Special Issue "Recent Machine Learning Applications to Internet of Things (IoT)"

A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".

Deadline for manuscript submissions: 31 December 2019.

Special Issue Editors

Guest Editor
Dr. Antonino Staiano

Università di Napoli Parthenope, Naples, Italy
Website | E-Mail
Interests: computational intelligence; machine learning; neural networks; clustering; data mining in bioinformatics and ecological informatics; wireless sensor networks; IoT; soft computing
Guest Editor
Dr. Angelo Ciaramella

Università di Napoli Parthenope, Naples, Italy
Website | E-Mail
Interests: soft computing; machine learning; computational intelligence; data mining; data mining for astrophysics; geology and biology data; signal processing; audio streaming; brain–computer interface

Special Issue Information

Dear Colleagues,

It was a long time ago that Mark Weiser envisioned a world of small, cheap, and robust networked processing devices, distributed across human and natural environments at all scales, to aid our everyday life. Since then, the technological developments, from nanotechnologies to computation and communication systems, have converged into what is nowadays known as Internet of Things (IoT), and made what Weiser envisioned a reality. IoT has paved the way to a plethora of new application domains, at the same time posing, however, several challenges as a multitude of devices, protocols, communication channels, architectures. and middleware exist. Nevertheless, IoT is growing exponentially in the number and heterogeneity of actors; indeed, a number between 50 and 100 billion objects are expected by 2020, and this growth makes “intelligence” a critical turning point for the success of IoT.

In particular, we are witnessing an incremental development of interconnections between devices (smartphones, tablets, smartwatches, fitness trackers and wearable devices in general, smart TVs, home appliances, and more), people, processes, and data.
Data generated by the devices are becoming big data and call for advanced learning and data mining techniques to efficiently and effectively understand, learn, and reason with this amazing volume of information.

Moreover, thanks to the latest results of research on Artificial Intelligence, applications can count on an “intelligent” network of billions of sensors “aware” of their operating environment, able to listen, learn, and respond to offer new services and functionalities in the most disparate application domains, which guarantee greater security, simplicity, and reliability.

This Special Issue aims at collecting contributions concerning any use of intelligent techniques to any IoT aspects related to the IoT domain, from protocols to applications, to give the reader an up-to-date picture of the state-of-the-art on the connection between machine learning, computational intelligence, and IoT.
General topics covered in this Special Issue include but are not limited to the following methodologies and IoT applications:

  • Methodologies:
    • Soft computing (e.g., fuzzy logic, rough sets);
    • Neural networks;
    • Neuro-fuzzy systems;
    • Deep learning;
    • Evolutionary and bio-inspired algorithms;
  • Applications:
    • Machine learning and computational intelligence-aided IoT;
    • Intelligent middleware solutions IoT;
    • Brain–computer interface and IoT;
    • IoT and cloud computing;
    • Semantic web of things;
    • Social network IoT;
    • Internet of vehicles;
    • Context awareness;
    • Security and IoT.

Dr. Antonino Staiano
Dr. Angelo Ciaramella
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 papers will be 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. Electronics is an international peer-reviewed open access monthly 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 1400 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.

Published Papers (2 papers)

View options order results:
result details:
Displaying articles 1-2
Export citation of selected articles as:

Research

Open AccessArticle
Individual Behavior Modeling with Sensors Using Process Mining
Electronics 2019, 8(7), 766; https://doi.org/10.3390/electronics8070766
Received: 3 June 2019 / Revised: 2 July 2019 / Accepted: 3 July 2019 / Published: 9 July 2019
PDF Full-text (11244 KB) | HTML Full-text | XML Full-text
Abstract
Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have [...] Read more.
Understanding human behavior can assist in the adoption of satisfactory health interventions and improved care. One of the main problems relies on the definition of human behaviors, as human activities depend on multiple variables and are of dynamic nature. Although smart homes have advanced in the latest years and contributed to unobtrusive human behavior tracking, artificial intelligence has not coped yet with the problem of variability and dynamism of these behaviors. Process mining is an emerging discipline capable of adapting to the nature of high-variate data and extract knowledge to define behavior patterns. In this study, we analyze data from 25 in-house residents acquired with indoor location sensors by means of process mining clustering techniques, which allows obtaining workflows of the human behavior inside the house. Data are clustered by adjusting two variables: the similarity index and the Euclidean distance between workflows. Thereafter, two main models are created: (1) a workflow view to analyze the characteristics of the discovered clusters and the information they reveal about human behavior and (2) a calendar view, in which common behaviors are rendered in the way of a calendar allowing to detect relevant patterns depending on the day of the week and the season of the year. Three representative patients who performed three different behaviors: stable, unstable, and complex behaviors according to the proposed approach are investigated. This approach provides human behavior details in the manner of a workflow model, discovering user paths, frequent transitions between rooms, and the time the user was in each room, in addition to showing the results into the calendar view increases readability and visual attraction of human behaviors, allowing to us detect patterns happening on special days. Full article
(This article belongs to the Special Issue Recent Machine Learning Applications to Internet of Things (IoT))
Figures

Figure 1

Open AccessArticle
Machine Learning Prediction Approach to Enhance Congestion Control in 5G IoT Environment
Electronics 2019, 8(6), 607; https://doi.org/10.3390/electronics8060607
Received: 17 March 2019 / Revised: 18 May 2019 / Accepted: 24 May 2019 / Published: 30 May 2019
Cited by 1 | PDF Full-text (3234 KB) | HTML Full-text | XML Full-text
Abstract
The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, [...] Read more.
The 5G network is a next-generation wireless form of communication and the latest mobile technology. In practice, 5G utilizes the Internet of Things (IoT) to work in high-traffic networks with multiple nodes/sensors in an attempt to transmit their packets to a destination simultaneously, which is a characteristic of IoT applications. Due to this, 5G offers vast bandwidth, low delay, and extremely high data transfer speed. Thus, 5G presents opportunities and motivations for utilizing next-generation protocols, especially the stream control transmission protocol (SCTP). However, the congestion control mechanisms of the conventional SCTP negatively influence overall performance. Moreover, existing mechanisms contribute to reduce 5G and IoT performance. Thus, a new machine learning model based on a decision tree (DT) algorithm is proposed in this study to predict optimal enhancement of congestion control in the wireless sensors of 5G IoT networks. The model was implemented on a training dataset to determine the optimal parametric setting in a 5G environment. The dataset was used to train the machine learning model and enable the prediction of optimal alternatives that can enhance the performance of the congestion control approach. The DT approach can be used for other functions, especially prediction and classification. DT algorithms provide graphs that can be used by any user to understand the prediction approach. The DT C4.5 provided promising results, with more than 92% precision and recall. Full article
(This article belongs to the Special Issue Recent Machine Learning Applications to Internet of Things (IoT))
Figures

Figure 1

Electronics EISSN 2079-9292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top