IoT with a Soft Touch: A Modular Remote Sensing Platform for STE(A)M
Applications
- URL: http://arxiv.org/abs/2204.01439v1
- Date: Tue, 29 Mar 2022 11:41:39 GMT
- Title: IoT with a Soft Touch: A Modular Remote Sensing Platform for STE(A)M
Applications
- Authors: Jona Cappelle, Geoffrey Ottoy, Sarah Goossens, Hanne Deprez, Jarne Van
Mulders, Guus Leenders, Gilles Callebaut
- Abstract summary: This work presents a remote sensing platform, named IoT with a Soft Touch, developed to achieve two goals.
First, it aims to lower the technicality, stimulating the students to do STE(A)M.
Second, the technology is to be used in softer' applications (e.g., environmental and health care)
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Besides wide attraction in the industry, IoT is being used to advance STEM
and STEAM education across a range of education levels. This work presents a
remote sensing platform, named IoT with a Soft Touch, developed to achieve two
goals. First, it aims to lower the technicality, stimulating the students to do
STE(A)M. Second, the technology is to be used in `softer' applications (e.g.,
environmental and health care), thereby aiming to attract a more diverse set of
student profiles. Students can easily build a wireless sensing device, with a
specific application in mind. The modular design of the platform and an
intuitive graphical configurator tool allows them to tailor the device's
functionality to their needs. The sensor's data is transmitted wirelessly with
LoRaWAN. The data can be viewed and analyzed on a dashboard, or the raw data
can be extracted for further processing, e.g., as part of the school's STE(A)M
curriculum. This work elaborates on the low-power and modular design
challenges, and how the platform is used in education.
Related papers
- Leveraging Foundation Models for Zero-Shot IoT Sensing [5.319176383069102]
Deep learning models are increasingly deployed on edge Internet of Things (IoT) devices.
ZSL aims to classify data of unseen classes with the help of semantic information.
In this work, we align the IoT data embeddings with the semantic embeddings generated by an FM's text encoder for zero-shot IoT sensing.
arXiv Detail & Related papers (2024-07-29T11:16:48Z) - IoT-LM: Large Multisensory Language Models for the Internet of Things [70.74131118309967]
IoT ecosystem provides rich source of real-world modalities such as motion, thermal, geolocation, imaging, depth, sensors, and audio.
Machine learning presents a rich opportunity to automatically process IoT data at scale.
We introduce IoT-LM, an open-source large multisensory language model tailored for the IoT ecosystem.
arXiv Detail & Related papers (2024-07-13T08:20:37Z) - Convert any android device into a programmable IoT device with the help of IoT Everywhere Framework [0.0]
With the use of the IoT Everywhere framework and Origin programming language, one can convert any Android smartphone into an IoT device.
This helps students of electrical engineering to grasp the idea of programming since it provides a lot of abstraction through simple function calls.
arXiv Detail & Related papers (2024-04-14T15:28:35Z) - Federated Learning for 6G: Paradigms, Taxonomy, Recent Advances and
Insights [52.024964564408]
This paper examines the added-value of implementing Federated Learning throughout all levels of the protocol stack.
It presents important FL applications, addresses hot topics, provides valuable insights and explicits guidance for future research and developments.
Our concluding remarks aim to leverage the synergy between FL and future 6G, while highlighting FL's potential to revolutionize wireless industry.
arXiv Detail & Related papers (2023-12-07T20:39:57Z) - FedOpenHAR: Federated Multi-Task Transfer Learning for Sensor-Based
Human Activity Recognition [0.0]
This paper explores Federated Transfer Learning in a Multi-Task manner for both sensor-based human activity recognition and device position identification tasks.
The OpenHAR framework is used to train the models, which contains ten smaller datasets.
By utilizing transfer learning and training a task-specific and personalized federated model, we obtained a similar accuracy with training each client individually and higher accuracy than a fully centralized approach.
arXiv Detail & Related papers (2023-11-13T21:31:07Z) - MultiIoT: Benchmarking Machine Learning for the Internet of Things [70.74131118309967]
The next generation of machine learning systems must be adept at perceiving and interacting with the physical world.
sensory data from motion, thermal, geolocation, depth, wireless signals, video, and audio are increasingly used to model the states of physical environments.
Existing efforts are often specialized to a single sensory modality or prediction task.
This paper proposes MultiIoT, the most expansive and unified IoT benchmark to date, encompassing over 1.15 million samples from 12 modalities and 8 real-world tasks.
arXiv Detail & Related papers (2023-11-10T18:13:08Z) - Low-cost Efficient Wireless Intelligent Sensor (LEWIS) for Engineering,
Research, and Education [72.2614468437919]
The vision of smart cities equipped with sensors informing decisions has not been realized to date.
Civil engineers lack of knowledge in sensor technology.
The electrical components and computer knowledge associated with sensors are still a challenge for civil engineers.
arXiv Detail & Related papers (2023-03-23T21:49:26Z) - PyTouch: A Machine Learning Library for Touch Processing [68.32055581488557]
We present PyTouch, the first machine learning library dedicated to the processing of touch sensing signals.
PyTouch is designed to be modular, easy-to-use and provides state-of-the-art touch processing capabilities as a service.
We evaluate PyTouch on real-world data from several tactile sensors on touch processing tasks such as touch detection, slip and object pose estimations.
arXiv Detail & Related papers (2021-05-26T18:55:18Z) - MOVO: a dApp for DLT-based Smart Mobility [9.034589850863714]
MOVO is a decentralized application (dApp) for smart mobility.
It includes: (i) a module for collecting data from vehicles and smartphones sensors; (ii) a component for interacting with Distributed Ledger Technologies (DLT) and Decentralized File Storages (DFS)
We describe the main software components and provide an experimental evaluation that confirms the viability of the MOVO dApp in real mobility scenarios.
arXiv Detail & Related papers (2021-04-28T15:01:28Z) - Wireless Communications for Collaborative Federated Learning [160.82696473996566]
Internet of Things (IoT) devices may not be able to transmit their collected data to a central controller for training machine learning models.
Google's seminal FL algorithm requires all devices to be directly connected with a central controller.
This paper introduces a novel FL framework, called collaborative FL (CFL), which enables edge devices to implement FL with less reliance on a central controller.
arXiv Detail & Related papers (2020-06-03T20:00:02Z) - LIMITS: Lightweight Machine Learning for IoT Systems with Resource
Limitations [8.647853543335662]
We present the novel open source framework LIghtweight Machine learning for IoT Systems (LIMITS)
LIMITS applies a platform-in-the-loop approach explicitly considering the actual compilation toolchain of the target IoT platform.
We apply and validate LIMITS in two case studies focusing on cellular data rate prediction and radio-based vehicle classification.
arXiv Detail & Related papers (2020-01-28T06:34:35Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.