Survey in Deep Learning for IoT Applications
A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "Internet of Things (IoT) and Industrial IoT".
Deadline for manuscript submissions: closed (31 December 2022) | Viewed by 57960
Special Issue Editors
Interests: artificial intelligence; signal processing; computer vision; human–machine interfaces
Special Issues, Collections and Topics in MDPI journals
Interests: disease diagnostics using artificial intelligence methods
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
In recent years, methods and dedicated communication channels of the Internet of Things (IoT) have been developed to detect and collect all kinds of information to deliver a variety of advanced services and applications, generating huge amounts of data, constantly received from millions of IoT sensors deployed around the world. The techniques behind deep learning now play an important role in desktop and mobile applications and are now entering the resource-constrained IoT sector, enabling the development of more advanced IoT applications, with proven results in a variety of areas already, including image recognition, medical data analysis, information retrieval, language recognition, natural language processing, indoor location, autonomous vehicles, smart cities, sustainability, pollution, bioeconomy, etc. This Special Issue focuses on the research and application of the Internet of Things, focusing on multimodal signal processing, sensor extraction, data visualization and understanding, and other related topics, answering the question of which deep neural network structures can efficiently process and integrate multimodal sensor input data for various IoT applications, how to adapt current and develop new designs to help to reduce the resource cost of running deep learning models for the efficient deployment on IoT devices, how to correctly calculate reliability measurements in deep learning predictions for IoT applications within limited and constrained calculation requirements, how to reduce the use of labeled IoT for needs linked to learning signal data considering operational limitations and other key areas.
Dr. Rytis Maskeliunas
Prof. Dr. Robertas Damaševičius
Guest Editors
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Keywords
- Internet of Things
- Deep learning
- Data fusion
- Multimodal signal processing
- Data processing and visualization
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