Ambient Intelligence Methods and Applications

Dear Colleague,

With data science and artificial intelligence becoming omnipresent and omnilayer, this has led researchers to reconsider and redesign our future cities, IoT devices and next-generation applications. This Special Issue has two purposes: First, it aims to present recent advances in data mining and machine learning techniques, focusing on knowledge discovery and optimal decision making with special emphasis on dynamic, heterogeneous and continuously changing environments. Second, it plans to present state-of-the-art data-driven applications that take input from distributed sources and build collective knowledge in order to improve the daily life and wellbeing of human society.

Sensors, the integration of actuators and processing units into our environment provides the contextual awareness and processing capability for revolutionizing how people interact and communicate with one another. This communication, also termed networking, includes humans, devices and appliances, giving novel opportunities to a wide range of telematic services and applications, such as autonomous vehicles, surveillance drones, energy-saving and e-health wearables, just to mention a few.

The evolution of human and animal species revealed that environmental factors have significantly influenced our intelligent lineage. Environmental changes and novel experiences have been found to be reflected in cognitive systems. In a similar way, we invite academic and industrial researchers to propose original, high-quality and innovative methods for incremental learning and few-shot and self-learning.

Topics of interest include, but are not limited to:

1. Online learning;
2. Zero- and few-shot learning;
3. Self-learning systems;
4. Distributed and collaborative learning;
5. Data-driven IoT;
6. Smart homes/cities/factoring/healthcare;
7. Mobility and intelligent transportation;
8. Intelligent networking;
9. Drones for object and event detection;
10. Cybernetics and human-to-machine interaction.

Dr. John Violos
Dr. Dimitrios Michail
Dr. Iraklis Varlamis
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. Mathematics 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.

Title: Double Reinforcement Learning-enabled, Revenue Driven Resource Allocation for IoT
Authors: Aris Leivadeas
Affiliation: École de Technologie Supérieure (ÉTS Montreal), Université du Québec, Canada

Title: TRANSFER LEARNING FOR DAY-AHEAD LOAD FORECASTING: ACASE STUDY ON EUROPEAN NATIONAL ELECTRICITY DEMANDTIME SERIES
Authors: Alexandros Menelaos Tzortzis; Sotiris Pelekis; Evangelos Spiliotis; Evangelos Karakolis; Spiros Mouzakitis; John Psarras; Dimitris Askounis
Affiliation: National Technical University of Athens
Abstract: Short-term load forecasting (STLF) is crucial for the daily operation of power grids. However, the non-linearity, non-stationarity, and randomness characterizing electricity demand time series renders STLF a challenging task. Various forecasting approaches have been proposed for improving STLF, including neural network (NN) models which are trained using data from multiple electricity demand series that may not necessary include the target series. In the present study, we investigate the performance of this special case of STLF, called transfer learning (TL), by considering a set of 27 time series that represent the national day-ahead electricity demand of indicative European countries. We employ a popular and easy-to-implement NN model and perform a clustering analysis to identify similar patterns among the series and assist TL. In this context, two different TL approaches, with and without the clustering step, are compiled and compared against each other as well as a typical NN training setup. Our results demonstrate that TL can outperform the conventional approach, especially when clustering techniques are considered.

Title: Intelligent Self-Optimization for Edge and IoT Storage Platforms
Authors: Evangelos Psomakelis
Affiliation: Department of Informatics and Telematics, Harokopio University of Athens, 9 Omirou Str., 17671 Athens, Greece