Proceedings of the International Conference on Internet-of-Things Design and Implementation最新文献_第2页

When Video meets Inertial Sensors: Zero-shot Domain Adaptation for Finger Motion Analytics with Inertial Sensors 当视频遇到惯性传感器:用惯性传感器进行手指运动分析的零射击域自适应

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453537

Yilin Liu, Shijia Zhang, Mahanth K. Gowda

{"title":"When Video meets Inertial Sensors: Zero-shot Domain Adaptation for Finger Motion Analytics with Inertial Sensors","authors":"Yilin Liu, Shijia Zhang, Mahanth K. Gowda","doi":"10.1145/3450268.3453537","DOIUrl":"https://doi.org/10.1145/3450268.3453537","url":null,"abstract":"Ubiquitous finger motion tracking enables a number of exciting applications in augmented reality, sports analytics, rehabilitation-healthcare etc. While finger motion tracking with cameras is very mature, largely due to availability of massive training datasets, there is a dearth of training data for developing robust machine learning (ML) models for wearable IoT devices with Inertial Measurement Unit (IMU) sensors. Towards addressing this problem, this paper presents ZeroNet, a system that shows the feasibility of developing ML models for IMU sensors with zero training overhead. ZeroNet harvests training data from publicly available videos for performing inferences on IMU. The difference in data among video and IMU domains introduces a number of challenges due to differences in sensor-camera coordinate systems, body sizes of users, speed/orientation changes during gesturing, sensor position variations etc. ZeroNet addresses these challenges by systematically extracting motion data from videos and transforming them into acceleration and orientation information measured by IMU sensors. Furthermore, data-augmentation techniques are exploited that create synthetic variations in the harvested training data to enhance the generalizability and robustness of the ML models to user diversity. Evaluation with 10 users demonstrates a top-1 accuracy of 82.4% and a top-3 accuracy of 94.8% for recognition of 50 finger gestures thus indicating promise. While we have only scratched the surface, we outline a number of interesting possibilities for extending this work in the cross-disciplinary areas of computer vision, machine learning, and wearable IoT for enabling novel applications in finger motion tracking.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122088941","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 30

Third Party Authorization of LwM2M Clients: Poster Abstract LwM2M客户端的第三方授权:海报摘要

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453512

Leandro Lanzieri, Peter Kietzmann, T. Schmidt, Matthias Wählisch

引用次数: 1

SmartParcels: A What-If Analysis and Planning Tool for IoT-Enabled Smart Communities: Demo Abstract 智能包裹:物联网智能社区的假设分析和规划工具:演示摘要

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453514

Tung-Chun Chang, Georgios Bouloukakis, C. Hsieh, Cheng-Hsin Hsu, N. Venkatasubramanian

引用次数: 0

A Transfer Learning Framework for IoT-enabled Environments 面向物联网环境的迁移学习框架

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3458855

Amin Andjomshoaa, E. Curry

引用次数: 1

Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT: Demo Abstract 物联网嵌入式系统异常检测模型的移植与执行:演示摘要

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453513

B. Sudharsan, Pankesh Patel, Abdul Wahid, Muhammad Yahya, J. Breslin, M. Ali

{"title":"Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT: Demo Abstract","authors":"B. Sudharsan, Pankesh Patel, Abdul Wahid, Muhammad Yahya, J. Breslin, M. Ali","doi":"10.1145/3450268.3453513","DOIUrl":"https://doi.org/10.1145/3450268.3453513","url":null,"abstract":"In the Industry 4.0 era, Microcontrollers (MCUs) based tiny embedded sensor systems have become the sensing paradigm to interact with the physical world. In 2020, 25.6 billion MCUs were shipped, and over 250 billion MCUs are already operating in the wild. Such low-power, low-cost MCUs are being used as the brain to control diverse applications and soon will become the global digital nervous system. In an Industrial IoT setup, such tiny MCU-based embedded systems are equipped with anomaly detection models and mounted on production plant machines for monitoring the machine's health/condition. These models process the machine's health data (from temperature, RPM, vibration sensors) and raise timely alerts when it predicts/detects data patterns that show deviations from the normal operation state. In this demo, we train One Class Support Vector Machines (OC-SVM) based anomaly detection models and port the trained models to their MCU executable versions. We then deploy and execute the ported models on 4 popular MCUs and report their on-board inference performance along with their memory (Flash and SRAM) consumption. The steps/procedure that we show in the demo is generic, and the viewers can use it to efficiently port a wide variety of datasets-trained classifiers and execute them on different resource-constrained MCU and small CPU-based devices.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121144594","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6

Accurate and Fast Detection of Tag Antenna Damage for RFID Sensing: Poster Abstract RFID传感中标签天线损伤的准确快速检测:海报摘要

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453515

Yinan Zhu, Chunhui Duan, Xuan Ding

引用次数: 0

Configuration Tuning for Distributed IoT Message Systems Using Deep Reinforcement Learning: Poster Abstract 使用深度强化学习的分布式物联网消息系统配置调优:海报摘要

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453517

Zhuangwei Kang, Yogesh D. Barve, S. Bao, A. Dubey, A. Gokhale

{"title":"Configuration Tuning for Distributed IoT Message Systems Using Deep Reinforcement Learning: Poster Abstract","authors":"Zhuangwei Kang, Yogesh D. Barve, S. Bao, A. Dubey, A. Gokhale","doi":"10.1145/3450268.3453517","DOIUrl":"https://doi.org/10.1145/3450268.3453517","url":null,"abstract":"Distributed messaging systems (DMSs) are often equipped with a large number of configurable parameters that enable users to define application run-time behaviors and information dissemination rules. However, the resulting high-dimensional configuration space makes it difficult for users to determine the best configuration that can maximize application QoS under a variety of operational conditions. This poster introduces a novel, automatic knob tuning framework called DMSConfig. DMSConfig explores the configuration space by interacting with a data-driven environment prediction model(a DMS simulator), which eliminates the prohibitive cost of conducting online interactions with the production environment. DMSConfig employs the deep deterministic policy gradient (DDPG) method and a custom reward mechanism to learn and make configuration decisions based on predicted DMS states and performance. Our initial experimental results, conducted on a single-broker Kafka cluster, show that DMSConfig significantly outperforms the default configuration and has better adaptability to CPU and bandwidth-limited environments. We also confirm that DMSConfig produces fewer violations of latency constraints than three prevalent parameter tuning tools.","PeriodicalId":130134,"journal":{"name":"Proceedings of the International Conference on Internet-of-Things Design and Implementation","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130575612","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Enabling a Flexible and Sustainable Smart Campus Networking Backbone Using LoRaWAN: Poster Abstract 利用LoRaWAN实现灵活和可持续的智能校园网络骨干网:海报摘要

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453506

Cheng-En Yu, Chao Wang

引用次数: 1

Online Energy Management under Uncertainty for Net-Zero Energy Ecosystems 净零能耗生态系统不确定性下的在线能源管理

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453516

Chunqiu Xia, Wei Li, XIAOMIN CHANG, Albert Y. Zomaya

引用次数: 1

Rim: Offloading Inference to the Edge 边缘:卸载边缘推理

Proceedings of the International Conference on Internet-of-Things Design and Implementation Pub Date : 2021-05-18 DOI: 10.1145/3450268.3453521

Yitao Hu, Weiwu Pang, Xiaochen Liu, Rajrup Ghosh, Bongjun Ko, Wei-Han Lee, R. Govindan

引用次数: 11