Deep Learning for the Internet of Things (IoT)
A special issue of Future Internet (ISSN 1999-5903). This special issue belongs to the section "Internet of Things".
Deadline for manuscript submissions: closed (20 March 2024) | Viewed by 1453
Special Issue Editor
Interests: social network analytics; multimedia recommender systems; big data; artificial intelligence; graph mining; IoT; deep learning
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recent advances in data-driven Artificial Intelligence, especially concerning Deep Learning (DL) models and platforms, have also had a tremendous impact in the context of the Internet of Things (IoT). By now, network-connected objects can be endowed with a certain “intelligence” and be able to make decisions autonomously in a wide variety of application contexts on the basis of the great amount of observed data. Thus, in modern IoT scenarios smart devices can be easily equipped with Deep Learning components, providing the capability of performing advanced analytics, also in a real-time way, on the collected data and supporting in a more effective and efficient way the development of IoT-based systems for a plethora of application domains such as healthcare, agriculture and farming, manufacturing, smart buildings, transportations, energy, environmental surveillance, monitoring systems, smart cities and so on.
The papers in this Special Issue will focus on state-of-the-art research and challenges in leveraging Deep Learning approaches for IoT applications. In this Special Issue, we shall solicit papers that cover numerous topics of interest that include but are not limited to:
- DL and IoT for system deployment and operation;
- DL and IoT for assisted automation;
- DL-enabled real-time IoT data analytics;
- DL- and IoT-enabled digital twin;
- Cloud/edge computing systems for IoT employing DL;
- Embedded DL for IoT;
- DL-enabled spatial-temporal IoT data fusion for intelligent decision making;
- DL for IoT application orchestration;
- DL for managing security in IoT data processing;
- DL for IoT attack detection and prevention;
- Testbed and empirical studies.
Dr. Vincenzo Moscato
Guest Editor
Manuscript Submission Information
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Keywords
- artificial intelligence
- machine learning
- deep learning
- internet of things
- data analytics
