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 [7,8,9]. 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 the 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 decision-making. 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 above-mentioned 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 researchers 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.