Future Edge and Tiny Machine Learning
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (31 August 2022) | Viewed by 380
Special Issue Editor
Interests: AI in healthcare; deep learning for patient monitoring; wearable devices
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Edge computing has transformed modern businesses by shifting data processing from centralized data centers to the periphery of the network, as close as possible to where data are produced. In this context, Edge machine learning offers on-site understanding and real-time inference of data captured from nearby sensors and IoT devices. This usually takes place on dedicated computing device or PC. Embedded machine learning, also known as Tiny ML, further pushes ML tasks onto the sensors themselves, performing on-device analytics at or below the mW power range and targeting predominately battery-operated embedded devices. Common sensing modalities include vision, audio, speech, motion, chemical, physical, textual, cognitive and many more.
With such concepts, network traffic and time delays are reduced, and data privacy is conserved, since any potentially sensitive information is processed locally. The boundaries between Edge ML and Tiny ML overlap, with both domains dealing with constraints in computing power, energy consumption, real-time capabilities, drifting data, network availability, context understanding from other data sources and more.
The aim of this Special Issue is to highlight the most recent innovation in Edge and Tiny ML.
The topics of this Special Issue include, but are not limited to:
- Low power wireless smart sensor networks;
- Federated learning for Edge and Tiny ML;
- Reducing energy and network bandwidth consumption;
- Efficient training methods for Edge and Tiny ML;
- ML model reduction techniques and architectures;
- Benchmarking tools for Edge and Tiny ML;
- Data and network management for Edge and Tiny ML;
- Smart sensors in the industry (e.g. visual inspection);
- Interaction with everyday objects at home using Edge and Tiny ML;
- Wearable health sensors.
Dr. Matthew Pediaditis
Guest Editor
Manuscript Submission Information
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Keywords
- Machine learning
- Edge ML
- Tiny ML
- Embedded machine learning
- Energy-efficient ML
- Model distillation
- Smart sensors
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