perform initial, small-scale lab experiments and network emulations. Specifically, we test the effectiveness of the soil moisture sensors, and we emulate in the Cooja simulator the performance of diverse spatial topologies of the networked sensors. the results of the experiments and simulations demonstrate that our proposed approach has the potential to (i) effectively reduce water consumption (ii) align to the corresponding universally accepted soil moisture best practices, and, (ii) tune its performance according to the eventual sensor topology application requirements, by scaling accordingly the number of sensors. © 2023 IEEE.

关键词： Irrigation

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Non-acoustic speech sensing system based on flexible piezoelectric 20

Non-acoustic speech sensing system based on flexible piezoel...

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20th acm conference on embedded networked sensor systems (SenSys)

作者： Yuan, Shiji Sun, Ying Wang, Shuai Chen, Xinlei Ding, Ying Zheng, Dezhi Fan, Shangchun Beihang Univ Sch Large Aircraft Adv Training Ctr Beijing Peoples R China Beihang Univ Sch Instrumentat & Optoelect Engn Beijing Peoples R China Beihang Univ Sch Comp Sci & Engn Beijing Peoples R China Tsinghua Univ Pengcheng Lab Shenzhen Peoples R China Beihang Univ Res Inst Frontier Sci Beijing Peoples R China

ISBN: (纸本)9781450398862

Speech is one of the most important biological signals to complement human-human and human-computer interaction. Traditional speech datasets were collected by air microphones, but using these datasets in noisy environments such as factories is practically challenging. therefore, speech recognition in noisy environments poses higher requirements. the non-acoustic speech dataset plays a significant role in robust speech recognition under high background noise. Existing datasets suffered from dull sound, low intelligibility and poor recognition accuracy due to hardware and computer technology limitations. this paper presents a non-acoustic speech sensing system based on flexible piezoelectric. the system collected vibration signals from the jaws of six males and five females, and the corpus contained ten different control commands at 90 dB of background noise. the dataset is reliable with high intelligibility and capable of achieving 93.7% recognition accuracy by calculation. With the aforementioned benefits, this dataset is an essential tool for studying human-computer interaction in high-noise environments, analyzing human acoustic properties, and aiding medical rehabilitation.

关键词： speech command data non-acoustic speech flexible piezoelectric sensor

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Characterising the Role of Pre-Processing Parameters in Audio-based embedded Machine Learning 21

Characterising the Role of Pre-Processing Parameters in Audi...

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19th acm conference on embedded networked sensor systems, SenSys 2021

作者： Toussaint, Wiebke Mathur, Akhil Ding, Aaron Yi Kawsar, Fahim Delft University of Technology Delft Netherlands Nokia Bell Labs Cambridge United Kingdom

ISBN: (纸本)9781450390972

When deploying machine learning (ML) models on embedded and IoT devices, performance encompasses more than an accuracy metric: inference latency, energy consumption, and model fairness are necessary to ensure reliable performance under heterogeneous and resource-constrained operating conditions. To this end, prior research has studied model-centric approaches, such as tuning the hyperparameters of the model during training and later applying model compression techniques to tailor the model to the resource needs of an embedded device. In this paper, we take a data-centric view of embedded ML and study the role that pre-processing parameters in the data pipeline can play in balancing the various performance metrics of an embedded ML system. through an in-depth case study with audio-based keyword spotting (KWS) models, we show that pre-processing parameter tuning is a remarkable tool that model developers can adopt to trade-off between a model's accuracy, fairness, and system efficiency, as well as to make an embedded ML model resilient to unseen deployment conditions. © 2021 Owner/Author.

关键词： Machine learning

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Multi-Task Learning Based Blind Calibration for Low-Cost Air Quality sensor Deployments 20

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

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20th acm conference on embedded networked sensor systems, SenSys 2022

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

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. © 2022 Owner/Author.

关键词： Air quality

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STeC: Exploiting Spatial and Temporal Correlation for Event-based Communication in WSNs 21

STeC: Exploiting Spatial and Temporal Correlation for Event-...

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19th acm conference on embedded networked sensor systems, SenSys 2021

作者： Biri, Andreas Forno, Reto Da Gsell, Tonio Gatschet, Tobias Beutel, Jan thiele, Lothar ETH Zurich Switzerland University of Innsbruck Austria

ISBN: (纸本)9781450390972

Low-power wireless sensor networks have demonstrated their potential for the detection of rare events such as rockfalls and wildfires, where rapid reporting as well as long-term energy-efficient operation is vital. However, current systems require periodic synchronization to maintain network coordination, heavily rely on node placement or use costly long-range links to infrastructure. We present STeC, a novel wireless communication design that directly exploits the spatial and temporal correlation of signals from the sensed phenomenon to orchestrate event-based communication. We leverage the locality of a co-detection, where a physical event triggers multiple sensors quasi-simultaneously, to efficiently collect, characterize and report sensor data. this eliminates the overhead of periodic network activity and centralized control, resulting in more energy-efficient communication with a lower, more consistent detection latency. In doing so, we propose a fundamentally new approach to avoid the elementary conflict between duty cycle and latency requirements immanent to synchronous protocols by exploiting correlated sensor signals for networking. Experiments using real-world traces of a natural hazard detection application show that STeC reduces the detection latency by up to 87 % compared to standard single-hop communication and outperforms traditional schedule-based methods by up to 58.4 x in energy efficiency. © 2021 Owner/Author.

关键词： Wireless sensor networks

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