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

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proceedings - 2021 IEEE 19th International conference on embedded and Ubiquitous Computing, EUC 2021

Proceedings - 2021 IEEE 19th International Conference on Emb...

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19th IEEE International conference on embedded and Ubiquitous Computing, EUC 2021

ISBN: (纸本)9781665400367

the proceedings contain 28 papers. the topics discussed include: dynamically interchangeable framework for component behavior of embedded component systems;VFAT: a personalized HAR scheme through exploiting virtual feature adaptation based on transfer learning;private estimation of symptom distribution for infectious disease analysis in edge computing;multi-agent multi-armed bandit learning for offloading delay minimization in V2X networks;a blockchain-based fast authentication and collaborative video data forwarding scheme for vehicular networks;PSSBP: a privacy-preserving scope-query searchable encryption scheme based on blockchain for parking lots sharing in vehicular networks;coding observer nodes for sybil attacks detection in mobile wireless sensor networks;a framework to optimize deep learning based web attack detection using attacker categorization;securing remote policy enforcement by a multi-enclave based attestation architecture;an adaptive edge-based steganography algorithm for hiding text into images;and a systematic evaluation of android anti-malware tools for detection of contemporary malware.

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Wireless Identification and Sensing Platform Version 6.0 22

Wireless Identification and Sensing Platform Version 6.0

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

作者： Rohan Menon Rohit Gujarathi Ali Saffari Joshua R. Smith University of Washington

ISBN: (纸本)9781450398862

Connected devices are becoming more ubiquitous, but powering them remains a challenge. the Wireless Identification and Sensing Platform (WISP) is a fully programmable device capable of energy harvesting and backscatter communication. It can accommodate a variety of sensing modalities and operate without batteries or a wired power supply, making it a suitable device for ubiquitous computing. A new version of WISP is presented. WISP-6.0 is designed to be low-power, modular, and enable dual energy harvesting from sources like a solar panel. Additionally, an upgraded cross-platform host application is built using the latest web technologies. Compared to its predecessor, WISP-5.1, WISP-6.0 consumes 13.62% and 6.29% less power in active accelerometer and active acknowledgment modes respectively. Furthermore, WISP-6.0 is better able to harvest RF energy collected from its antenna, with the greatest improvements at higher input powers.

关键词： sensor platforms battery-free camera battery-free microphone wireless sensor networks battery-free sensor energy harvesting WISP internet of things

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Fusing Computer Vision and Wireless Signal for Accurate sensor Localization in AR View 22

Fusing Computer Vision and Wireless Signal for Accurate Sens...

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

作者： Md Fazlay Rabbi Masum Billah Md Mofijul Islam Nurani Saoda Tariq Iqbal Bradford Campbell University of Virginia

ISBN: (纸本)9781450398862

Recent years have seen increasing traction to enable new applications that can localize sensors on the screen of an Augmented Reality (AR) device (e.g. smartphone, tablet) so that sensors can be controlled more intuitively. Despite recent advances in this area, both wireless signal dependent and computer vision based localization solutions have seen a slow acceptance due to signal noise, multipath effect, and limited AR device-sensor interactivity. In this paper, we propose a novel solution to combine the complementary advantages of wireless signal based localization solution with the computer vision based solution to track IoT devices and sensors more accurately. Experimental result shows that our system can accurately track IoT devices with an average pixel error of 34 pixels in a 1024 × 768 pixels image, which is a 75.8% improvement from the state-of-the-art model.

关键词： sensor localization computer vision augmented reality BLE

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DIPS: Debug Intermittently-Powered systems Like Any embedded System 22

DIPS: Debug Intermittently-Powered Systems Like Any Embedded...

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

作者： Jasper de Winkel Tom Hoefnagel Boris Blokland Przemysław Pawełczak Delft University of Technology Delft The Netherlands

ISBN: (纸本)9781450398862

Debugging and testing battery-free intermittently-powered systems is notoriously difficult. this is not only due to the additional complexity of maintaining state through power failures but also due to the lack of proper tools to test and debug these systems. As a solution, we present DIPS: a fully-featured hardware debugger for battery-free intermittently-powered systems capable of automatically verifying memory and peripheral state between power failures. Our solution seamlessly integrates an emulator allowing for emulation of any power scenario to the device under test. this allows our debugger to pause emulation and program execution when debugging or when state restoration issues are detected. Our new system is built around GNU Debugger (GDB): a widely-used debugging tool. therefore, DIPS allows for a debugging process identical to state-of-the-art debuggers for continuously-powered devices. User studies found that our debugger is easy and intuitive to use. It allows embedded system developers to find bugs quicker in code written for battery-free devices. With our debugger we found unseen errors in state-of-the-art software frameworks for intermittently-powered systems.

关键词： emulation debugging intermittent systems software testing

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Towards the Plant-Based sensor: Agricultural Energy Harvesting and Sensing Experiments 22

Towards the Plant-Based Sensor: Agricultural Energy Harvesti...

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

作者： Tyler Luong Stewart J. thomas Bucknell University

ISBN: (纸本)9781450398862

With an ongoing search for alternative energy sources, plants may serve as a viable means for operating low-power digital circuits. Biopotential, the electrical signals found within the body due to electrochemical activity, can be monitored and harnessed within plants and vegetables. Measuring plant biopotential, in combination with electrogenic microbial fuel cell (MFC) technology [14], suggests that a self-sufficient and micro-power generation system can be made using a microbial fuel cell in tandem with a living plant [18]. this research presents ongoing work in proposed sub-systems within this power generation system and their compatibility with low-power electronics, a project inspired from works focused on converting plants into batteries [7, 8]. the power harvesting aspect of the project consists of long deployment experiments with mud based MFCs using MudWatt kits, as well as a plant based MFC. the plant sensing aspect of the project is depending on thigmonastic reactions from the plant specie Mimosa pudica.

关键词： bioelectricity energy harvesting plant monitoring

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Intelligent Continuous Monitoring to Handle Data Distributional Changes for IoT systems 22

Intelligent Continuous Monitoring to Handle Data Distributio...

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

作者： Soma Bandyopadhyay Anish Datta Arpan Pal Srinivas Raghu Raman Gadepally TCS Research Kolkata India TCS Hyderabad India

ISBN: (纸本)9781450398862

Intelligent continuous monitoring of an IoT system to identify the operational changes, encompassing both normal and abnormal scenarios, with drift in sensing device is a challenging problem. It demands capability of learning continuously with multiple interventions or shifts, without forgetting past events information. However, forgetting the past learned knowledge, known as catastrophic forgetting, impacts significantly on the performance of continuous monitoring. In this work, we propose a generative neural network based model to handle various operational changes. Here, one objective is to learn continually by capturing past data distributional knowledge, while adapting new data signatures. Other objective, is to handle changes in data distribution like, to identify drifts, as well as, variations in diverse operational conditions of the system. We have experimented using vibration sensor based public real-world bearing data and performed extensive analysis incorporating synthetic drifts. Proposed method outperforms existing benchmark performances with clear separation of drift in learned representation.

关键词： sensor drift continual learning machine vibration

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ElasticAI-Creator: Optimizing Neural Networks for Time-Series-Analysis for on-Device Machine Learning in IoT systems 22

ElasticAI-Creator: Optimizing Neural Networks for Time-Serie...

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

作者： Chao Qian Lukas Einhaus Gregor Schiele University of Duisburg-Essen Duisburg Germany

ISBN: (纸本)9781450398862

Deep learning (DL) is quickly becoming a core technology to process sensor data from IoT devices. Nowadays, data is usually sent to remote Cloud services where GPU-based ML platforms process it. In contrast, our vision is to have IoT devices with local, on-device DL. this can increase privacy and accessibility as well as reduce processing costs. To do so, specialized hardware accelerators are needed to perform ML operations efficiently without high performance CPUs. In this paper we introduce our approach for a generator for ML hardware accelerators in the IoT. We focus specifically on the inference of ML algorithms for a typical class of IoT use cases, i.e., processing time-series data under real-time constraints. Additionally our optimization techniques rely on the co-design of hardware and ML algorithms to explore the most specialized and efficient corners of the design space. In this work we analyze the requirements for our generator, discuss optimization stages and techniques and show a case study based on an LSTM model for traffic flow prediction.

关键词： code generator energy-efficiency machine learning embedded FPGA IoT

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CASPER: Criticality-Aware Self-Powered Wireless in-vivo Sensing Edge for Precision Animal Agriculture 22

CASPER: Criticality-Aware Self-Powered Wireless in-vivo Sens...

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

作者： Upinder Kaur Richard M. Voyles Purdue University

ISBN: (纸本)9781450398862

the promise of individualized care for improving animal welfare demands real-time continuous monitoring of animals. While technology has helped crop agriculture realize the goals of precision care, animal agriculture is still lacking domain-adapted technology. In this work, we present a novel Criticality-Aware Self-Powered in-vivo sensing Edge for pRecision animal agriculture, CASPER. Enabling real-time monitoring of a suite of biomarkers while scavenging power from both thermal and physiological sources, CASPER promises unprecedented adaptability, range, and life-cycle for such an edge node. Field deployments show that CASPER generates 30mW of power with a surplus of 9.08mW, during the ultra-low power mode. the criticality-aware control is proven to capture deviation in trends of biomarker activity that would be missed in fixed-interval transmission. Hence proving the validity and effectiveness of CASPER.

关键词： precision animal agriculture sensor self-powered IoT wireless edge

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Convolutional Compressed Sensing for Smartphone Acceleration Data Compression 22

Convolutional Compressed Sensing for Smartphone Acceleration...

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

作者： Liqiang Xu Yuuki Nishiyama Masamichi Shimosaka Kota Tsubouchi Kaoru Sezaki The University of Tokyo Tokyo Institute of Technology Yahoo Japan Corporation

ISBN: (纸本)9781450398862

As intelligent sensing and smartphone technologies have progressed, a huge amount of highly heterogeneous data have come to be stored in smartphones and uploaded to servers for analysis on a daily basis. this has led to vast storage overheads for users and companies. Hence, data compression becomes the most efficient strategy for suppressing the increase in storage overhead. Compressed sensing (CS) technology is one approach to compressing data, but traditional CS-based algorithms are significantly time-consuming and have low reconstruction performance. In light of these drawbacks, this paper proposes a compressed sensing framework that instead takes advantage of the low time cost and adaptive learning capability of deep learning methods, wherein a convolutional neural network (CNN) is used for compressing and reconstructing acceleration data. Our experiments with actual smartphone acceleration data show that the proposed method dramatically improves the reconstruction performance with very little reconstruction time compared with traditional compressed sensing methods.

关键词： acceleration data smartphone sensor convolutional neural network compressed sensing

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A toolkit for spatial interpolation and sensor placement: Poster abstract 20

A toolkit for spatial interpolation and sensor placement: Po...

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

作者： Narayanan, S Deepak Patel, Zeel B Agnihotri, Apoorv Batra, Nipun Iit Gandhinagar India

ISBN: (纸本)9781450375900

Sensing is central to the SenSys and related communities. However, fine-grained spatial sensing remains a challenge despite recent advancements, owing to cost, maintenance, among other factors. thus, estimating the sensed phenomenon at unmonitored locations and strategically installing sensors is of prime importance. In this work, we introduce Polire - an open-source tool that provides a suite of algorithms for spatial interpolation and near-field passive sensor placements. We replicate two existing papers on these two tasks to show the efficacy of Polire. We believe that Polire is an essential step towards lowering entry barriers towards sensing and scientific reproducibility. © 2020 acm.

关键词： embedded systems

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