Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.8
3.9
Journals
Sensors
Special Issues
AI for Smart Home Automation: 2nd Edition
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

AI for Smart Home Automation: 2nd Edition

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

Deadline for manuscript submissions: 20 August 2024 | Viewed by 1542

Special Issue Editor

Dr. Daniele Cenni

E-Mail Website
Guest Editor
Area for Innovation and Management of Information and Computer Systems, University of Florence, 50139 Firenze, Italy
Interests: deep learning; cloud computing, information retrieval; social networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In recent years, the home automation industry has evolved in tandem with the development of the information society. The development of artificial intelligence has favored the emergence of new paradigms, with beneficial effects on various sectors of automation, including home automation, which has become increasingly smart. This Special Issue aims to collect innovative contributions in the field of artificial intelligence applied to home automation, with the aim of encouraging the development of an increasingly intelligent, efficient, and energy-conscious home.

Dr. Daniele Cenni
Guest Editor

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.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

24 pages, 831 KiB  
Article
A Scheduler for Smart Home Appliances Based on a Novel Concept of Tariff Space
by Luis Rodolfo Rebouças Coutinho, Giovanni Cordeiro Barroso and Bruno de Athayde Prata
Sensors 2024, 24(6), 1875; https://doi.org/10.3390/s24061875 - 14 Mar 2024
Viewed by 426
Abstract
The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day from official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17% lower [...] Read more.
The background of this work is related to the scheduling of household appliances, taking into account variations in energy costs during the day from official Brazilian domestic tariffs: constant and white. The white tariff can reach an average price of around 17% lower than the constant, but charges twice its value at peak hours. In addition to cost reduction, we propose a methodology to reduce user discomfort due to time-shifting of controllable devices, presenting a balanced solution through the analytical analysis of a new method referred to as tariff space, derived from white tariff posts. To achieve this goal, we explore the geometric properties of the movement of devices through the tariff space (geometric locus of the load), over which we can define a limited region in which the cost of a load under the white tariff will be equal to or less than the constant tariff. As a trial for the efficiency of this new methodology, we collected some benchmarks (such as execution time and memory usage) against a classic multi-objective algorithm (hierarchical) available in the language portfolio in which the project has been executed (the Julia language). As a result, while both methodologies yield similar results, the approach presented in this article demonstrates a significant reduction in processing time and memory usage, which could lead to the future implementation of the solution in a simple, low-cost embedded system like an ARM cortex M. Full article
(This article belongs to the Special Issue AI for Smart Home Automation: 2nd Edition)
►▼ Show Figures

Figure 1

15 pages, 2735 KiB  
Article
Sound-Event Detection of Water-Usage Activities Using Transfer Learning
by Seung Ho Hyun
Sensors 2024, 24(1), 22; https://doi.org/10.3390/s24010022 - 19 Dec 2023
Viewed by 793
Abstract
In this paper, a sound event detection method is proposed for estimating three types of bathroom activities—showering, flushing, and faucet usage—based on the sounds of water usage in the bathroom. The proposed approach has a two-stage structure. First, the general sound classification network [...] Read more.
In this paper, a sound event detection method is proposed for estimating three types of bathroom activities—showering, flushing, and faucet usage—based on the sounds of water usage in the bathroom. The proposed approach has a two-stage structure. First, the general sound classification network YAMNet is utilized to determine the existence of a general water sound; if the input data contains water sounds, W-YAMNet, a modified network of YAMNet, is then triggered to identify the specific activity. W-YAMNet is designed to accommodate the acoustic characteristics of each bathroom. In training W-YAMNet, the transfer learning method is applied to utilize the advantages of YAMNet and to address its limitations. Various parameters, including the length of the audio clip, were experimentally analyzed to identify trends and suitable values. The proposed method is implemented in a Raspberry-Pi-based edge computer to ensure privacy protection. Applying this methodology to 10-min segments of continuous audio data yielded promising results. However, the accuracy could still be further enhanced, and the potential for utilizing the data obtained through this approach in assessing the health and safety of elderly individuals living alone remains a topic for future investigation. Full article
(This article belongs to the Special Issue AI for Smart Home Automation: 2nd Edition)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop