WinOSens
Ultra-low-power and ultra-low-performance asset localization to increase efficiency and transparency in industrial logistics processes with the help of machine learning in embedded sensor systems
| Contact | Prof. Dr. rer. nat. Volker Turau |
| Staff | Dr. Marcus Venzke |
| Start | 1. June 2021 |
| Financing | BMBF |
Project Description
WinOSens aims to research and develop ways to combine microcontroller-based sensor modules with machine learning for object localization and self-awareness. Simultaneously, the data read by the sensor nodes and the localization information are transferred to the cloud backend for further processing using Artificial Intelligence. The targeted use-cases include inventory tracking in in-house logistic systems and facilitating Enterprise Resource Planning (ERP) systems.
In this project, The Institute of Telematics primarily focuses on researching and developing machine learning models for ultra-low power, ultra-low-performance devices such as microcontrollers.
Project Partners
Publications
Nisal Hemadasa, Marcus Venzke, Volker Turau and Yanqiu Huang. Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments. In Proceedings of OkIP International Conference on Automated and Intelligent Systems, CAIS 2023, OkIP Books, October 2023. Oklahoma, USA.
@InProceedings{Telematik_cais_2023,
author = {Nisal Hemadasa and Marcus Venzke and Volker Turau and Yanqiu Huang},
title = {Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments},
booktitle = {Proceedings of OkIP International Conference on Automated and Intelligent Systems, CAIS 2023},
pages = ,
publisher = {OkIP Books},
day = {2-5},
month = oct,
year = 2023,
location = {Oklahoma, USA},
}
Abstract:
Indoor Positioning Systems (IPS) gained importance in many industrial applications. State-of-the-art solutions heavily rely on external infrastructures and are subject to potential privacy compromises, external information requirements, and assumptions, that make it unfavorable for environments demanding privacy and prolonged functionality. In certain environments deploying supplementary infrastructures for indoor positioning could be infeasible and expensive. Recent developments in machine learning (ML) offer solutions to address these limitations relying only on the data from onboard sensors of IoT devices. However, it is unclear which model fits best considering the resource constraints of IoT devices. This paper presents a machine learning-based indoor positioning system, using motion and ambient sensors, to localize a moving entity in privacy concerned factory environments. The problem is formulated as a multivariate time series classification (MTSC) and a comparative analysis of different machine learning models is conducted in order to address it. We introduce a novel time series dataset emulating the assembly lines of a factory. This dataset is utilized to assess and compare the selected models in terms of accuracy, memory footprint and inference speed. The results illustrate that all evaluated models can achieve accuracies above 80 %. CNN-1D shows the most balanced performance, followed by MLP. DT was found to have the lowest memory footprint and inference latency, indicating its potential for a deployment in real-world scenarios.
Nisal Manikku Badu, Marcus Venzke, Volker Turau and Yanqiu Huang. Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments. Technical Report Report arXiv:2308.11670, arXiv.org e-Print Archive - Computing Research Repository (CoRR), Cornell University, August 2023.
@TechReport{Telematik_arxiv_2023,
author = {Nisal Manikku Badu and Marcus Venzke and Volker Turau and Yanqiu Huang},
title = {Machine Learning-based Positioning using Multivariate Time Series Classification for Factory Environments},
number = {Report arXiv:2308.11670},
institution = {arXiv.org e-Print Archive - Computing Research Repository (CoRR)},
address = {Cornell University},
month = aug,
year = 2023,
}
Abstract:
Indoor Positioning Systems (IPS) gained importance in many industrial applications. State-of-the-art solutions heavily rely on external infrastructures and are subject to potential privacy compromises, external information requirements, and assumptions, that make it unfavorable for environments demanding privacy and prolonged functionality. In certain environments deploying supplementary infrastructures for indoor positioning could be infeasible and expensive. Recent developments in machine learning (ML) offer solutions to address these limitations relying only on the data from onboard sensors of IoT devices. However, it is unclear which model fits best considering the resource constraints of IoT devices. This paper presents a machine learning-based indoor positioning system, using motion and ambient sensors, to localize a moving entity in privacy concerned factory environments. The problem is formulated as a multivariate time series classification (MTSC) and a comparative analysis of different machine learning models is conducted in order to address it. We introduce a novel time series dataset emulating the assembly lines of a factory. This dataset is utilized to assess and compare the selected models in terms of accuracy, memory footprint and inference speed. The results illustrate that all evaluated models can achieve accuracies above 80 %. CNN-1D shows the most balanced performance, followed by MLP. DT was found to have the lowest memory footprint and inference latency, indicating its potential for a deployment in real-world scenarios.