AI and Big Data in Internet of Things: Collection, Management and Analysis
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 31 March 2026 | Viewed by 32
Special Issue Editors
Interests: web design and development; application development; networks and security; systems programming; cloud and edge computing; big data technologies; big data analytics; artificial intelligence; automation; robotics; 3D graphics design
Special Issues, Collections and Topics in MDPI journals
Interests: algorithms for cloud computing; big data; wireless communication
Special Issues, Collections and Topics in MDPI journals
Interests: computer networks; wireless and mobile communications; multimedia transmission over 5G networks; cloud computing (CC); big data analytics (BDA); wireless sensor network (WSN); Internet of Things (IoT); artificial intelligence (AI); machine learning (ML); cyber security; cryptography; privacy and security software testing
Special Issues, Collections and Topics in MDPI journals
Interests: digital signals and systems; 6G-enabled-ubiquitous big data/AI-IoT/clouds and communications; digital media communications; media coding/synchronization; transport over a variety of networks
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
The world has changed. New technologies, trends, protocols, efficient algorithms, and systems have been widely adopted by developed countries to enable better and more convenient living. As applications based on the “Internet of Things” (IoT) have been widespread in every sector, many studies have provided useful information about new trends regarding the IoT, the issues that they cause, some solutions that already exist, and various challenges that need to be overcome in order to benefit from Internet technology.
Moreover, “Artificial Intelligence” (AI) is a technological advancement providing solutions to various issues. Such solutions are based on the efficient preprocessing and analysis of Big Data, although they can involve algorithms with high complexity.
This Special Issue aims to compile high-quality review papers on applied informatics. We encourage contributions from researchers in various disciplines within the journal’s scope that highlight the latest developments in AI, the IoT, and Big Data in their field. Alternatively, you may invite relevant experts and colleagues to do so.
The topics of interest for this Special Issue include, but are not limited to, the following:
- Big Data delivery in the Internet of Artificially Intelligent Things;
- Big Data management and analytics for various applications;
- AI and Machine Learning applications;
- AI in the IoT for sustainable Big Data management;
- AI-driven UAV communications for time-critical IoT applications;
- AI and ML for Autonomous Vehicles;
- Efficient data delivery and analytics in various applications;
- Intelligent Decision Systems for the IoT.
Dr. Andreas P. Plageras
Dr. Christos L. Stergiou
Dr. Vasileios Memos
Prof. Dr. Konstantinos E. Psannis
Guest Editors
Manuscript Submission Information
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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
Keywords
- AI-IoT
- algorithms
- analytics
- applications
- big data
- machine learning
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