Empowering Sensors in the Internet of Things with Tiny Machine Learning
A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: 5 June 2025 | Viewed by 1894
Special Issue Editors
Interests: ML/federated learning in wireless systems; heterogeneous networks; massive MIMO; reconfigurable intelligent surface-assisted networks; mmWave communication networks; energy harvesting; full-duplex communications; cognitive radio; small cell; non-orthogonal multiple access (NOMA); physical layer security; UAV networks; visible light communication; IoT system
Special Issues, Collections and Topics in MDPI journals
Interests: cryptography; mathematical cryptography; data security; network security; mobile communications; mobile computing; wireless communications
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The aim of this special issue is to explore the potential of Tiny Machine Learning techniques in enhancing the capabilities of sensors in the context of the Internet of Things (IoT).
With the rapid growth of IoT applications, sensors play a crucial role in collecting and transmitting data. However, the limited resources of sensors often pose challenges in terms of data processing and analysis. Tiny Machine Learning offers a promising approach to address these challenges by providing lightweight machine learning algorithms and methods that can be deployed directly on sensors.
This special issue aims to bring together researchers and practitioners to discuss and showcase the latest advancements and applications of Tiny Machine Learning in empowering sensors within the IoT ecosystem. We invite original research articles, reviews, and perspectives on the following topics:
- Tiny Machine Learning algorithms for sensor data processing and analysis.
- Hardware and software platforms for implementing Tiny Machine Learning on sensors.
- Applications of Tiny Machine Learning in various IoT domains, including healthcare, environmental monitoring, smart cities, and industrial automation.
- Energy-efficient and resource-constrained machine learning models for sensors.
- Performance evaluation and benchmarking of Tiny Machine Learning techniques on sensor devices.
- Security and privacy considerations in deploying Tiny Machine Learning on sensors.
Dr. Dinh-Thuan Do
Prof. Dr. Cheng-Chi Lee
Guest Editors
Manuscript Submission Information
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Keywords
- tiny machine learning
- Internet of Things
- data processing
- data analysis
- energy efficiency
- performance evaluation
- security
- privacy considerations
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