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Home Automation for the Internet of Things
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Home Automation for the Internet of Things

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

Deadline for manuscript submissions: closed (15 November 2019) | Viewed by 10127

Special Issue Editor

Prof. Dr. Subhas Mukhopadhyay

E-Mail Website
Guest Editor
School of Engineering, Macquarie University, Sydney, NSW 2109, Australia
Interests: smart sensors; sensing technology; WSN; IoT; ICT; smart grid; energy harvesting
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

With the relentless advance in sensing technologies, we are witnessing an increasing number of sophisticated smart home deployments covering a wide spectrum of applications, including health monitoring and home automation, which can greatly improve the quality of life of many people. In this Special Issue we will focus on the latter: home automation (often called smart homes). With the help of wireless sensor networks (WSN) and the Internet of Things (IoT), any residence can be equipped with innovative technological solutions and services to improve the lives and security of residents. Smart home technologies (SHTs) comprise sensors, monitors, interfaces, appliances and devices networked together to enable automation, as well as localized and remote control of the domestic environment. In this context, thanks to the latest sensor technologies and machine learning algorithms, the domestic technological environment is able to monitor the well-being and daily life activities of inhabitants, and to learn their specific needs and habits, adapting to them and thus improving their overall quality of life. In addition, smart homes can intelligently manage the energy usage of appliances and all other aspects of the domestic environment, thus creating a more comfortable, energy-efficient space for their inhabitants.

This Special Issue solicits the submission of high-quality and unpublished papers that aim to solve open technical problems and challenges typical of IoT-oriented home automation. The main aim is to integrate novel approaches efficiently, focusing on performance evaluation and comparisons with existing solutions. Both theoretical and experimental studies for typical IoT-oriented home automation scenarios are encouraged. Furthermore, high-quality review and survey papers are welcome. Topics considered for publication include but are not limited to the following areas:

  • Smart Sensors for Home Automation and Smart Homes
  • Wireless Sensor Networks for Smart Homes
  • Internet of Things-Enabled Home Automation
  • Green Communication for Smart Homes
  • Energy Management Systems and Networks for Smart Homes
  • Smart Environment Monitoring and Control
  • Smart Management of Home Appliances

Prof. Subhas Mukhopadhyay
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.

Keywords

  • Home Automation
  • Green Communications
  • Security and Privacy
  • Artificial Intelligence
  • Smart Homes
  • WSN for Smart Homes
  • IoT Enabled Homes
  • Smart Sensors

Published Papers (1 paper)

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Research

23 pages, 1232 KiB  
Article
Reinforcement Learning-Based Energy Management of Smart Home with Rooftop Solar Photovoltaic System, Energy Storage System, and Home Appliances
by Sangyoon Lee and Dae-Hyun Choi
Sensors 2019, 19(18), 3937; https://doi.org/10.3390/s19183937 - 12 Sep 2019
Cited by 77 | Viewed by 9419
Abstract
This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing model-based optimization methods for home energy management [...] Read more.
This paper presents a data-driven approach that leverages reinforcement learning to manage the optimal energy consumption of a smart home with a rooftop solar photovoltaic system, energy storage system, and smart home appliances. Compared to existing model-based optimization methods for home energy management systems, the novelty of the proposed approach is as follows: (1) a model-free Q-learning method is applied to energy consumption scheduling for an individual controllable home appliance (air conditioner or washing machine), as well as the energy storage system charging and discharging, and (2) the prediction of the indoor temperature using an artificial neural network assists the proposed Q-learning algorithm in learning the relationship between the indoor temperature and energy consumption of the air conditioner accurately. The proposed Q-learning home energy management algorithm, integrated with the artificial neural network model, reduces the consumer electricity bill within the preferred comfort level (such as the indoor temperature) and the appliance operation characteristics. The simulations illustrate a single home with a solar photovoltaic system, an air conditioner, a washing machine, and an energy storage system with the time-of-use pricing. The results show that the relative electricity bill reduction of the proposed algorithm over the existing optimization approach is 14%. Full article
(This article belongs to the Special Issue Home Automation for the Internet of Things)
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