The Role of Internet of Things in Smart Homes

Padmanaban, Sanjeevikumar; Nasab, Mostafa Azimi; Shiri, Mohammad Ebrahim; Javadi, Hamid Haj Seyyed; Nasab, Morteza Azimi; Zand, Mohammad; Samavat, Tina

doi:10.1002/9781119893998.ch13

Cited by 24 publications

(8 citation statements)

References 27 publications

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“…Within the IoT architecture, the application layer resides at the top level. This layer provides the user interface, which includes services such as healthcare, smart homes, and connected cars [125], [126]. It recognizes spam and filters out the malicious content [127].…”

Section: ) Application Layermentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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The Healthcare Internet-of-Things (H-IoT), commonly known as Digital Healthcare, is a datadriven infrastructure that highly relies on smart sensing devices (i.e., blood pressure monitors, temperature sensors, etc.) for faster response time, treatments, and diagnosis. However, with the evolving cyber threat landscape, IoT devices have become more vulnerable to the broader risk surface (e.g., risks associated with generative AI, 5G-IoT, etc.), which, if exploited, may lead to data breaches, unauthorized access, and lack of command and control and potential harm. This paper reviews the fundamentals of healthcare IoT, its privacy, and data security challenges associated with machine learning and H-IoT devices. The paper further emphasizes the importance of monitoring healthcare IoT layers such as perception, network, cloud, and application. Detecting and responding to anomalies involves various cyber-attacks and protocols such as Wi-Fi 6, Narrowband Internet of Things (NB-IoT), Bluetooth, ZigBee, LoRa, and 5G New Radio (5G NR). A robust authentication mechanism based on machine learning and deep learning techniques is required to protect and mitigate H-IoT devices from increasing cybersecurity vulnerabilities. Hence, in this review paper, security and privacy challenges and risk mitigation strategies for building resilience in H-IoT are explored and reported.

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Section: ) Application Layermentioning

confidence: 99%

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Khatun,

Memon,

Eising

et al. 2023

IEEE Access

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“…The Internet of Things (IoT) is an emerging field of computer science that has impacted the world in a short period of time. The concepts of smart homes [ 2 ], smart-resource-based cities [ 3 ], smart driving [ 4 ], and smart farming [ 5 ] have changed the living and working styles of many people around the world. Smart devices are embedded in smart homes and cities, and things are controlled by smart devices.…”

Section: Introductionmentioning

confidence: 99%

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

Almujally

Aljrees

Saidani

et al. 2023

Sensors

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With technological advancements, smart health monitoring systems are gaining growing importance and popularity. Today, business trends are changing from physical infrastructure to online services. With the restrictions imposed during COVID-19, medical services have been changed. The concepts of smart homes, smart appliances, and smart medical systems have gained popularity. The Internet of Things (IoT) has revolutionized communication and data collection by incorporating smart sensors for data collection from diverse sources. In addition, it utilizes artificial intelligence (AI) approaches to control a large volume of data for better use, storing, managing, and making decisions. In this research, a health monitoring system based on AI and IoT is designed to deal with the data of heart patients. The system monitors the heart patient’s activities, which helps to inform patients about their health status. Moreover, the system can perform disease classification using machine learning models. Experimental results reveal that the proposed system can perform real-time monitoring of patients and classify diseases with higher accuracy.

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“…So, offline approaches do not provide satisfactory performance [9]. Other well-known methods include the Temperature algorithm, Open Circuit Voltage Technique [10] a Short Circuit Current Technique [11].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

Samavat

Nazari

Ghalehnoie

et al. 2023

International Journal of Energy Research

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With the maturation of nonlinear systems, considerable endeavors have been made to provide valid and high-speed controllers to supervise superior and more complex systems. Artificial intelligence has been remembered as the head topic among designers in the last decade. One of the popular control techniques is fuzzy logic, which is known to provide a controller that simulates the behavior of an expert operator. On the other hand, due to the necessity of change in human energy sources and the popularity of solar energy, attention to the greatest utilization of this category of green resources has significantly increased. Maximum power point tracking (MPPT) in solar systems is a headed topic, with innovative methods being presented every day despite numerous articles. However, the less discussed topic is the choice of a fuzzy inference system. In this article, the two classes of Mamdani and Sugeno are discussed to introduce the best controller for extracting more power from a solar system by implementing both types and gaining an understanding of their differences. In addition, the influence of the number of input membership functions on the controller performance is investigated. Therefore, two different input membership functions are given to each fuzzy system model. It should be noted that fuzzy system setup has been done by genetic algorithm to respond to the mortal desire to automate various processes, which is a subset of artificial intelligence. Accordingly, four different fuzzy systems have been designed and implemented on a solar system. The results were tested and summarized in various radiations in MATLAB Simulink.

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The Role of Internet of Things in Smart Homes

Cited by 24 publications

References 27 publications

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Machine Learning for Healthcare-IoT Security: A Review and Risk Mitigation

Monitoring Acute Heart Failure Patients Using Internet-of-Things-Based Smart Monitoring System

A Comparative Analysis of the Mamdani and Sugeno Fuzzy Inference Systems for MPPT of an Islanded PV System

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