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Artificial Intelligence of Things for Real-Time Data Monitoring and Processing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Internet of Things".

Deadline for manuscript submissions: closed (31 October 2021) | Viewed by 3374

Special Issue Editors


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Guest Editor
Internet and Data Lab (IDLab), University of Antwerp – imec, 2000 Antwerp, Belgium
Interests: protocols for wireless networks; performance modeling; wireless sensor networks; IoT

E-Mail Website
Guest Editor
Internet and Data Lab (IDLab), University of Antwerp – imec, 2000 Antwerp, Belgium
Interests: wireless network optimization; sensor networks; IoT
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
i2CAT Foundation, 08034 Barcelona, Spain
Interests: IoT; low-power wide-area networks; WSN; Artificial Intelligence of Things; AI
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Internet of Things (IoT) is transforming the way we interact with our devices at home, at work, and throughout our cities. The number of IoT devices that continuously gather data from our environment is exponentially increasing, and so is the amount of data obtained. The potential value of the data is proportional to the capacity of analyzing it and making rapid decisions. Therefore, new artificial intelligence (AI) techniques are needed to intelligently let the devices learn, reason, and process the information without human interaction. In this way, the devices become smart, being able to take actions and/or make decisions in real time on their own, as humans do. Artificial Intelligence of Things (AIoT) is a promising paradigm that enables the use of data analytics to optimize a system and generate higher performance by making better decisions in the device itself without using the cloud. In this way, less data needs to be transferred to the cloud and decisions can be made faster. However, limited resources and IoT device capabilities pose significant challenges in supporting AI.

This Special Issue solicits high-quality unpublished work on recent advances in real-time data monitoring and processing using lightweight AI on the IoT device itself. It welcomes theoretical contributions as well as applications in the area of AIoT. Topics of interest include but are not limited to the following:

  • Energy-efficient artificial intelligence and machine learning algorithms;
  • Neuromorphic hardware and computing for IoT devices;
  • Edge computing-enabled AI;
  • Internet of Intelligent Things;
  • Machine learning-enabled sensing and decision-making for IoT;
  • Real-time IoT data analytics;
  • Applications using AIoT.

Prof. Dr. Chris Blondia
Prof. Dr. Jeroen Famaey
Dr. Carmen Delgado
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

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. Sensors 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 2600 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

  • Internet of Things
  • artificial intelligence
  • real-time data monitoring
  • Artificial Intelligence of Things
  • IoT

Published Papers (1 paper)

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Research

15 pages, 871 KiB  
Article
Design and Development of an AIoT Architecture for Introducing a Vessel ETA Cognitive Service in a Legacy Port Management Solution
by Clara I. Valero, Enrique Ivancos Pla, Rafael Vaño, Eduardo Garro, Fernando Boronat and Carlos E. Palau
Sensors 2021, 21(23), 8133; https://doi.org/10.3390/s21238133 - 05 Dec 2021
Cited by 5 | Viewed by 2443
Abstract
Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT [...] Read more.
Current Internet of Things (IoT) stacks are frequently focused on handling an increasing volume of data that require a sophisticated interpretation through analytics to improve decision making and thus generate business value. In this paper, a cognitive IoT architecture based on FIWARE IoT principles is presented. The architecture incorporates a new cognitive component that enables the incorporation of intelligent services to the FIWARE framework, allowing to modernize IoT infrastructures with Artificial Intelligence (AI) technologies. This allows to extend the effective life of the legacy system, using existing assets and reducing costs. Using the architecture, a cognitive service capable of predicting with high accuracy the vessel port arrival is developed and integrated in a legacy sea traffic management solution. The cognitive service uses automatic identification system (AIS) and maritime oceanographic data to predict time of arrival of ships. The validation has been carried out using the port of Valencia. The results indicate that the incorporation of AI into the legacy system allows to predict the arrival time with higher accuracy, thus improving the efficiency of port operations. Moreover, the architecture is generic, allowing an easy integration of the cognitive services in other domains. Full article
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