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IoT
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Advances in IoT and Machine Learning for Smart Homes
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Advances in IoT and Machine Learning for Smart Homes

A special issue of IoT (ISSN 2624-831X).

Deadline for manuscript submissions: 31 August 2025 | Viewed by 1813

Special Issue Editor

Dr. Tareq Hasan Khan

E-Mail Website
Guest Editor
School of Engineering, Eastern Michigan University, Ypsilanti, MI, USA
Interests: Internet of Things (IoT); smart home; embedded systems; machine learning; image processing

Special Issue Information

Dear Colleagues,

This Special Issue will focus on the transformative impact of integrating Internet of Things (IoT) technologies with machine learning techniques to create intelligent, efficient, and secure smart home environments. The proliferation of IoT devices in home automation has led to significant advancements in energy management, security, and user convenience, while machine learning algorithms enable these devices to learn from user behavior, anticipate needs, and provide real-time decision-making support.

We invite submissions that explore the intersection of IoT, embedded systems, and machine learning in smart home applications. Topics may include novel IoT-based architectures for home automation, energy-efficient solutions, security and privacy concerns, as well as emerging applications such as intelligent health monitoring, smart appliances, and adaptive home environments. Contributions addressing challenges in real-time data processing, sensor integration, and power optimization in smart homes are particularly welcome.

Dr. Tareq Hasan Khan
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. IoT is an international peer-reviewed open access quarterly 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 1200 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 (IoT)
  • smart home automation
  • machine learning in IoT
  • embedded systems for home automation
  • real-time data processing
  • energy-efficient IoT solutions
  • home security and privacy
  • intelligent health monitoring
  • adaptive smart environments
  • deep learning in IoT

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

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Research

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30 pages, 4500 KiB  
Article
A Deep Learning-Based Gunshot Detection IoT System with Enhanced Security Features and Testing Using Blank Guns
by Tareq Khan
IoT 2025, 6(1), 5; https://doi.org/10.3390/iot6010005 - 3 Jan 2025
Viewed by 818
Abstract
Although the U.S. makes up only 5% of the global population, it accounts for approximately 31% of public mass shootings. Gun violence and mass shootings not only result in loss of life and injury but also inflict lasting psychological trauma, cause property damage, [...] Read more.
Although the U.S. makes up only 5% of the global population, it accounts for approximately 31% of public mass shootings. Gun violence and mass shootings not only result in loss of life and injury but also inflict lasting psychological trauma, cause property damage, and lead to significant economic losses. We recently developed and published an embedded system prototype for detecting gunshots in an indoor environment. The proposed device can be attached to the walls or ceilings of schools, offices, clubs, places of worship, etc., similar to smoke detectors or night lights, and they can notify the first responders as soon as a gunshot is fired. The proposed system will help to stop the shooter early and the injured people can be taken to the hospital quickly, thus more lives can be saved. In this project, a new custom dataset of blank gunshot sounds is recorded, and a deep learning model using both time and frequency domain features is trained to classify gunshot and non-gunshot sounds with 99% accuracy. The previously developed system suffered from several security and privacy vulnerabilities. In this research, those vulnerabilities are addressed by implementing secure Message Queuing Telemetry Transport (MQTT) communication protocols for IoT systems, better authentication methods, Wi-Fi provisioning without Bluetooth, and over-the-air (OTA) firmware update features. The prototype is implemented in a Raspberry Pi Zero 2W embedded system platform and successfully tested with blank gunshots and possible false alarms. Full article
(This article belongs to the Special Issue Advances in IoT and Machine Learning for Smart Homes)
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Review

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41 pages, 2872 KiB  
Review
A Comprehensive Survey on the Requirements, Applications, and Future Challenges for Access Control Models in IoT: The State of the Art
by Mohammad Shamim Ahsan and Al-Sakib Khan Pathan
IoT 2025, 6(1), 9; https://doi.org/10.3390/iot6010009 - 24 Jan 2025
Viewed by 547
Abstract
The Internet of Things (IoT) is a technologyof connecting billions of devices with heterogeneous types and capabilities. Even though it is an attractive environment that could change the way we interact with the devices, the real-life and large-scale implementation of it is greatly [...] Read more.
The Internet of Things (IoT) is a technologyof connecting billions of devices with heterogeneous types and capabilities. Even though it is an attractive environment that could change the way we interact with the devices, the real-life and large-scale implementation of it is greatly impeded by the potential security risks that it is susceptible to. While the potential of IoT is significant, the security challenges it faces are equally formidable. IoT security can be addressed from different angles, but one of the key issues is the access control model because among the many challenges, access control is a pivotal concern that determines the overall security of IoT systems. This eventually determines which device is given access to the IoT systems and which is denied access. In this work, we conduct a systematic and thorough survey on the state-of-the-art access control models in IoT. This study includes more than 100 related articles, including 77 best-quartile journal papers. We cover conventional as well as advanced access control models, taking the crucial period of various studies in this particular area. In addition, a number of critical questions are answered and key works are summarized. Furthermore, we identify significant gaps in existing models and propose new considerations and prospects for future developments. Since no existing survey explores both conventional and sophisticated access control models with essential challenges, trends and application domains analysis, and requirements analysis, our study significantly contributes to the literature, especially in the IoT security field. Full article
(This article belongs to the Special Issue Advances in IoT and Machine Learning for Smart Homes)
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