Machine Learning for embedded systems
| Contact | Prof. Dr. rer. nat. Volker Turau |
| Staff | Dr. Marcus Venzke |
Project Description
After decades of intensive research, Machine Learning (ML) is increasingly finding its way into real-world applications. This was driven by the progress of computing systems with enormous computing power and the availability of large amounts of data. ML has enormous potential to enhance the performance of devices and machines in a wide variety of application areas. In the next five to ten years, ML will also find its way into embedded systems. The institute thus focusses its ML research on embedded systems.
The work is part of the MLE initiative of the TUHH. This initiative concentrates the university's competencies in the field of machine learning with the aim of transferring knowledge to business and industry. Professor Turau is the speaker of the initiative.
Subprojects
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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
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.
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.
Florian Meyer and Volker Turau. QMA: A Resource-efficient, Q-learning-based Multiple Access Scheme for the IIoT. In 2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS), IEEE, October 2021, pp. 864–874. Washington DC, USA / Virtually.
@InProceedings{Telematik_icdcs_2021,
author = {Florian Meyer and Volker Turau},
title = {QMA: A Resource-efficient, Q-learning-based Multiple Access Scheme for the IIoT},
booktitle = {2021 IEEE 41st International Conference on Distributed Computing Systems (ICDCS)},
pages = {864-874},
publisher = {IEEE},
day = {7-10},
month = oct,
year = 2021,
location = {Washington DC, USA / Virtually},
}
Abstract:
Many MAC protocols for the Industrial Internet of Things, such as IEEE 802.15.4 and its extensions, require contention-based channel access for management traffic, e.g., for slot (de)allocations and broadcasts. In many cases, subtle but hidden patterns characterize this secondary traffic, but present contention-based protocols are unaware of these patterns and therefore cannot exploit them. Especially in dense networks, these protocols often do not provide sufficient throughput and reliability for primary traffic, i.e., they cannot allocate transmission slots in time. In this paper, we propose QMA, a contention-based multiple access scheme based on Q-learning. It dynamically adjusts transmission times to avoid collisions by learning patterns in contention-based traffic. We show that QMA solves the hidden node problem without the overhead for RTS/CTS messages and, for example, increases throughput from 10 packets/s to 50 packets/s in a hidden three-node scenario without sacrificing reliability. Additionally, QMA's scalability is evaluated in a realistic scenario for slot (de)allocation in IEEE 802.15.4 DSME, where it achieves up to twice more slot (de)allocations per second.
Marcus Venzke, Daniel Klisch, Philipp Kubik, Asad Ali, Jesper Dell Missier and Volker Turau. Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems. Technical Report Report arXiv:2012.08403, arXiv.org e-Print Archive - Computing Research Repository (CoRR), Cornell University, December 2020.
@TechReport{Telematik_Venzke_ANNsES,
author = {Marcus Venzke and Daniel Klisch and Philipp Kubik and Asad Ali and Jesper Dell Missier and Volker Turau},
title = {Artificial Neural Networks for Sensor Data Classification on Small Embedded Systems},
number = {Report arXiv:2012.08403},
institution = {arXiv.org e-Print Archive - Computing Research Repository (CoRR)},
address = {Cornell University},
month = dec,
year = 2020,
}
Abstract:
In this paper we investigate the usage of machine learning for interpreting measured sensor values in sensor modules. In particular we analyze the potential of artificial neural networks (ANNs) on low-cost microcontrollers with a few kilobytes of memory to semantically enrich data captured by sensors. The focus is on classifying temporal data series with a high level of reliability. Design and implementation of ANNs are analyzed considering Feed Forward Neural Networks (FFNNs) and Recurrent Neural Networks (RNNs). We validate the developed ANNs in a case study of optical hand gesture recognition on an 8-bit microcontroller. The best reliability was found for an FFNN with two layers and 1493 parameters requiring an execution time of 36 ms. We propose a workflow to develop ANNs for embedded devices.
Florian Meyer and Volker Turau. Towards Delay-Minimal Scheduling through Reinforcement Learning in IEEE 802.15.4 DSME. In Proceedings of the First GI/ITG KuVS Fachgespräche Machine Learning and Networking, February 2020. München, Germany.
@InProceedings{Telematik_meyer_FGMLVS,
author = {Florian Meyer and Volker Turau},
title = {Towards Delay-Minimal Scheduling through Reinforcement Learning in IEEE 802.15.4 DSME},
booktitle = {Proceedings of the First GI/ITG KuVS Fachgespr{\"a}che Machine Learning and Networking},
pages = ,
publisher = {},
day = {20-21},
month = feb,
year = 2020,
location = {M{\"u}nchen, Germany},
}
Abstract:
The rise of wireless sensor networks (WSNs) in industrial applications imposes novel demands on existing wire- less protocols. The deterministic and synchronous multi-channel extension (DSME) is a recent amendment to the IEEE 802.15.4 standard, which aims for highly reliable, deterministic traffic in these industrial environments. It offers TDMA-based channel access, where slots are allocated in a distributed manner. In this work, we propose a novel scheduling algorithm for DSME which minimizes the delay in time-critical applications by employing reinforcement learning (RL) on deep neural networks (DNN).