**1. Introduction**

Due to the development of technology, dynamic changes have occurred in the automation and application of robotics and related systems. Nowadays, robotics plays a vital role in various applications, reducing the workload of human beings as well as errors made by humans. Robots are used in different surveillance processes such as detection of gas leaks and minimizing the risk of disaster through leakage in the chemical industry. Surveillance is the process of closely monitoring an industry, person, or group in the same and different situations. Surveillance is mainly needed in monitoring public places, border areas, companies, and industries in which the intervention of humans is difficult. This surveillance takes place with the help of an embedded system of robots. A robot is a pre-programmed electronic machine that replaces human work through automation and provides accurate results while minimizing error and improving time efficiency [1]. IoT-based devices are linked with one another by a network that connects electronic home appliances, vehicle-based electronic devices, actuators, and software, allows the exchange of information between one device and another. IoT devices can interact with other devices

**Citation:** Alshdadi, A.A. Evaluation of IoT-Based Smart Home Assistance for Elderly People Using Robot. *Electronics* **2023**, *12*, 2627. https:// doi.org/10.3390/electronics12122627

Academic Editors: Antonio Cano-Ortega and Francisco Sánchez-Sutil

Received: 10 April 2023 Revised: 4 June 2023 Accepted: 5 June 2023 Published: 11 June 2023

**Copyright:** © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

via Wi-Fi communication module by using the wireless sensor networks (WSN) in smart home electronic appliances and by Low Power Wireless Personal Area Networks (LoW-PAN) using RFID (Radio-Frequency Identification). An IoT-based smart home environment operates sensor-based devices remotely using mobile applications [2,3].

Human Interaction Robots (HIR) are mainly used in activities with a social component, such as medicine, neuroscience, cognitive science, and robotics. In order to provide security, the need for human intervention can be replaced with Cyborg. This robot can assist elderly people who are home alone, helping them to avoid crime due to home invasion or theft. In this case, it is necessary to provide security to elderly people by implementing a smart home environment system that contains the required sensor devices and it can transmit sensor signals through a communication module in order to alert the user and allow them to take precautionary steps [4,5]. The smart home secure environment enhances the lifestyle of human beings by providing security, detecting gas leakage in the kitchen, monitoring temperature and humidity in the home, detecting intruders, and more. This can be achieved by monitoring the surroundings of the smart home using a Raspberry Pi-based wireless camera, capturing images with related information, and sending it to the server. The main components of Cyborg are DC motors, a battery, and a wheel chassis, and it can be implemented in either automatic or manual mode [6].

Many research works have been implemented in smart home environments. The main issues are that they are inaccurate and inefficient, consumption time is high, and large amounts of storage space are required. This paper proposes a smart home environment for assisting elderly people using the KNN-ABC technique. It uses sensor-based electronic home appliances to monitor the surroundings of the smart home, detect intruders, and generate an alert notification to a registered mobile device or through a mobile app.

The contributions of this work are as follows:


This paper is written in five sections including this introduction. In the remainder of this paper, Section 2 discusses relevant previous works on smart home systems, Section 3 describes the proposed methodology, Section 4 describes the results and evaluates the outcomes, and Section 5 concludes the paper.

#### **2. Related Work**

Smart home electronic appliances based on IoT technology require automatic ON/OFF operation using a remote control-based application, voice-based technology, or fixed-time scheduling. A notification can be sent to the user by the server. This control is completely based on the activities of the user and passing the commands which can be triggered the activities through the mobile phone [7–9]. C. Victor et al. [10] proposed an IoT-based sensor system for monitoring the temperature in the environment. Using a temperature sensor, the system can collect sensor signals and store them in the server. Gladence et al. [11] proposed a client–server-based machine learning algorithm implemented for establishing an automated smart home environment control system able to interact with humans who send commands or triggering the smart appliances. M. Wendy et al. [12] presented a review of effective smart home technology to support elderly people in aspects related to health and security issues. Mehmood et al. [13] proposed an innovative concept involving managing a cloud storage platform, detecting hindrances, activating IoT devices by passing commands, executing those commands, and then transmitting the information to the registered users via mobile notification. To monitor health-related issues for elderly people in smart homes, various machine learning algorithms (LSTM, SVM, and RNN) can be used. IoT devices can closely observe health conditions of elderly people, analyze their

symptoms, and make predictions related to disease, as well as helping patients to consult their physicians and alert them to take medicine at the proper time [14]. Sensor devices are used with wireless networks, software, and computers to detect threats which affect the smart home environment. The implementation of the CNN model produces efficient detection of threats [15]. The Cyborg system can be used to save power, as it is able to automatically switch unnecessary electronic devices into the OFF state. In addition, it can detect the presence of human beings in the external surroundings of the smart home. At the same time, it can send a notification to the resident to perform important activities such as taking medicine, watering plants, etc. The proposed smart home system interfaces with sensor devices and assists elderly people in the smart home environment based on the generated sensor signals [16]. Table 1 enumerates related works on smart home environment systems along with the technology and sensor measurements employed by the respective systems.

Many earlier works demonstrated the use of IoT technology for energy efficiency, monitoring, and activity detection in a smart home environment. Below, we present selected works, which are tabulated in Table 1 along with their prominent features.

In [17], the author presented a smart home remote control system based on wireless sensor networks that collect positioning information and use actuators to control electrical appliances and operate alarms. In [18], X. Gengyi applied support vector machine (SVM) in a smart-home energy monitoring system using a cloud computing-based platform. The proposed solution improves energy efficiency and makes it easier for human interaction. In [19], C. Zhou et al. proposed a design for a smart home system based on virtual reality. Virtual reality was used to improve control interaction in the smart home. Their experimental results indicated that control methods could be simplified and costs reduced by as much as twenty percent through the use of virtual reality. In [20], P. Sharma et al. proposed a design for an IoT system using NodeMCU for real-time supervision of sensor measurements, allowing the user to control electrical loads in a smart home. O. Taiwo et al. [21] proposed a smart home automation mobile application that uses an Arduino microcontroller and personal area communication technologies such as Zigbee and Bluetooth. The practicality of the system was demonstrated through a simulation of the smart home environment.

In [22], M. S. Soliman et al. proposed a smart home automation system based on Arduino and Labview that allows the user to control temperature, save energy, and detect intruders. M. Naing et al. (2019) [23] demonstrated a proposed smart home automation system through a prototype implementation employing two Arduino Nano sensors. Sensors for measuring temperature, smoke, and motion were interfaced with these microcontrollers, which in turn interfaced with actuators to control and secure the home. R. D. Manu et al. (2019) [24] proposed a smart home system able to measure and respond to human activities using long-short term memory (LSTM) deep learning-based decisionmaking. S. K. Saravanan et al. (2019) [25] proposed a smart home controller using Arduino and Android. A smart door actuator was secured using a multi-factor authentication mechanism. L. D. Liao et al. (2019) [26] proposed the design of a smart home system using Arduino–Uno that provides user control and monitoring through a mobile application. Temperature and motion sensors were connected and controlled by the system to demonstrate its application in a smart home environment.

D. Popa et al. (2019) [27] demonstrate a smart home application where measurements of energy consumption and other sensor data could be stored on a cloud and later analyzed using machine learning methods for improved environmental sustainability and energy efficiency.

The authors of [28] applied linear discriminant analysis to classify power quality disturbances and carry out a performance analysis using KNN, naive Bayes, support vector machine (SVM), and random forest (RF) classifiers. Their results showed that higher classification accuracy was obtained in the presence of noise. In [29], Moraes et al. used a naive Bayes algorithm to propose a structured data mining model that can predict whether a smaller enterprise can join a business association with given attributes. The proposed approach can be utilized as a decision assistance tool for business associations to choose member enterprises. In [30], the authors used four different classifiers, i.e., KNN, naive Bayes, decision tree, and random forest approaches, to distinguish between defective and non-defective metal parts using laser-induced-breakdown spectroscopy. The abovementioned works show that machine learning algorithms can be used to make accurate predictions and to inform decisions in many situations and for a variety of data formats.

To make the literature survey more comprehensive, below we include several recent optimization methods for feature selection and classification. The authors of [31] proposed a hybrid feature selection method using a combination of the Butterfly optimization algorithm and the Ant Lion optimizer for breast cancer prediction. The proposed hybrid method outperforms both component methods for breast cancer diagnosis in terms of accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve.

Chakraborty et al. [32] proposed an improved whale optimization method for segmentation of chest X-Rays from patients with symptoms of COVID-19. During the global search phase, a random initialization is used to exploit after exploration. The proposed method outperformed the original method in terms of segmentation accuracy.

Sayed et al. [33] adopted a hybrid approach combining a convolutional neural network with Bald Eagle optimization to improve detection performance in melanoma skin cancer prediction. The robustness and accuracy of the proposed approach were verified as being superior through a comparison with state-of-the-art methods.

Xing et al. [34] proposed a modified whale optimization method using a quasi-Gaussian "bare bones" method. The modified method was able to promote diversity and expand the scope of the solution space.

Piri et al. [35] proposed a modified optimization method based on the Harris Hawk optimizer. This method, called multi-objective quadratic binary Harris Hawk optimization, uses a KNN classifier to extract the optimal feature subsets. The proposed methodology proved superior thanks to its better combination of fitness assessment criteria.


