2.1 Smart Home Services

Smart home automation is implemented to offer tailored services to homeowners, prioritizing convenience, energy optimization, security, and timeliness within the residential premises. These services are briefly discussed as outlined below.

2.2 Smart Home Control and Monitoring System

Smart home control and monitoring systems integrate different household appliances and other devices into an automatically controlled ecosystem for remote and intelligent management of home gadgets and other connected devices. In this context, the focus is on remote control and monitoring of home appliances and all other devices on the smart home system. The control and monitoring are multimodal and include lighting controls, security breaches and alerts, real-time monitoring of home conditions, and other personalized user preference services.

Khan et al. [39] introduced an automation system that enables remote control of lights in a smart home environment. The system utilizes a real-time algorithm for controlling lights and monitoring environmental conditions. The algorithm makes use of motion sensors to generate inferences for light control. Additionally, the algorithm is employed for monitoring power consumption in the home via the Wi-Fi module and triggering an alarm based on gas levels. While the real-time algorithm offers intelligent light switching, the limitation of using a web page for home control is acknowledged.

Rani et al. [40] proposed a voice-controlled home automation system that leverages natural language processing (NLP) and artificial intelligence techniques. Users can issue voice commands through a mobile phone to control home appliances, which are interpreted using a predefined NLP medium. However, this system is focused solely on controlling home appliances and does not extend its functionality to other aspects of home automation, such as environmental condition monitoring, intruder detection, or motion sensing. Bimenyimana et al. [41] designed and implemented a web-based home control and monitoring system for electrical appliances. The prototype implementation utilized an Arduino Board, Node MCU, Light Emitting Diodes (LED), relays, and other hardware components. The control system's design aims to reduce energy consumption levels. However, the web-based control interface may result in longer control times compared to a mobile-based application. The aforementioned studies highlight different approaches to smart home automation, showcasing various techniques and technologies employed for controlling lights, implementing voice control, and managing electrical appliances. While each study presents innovative solutions, they also acknowledge limitations and suggest potential areas for improvement in terms of control interfaces and system capabilities.

Mehmood et al. [42] developed an automation system for controlling smart home appliances based on an object detection mechanism. The system utilizes an object detection algorithm, model-view-controller architecture, and a cloud of things. Communication between IoT devices and home appliances is facilitated through the message queuing telemetry transport (MQTT) mode. However, a limitation of the system lies in the requirement of object detection before control, which may introduce delays or inaccuracies. Vaidya et al. [43] proposed a smart home automation system targeting the elderly, implemented as an Android application. The system enables remote control of appliances through voice or touch commands. The door monitoring system incorporates face detection using an installed camera. Additionally, the system includes an energy efficiency option, featuring a module that can analyze the usage of each electrical appliance. Although the proposed system offers valuable functionalities, the implementation details are not specified. Wibowo, Muhammad, and Alif [44] presented a smart home control and monitoring system based on the database replication method. User access is granted through a single online master database, requiring login with username and password. The system can control and monitor multiple systems online; however, the specific implementation details are not mentioned. It is worth noting that the system's functionality heavily relies on the availability and reliability of the online database. Any issues or disruptions with the database may impact the effectiveness of the system. More, these studies highlight different approaches to smart home automation, focusing on object detection, elderly-friendly interfaces, and database replication. Each system offers unique features and functionalities but also acknowledges certain limitations or potential challenges. Further research and development are needed to address these limitations and improve the overall effectiveness and robustness of SHAS.

Taiwo et al. [45, 46] proposed a framework for controlling electrical home appliances, enabling both on-site and off-site control. The framework utilizes Bluetooth and Zigbee technologies for communication, with commands issued through an Android mobile application. However, the communication range of the system was limited due to the proposed Bluetooth technology, which may restrict the usability of the system in larger homes or buildings. Obeidat et al. [47] presented a GSM and IoT-based smart home automation system that allows for the control of home appliances and environmental monitoring through a short messaging service (SMS) notification. Users communicate with the home and issue commands to the microcontroller via SMS. The use of GSM protocol for communication enables remote control; however, it should be noted that GSM technology can be slower compared to newer technologies like 3G and 4G due to shared bandwidth and potential interference [48]. Nonetheless, the system's strength lies in its ability to automatically control the lighting system, contributing to energy consumption reduction.

Hassan et al. [49] introduced a smart control and monitoring system for home automation, specifically targeting the detection of kitchen temperature and gas leakage. The system automatically detects gas pressure levels and notifies the user if it deviates from the expected range. Remote control and monitoring of appliances are facilitated through an Android application. The authors conducted simulations using MATLAB 2016b and implemented a prototype using ESP32, a DHT11 sensor, a 5 V Relay, and an MQ-135 gas sensor. The system prioritizes the safety of home occupants by promptly detecting and notifying gas leakages. These studies demonstrate different approaches to smart home automation, considering various communication technologies such as Bluetooth, Zigbee, and GSM. Each system offers unique features and addresses specific aspects of home automation, including appliance control, energy efficiency, and safety. However, certain limitations and considerations, such as communication range, bandwidth limitations, and potential interference, need to be taken into account for the successful deployment and scalability of such systems.

2.3 Energy Management

The primary focus in the design and deployment of some SHAS is energy management and optimization. In that case, the target is to design a smart home system that allows users to manage electricity usage in the home by remotely turning off appliances when they are not in use. The systems provide information on the appliances that consume more electricity, thereby providing insights into making informed decisions either on when to minimize usage or turn off the appliance. Energy management in smart home automation not only optimizes energy efficiency and costs but also contributes to environmental sustainability in the green economy.

Zhuang et al. [50] utilized a power scheduling approach to propose a smart home system for optimal energy management. The system introduces a smart grid-based scheduling method for power usage within the home environment. To improve the operation start time of appliances, a genetic algorithm was utilized. The algorithm considered Real-Time Electricity Pricing to schedule power, taking into account residents' preferences. The primary objective of the system was to optimize user power consumption while also providing benefits to utility companies through the pricing scheme. Singh, Sajwan, and Pal [51] designed a solar-powered home automation application that integrates solar power with a microcontroller. The system enables the control of household appliances and doors using a mobile phone. A prototype demonstration was implemented using IoT hardware. The system aims to reduce electricity load by utilizing solar power as an alternative energy source. However, if the solar system's battery is drained, the system might become nonfunctional, and control of the home cannot be achieved until the battery is recharged. These studies present innovative approaches to energy management in smart homes. Zhuang et al. [50] focused on optimizing power consumption through intelligent scheduling, considering real-time pricing and user preferences. On the other hand, Singh et al. emphasized the utilization of solar power for household automation, promoting energy efficiency. However, potential limitations, such as system functionality during low battery levels or in the absence of solar power, should be taken into account for reliable operation and user satisfaction.

Priya and Kannammal [52] introduced an energy management system for automation that focuses on load scheduling and cargo balancing. The system utilizes sensor data to generate information for automatic control. Control of the overall system was conducted through an online browser on a local PC. However, it should be noted that control via mobile applications tends to be faster compared to PC-based control. Mahmud, Ahmed, and Shikder [53] introduced an automated system for metering in smart homes. This system empowers homeowners to oversee and regulate the energy consumption of household appliances and electronic devices via a specialized website. The primary objective was to simplify energy monitoring for individuals while advocating for energy-efficient behaviors. Furthermore, the design incorporates an online metering mechanism capable of detecting potential household power distribution issues, along with a billing system aimed at enhancing energy efficiency even further.

Additionally, Popa et al. [54] experimented with a smart home automation system to detect unusual energy patterns. Their approach was based on neural network models, and they utilized cloud computing services to establish a modular platform for data collection, aggregation, and storage in the smart home environment. The authors demonstrated that the proposed machine learning algorithms contributed to the enhancement of smart home automation by reducing energy consumption. These studies highlighted different aspects of energy management and automation in smart homes. Priya and Kannammal focused on load scheduling and cargo balancing, Mahmud et al. emphasized metering, control, and observation of appliances, and Popa et al. explored the detection of unusual energy patterns using machine learning. Each approach contributed to promoting energy efficiency and optimizing the performance of SHAS.

Similarly, Khan, Nazir, and Ullah Khan [55] reported the efficacy of Convolutional Neural Network (CNN) for tasks involving image detection and processing within smart home technology. Their study showcased CNN's utilization of extracting spatial features of objects through an IoT camera integrated into Raspberry Pi hardware. Concurrently, Long Short-Term Memory (LSTM) was employed for extracting temporal features and classification. The integration of CNN and LSTM aims to attain optimal detection and recognition results of objects captured by cameras within the smart home automation system. Notably, image preprocessing techniques and pretrained models can be employed to extract salient features of objects within the surveillance boundary of the smart home for object detection tasks. These features are subsequently inputted into CNN and LSTM for recognition [56].

Parsa, Najafabadi, and Salmasi [57] introduced an automated control system aimed at optimizing the management of electrical home appliances. This system is engineered to regulate device energy consumption by intelligently activating and deactivating smart plugs connected to particular appliances at suitable intervals. Employing an optimization methodology, the system identifies the most opportune times to operate the appliances, adhering to predetermined constraints, before executing the automated switching process. However, it should be noted that the designed system appears to prioritize the electricity supplier's interests over those of the home's consumer. Rajesh, Shajin, and Kannayeram [58] proposed a hybrid energy management method for smart homes using IoT. The methods employed in this approach involve the Sailfish Optimizer (SFO) and Adaptive Neuro-Fuzzy Interference System (ANFIS). These methods are applied to achieve optimal power management and data collection within the home. By predicting load demand and effectively managing energy, the methods contribute to energy conservation in the smart home environment. Khajeh, Laaksonen, and Simoes [59] introduced a fuzzy logic controller for energy conservation in a smart home setting. The proposed method aims to conserve energy by managing congestion and enhancing the distribution system within the smart home. Flexibility of energy resources is promoted to control the power system frequency through balancing services. These studies highlight different approaches to managing and conserving energy in smart homes. Parsa et al. focus on automatic control and optimization, Rajesh et al. utilize hybrid techniques for energy management, and Khajeh et al. propose a fuzzy logic controller for energy conservation. Each approach contributes to the efficient utilization of energy and promotes sustainability in the smart home environment.

2.4 Home Safety and Security System

Saravanan, Nainar, and Marichamy [60] proposed a home automation safety and security system by utilizing Bluetooth technology. This system comprises a smartphone application responsible for overseeing home devices, monitoring security, and regulating door access. The design incorporates various features, such as automatically turning off lights during nighttime, detecting gas leaks or smoke, and managing home appliances. Additionally, an authentication module was integrated to automatically secure and release door locks. Communication between the user's smartphone and the hardware relies on Bluetooth, which, although offering a narrower coverage range compared to Wi-Fi-based technologies, provides a secure mechanism according to the proposed approach.

Vasyl et al. [61] presented a smart home system for the prevention of domestic accidents. The system aims to alert home users in the event of potential dangers or accidents, particularly fire outbreaks. Sensors for fire, gas, smoke, and water leak detection are employed to identify and prevent such incidents. The system design incorporates a neuro-controller that interfaces with the sensors, appliances, and alarms. Arduino microcontrollers and Artificial Neural Network-based programming models were used to build and test the system's reliability and functionality. However, there was no specific mention of implemented home appliances or their functionality.

Ajao et al. [62] introduced a smart home security system that focuses on revitalizing the home entry points. Access is granted through rigorous authorization and authentication processes, with users notified of access or denial of access through in-app messages. Moreover, motion sensors are integrated to detect any movement within the premises. The operation of the system is managed through an Android mobile application utilizing wireless IoT communication. Despite its ease of use and flexibility, the system lacks the capability to capture images of intruders for monitoring purposes.

Singh et al. [63] devised a smart home system prioritizing home appliance safety and control, door access, and intruder detection. The system is engineered to alert users of low gas levels in cylinders or the presence of individuals as detected by sensors. The initial version of the system utilized components such as an Arduino Uno board, IR, Node MCU, and LDR sensor modules. However, the authors did not provide specific details regarding the controllable home appliances or the overall functionality of the system.

Taiwo et al. [64] presented an enhanced intelligent smart home system that encompasses monitoring, control, security, and safety features. The system performs dual functionality of appliance control and home security. A deep learning model is employed to develop an algorithm for motion detection and classification based on motion patterns. By classifying images from surveillance cameras, the deep learning model can determine whether an individual is an intruder or a homeowner and send notifications accordingly.

Lee et al. [65] pioneered the integration of image and voice recognition to devise a smart home security system. Recognizing the evolving scenery of security breaches, relying solely on a single mode of identification for authorization is no longer deemed reliable. Thus, the fusion of voice and image recognition, particularly facial recognition, establishes a two-step authentication process before granting access to home services. This innovation has gained substantial acceptance, evidenced by the deployment of security measures such as facial recognition-enabled doors and access to facilities.

These studies demonstrate various approaches to enhancing safety and security in SHAS. Saravanan et al. focus on Bluetooth-based control with a secured mechanism, Vasyl et al. present an accident prevention subsystem, Ajao et al. [62] emphasize home security and access control, Singh et al. propose a system for safety and appliance control, and Taiwo et al. introduce an intelligent system with deep learning-based motion detection. Each approach contributes to ensuring the safety and security of smart homes, providing users with peace of mind.

2.5 Smart Home Healthcare

Minh et al. [66] introduced a cloud-dependent smart home automated healthcare system that monitors patients' daily activities, rehabilitation progress, and behavioral changes to facilitate their recovery. The system not only enables home control but also allows healthcare providers to remotely monitor patients' physiological conditions through data captured by noninvasive wearable sensors. Human motion and activity information are collected using environmental sensors. However, a limitation of the system is the high-energy consumption resulting from the type of sensors used and data transmission. Lin et al. [67] developed a smart home mobile healthcare system specifically designed for wheelchair users. The system utilizes nodes of wireless body sensor networks (WBSN) and ECG sensors to measure vital signs such as heart rate and ECG. Additionally, the system can sense and monitor the home environment and human activities efficiently.

Taiwo and Ezugwu [68] presented a smart home monitoring and healthcare system tailored for patients in Covid-19 quarantine. This system remotely monitors specific physiological conditions and transmits recorded vitals to the doctors. It also controls essential home devices and monitors environmental conditions such as humidity and temperature. The system enables doctors to remotely monitor their patients, minimizing the need for physical contact during treatment. Patients can also comfortably manage their living environment by remotely controlling lights, fans, and other appliances. However, the feasibility of the system was not evaluated through a hardware implementation.

Eren et al. [69] introduced an integrated smart home system personalized for individuals with dementia, aimed at facilitating ambient living. Operating on the Android platform, this system is designed to gather, record, and transmit data through a cloud-based platform. It incorporates diverse sensors capable of detecting an individual's location, identifying flames, and monitoring the usage of specific household appliances. Furthermore, the system can issue alerts to prompt the patient's response to scheduled tasks based on sensor inputs and includes a switch to monitor light status. Its primary goal is to recognize patient activities and relay pertinent information to healthcare providers or caregivers. Data collected from various sensors positioned throughout the home are processed for subsequent analysis.

Neeraj et al. [70] developed a smart home automation software designed specifically for individuals with short-term memory loss. This system aids them in executing tasks easily, including automatic management of lighting, plant watering, gate operation, and notification for the dustbin. Meanwhile, Bilal and Khaled [71] introduced a wireless system design that allows for the remote control of household appliances. Targeted at elderly and disabled individuals, this system facilitates convenient control and monitoring of home appliances. It permits users to regulate electronic devices via a remote control device, transmitting commands wirelessly through XBee transceivers. The remote control features distinct buttons for various appliances and an LCD for message notifications. However, the system's communication range may encounter disruptions if obstructed by obstacles.

Bhardwaj, Joshi, and Gaur [72] presented an IoT-enabled smart healthcare monitoring system specifically for COVID-19 patients. The system allows doctors to remotely monitor patients by collecting real-time data on temperature, blood pressure, and pulse rate. Detectors and sensors deployed in the home environment enable continuous monitoring. A prototype implementation of the system utilized Raspberry Pi, a blood pressure sensor, and a temperature sensor. The data capturing and transmission modules enhance real-time monitoring capabilities for COVID-19 patients. In summary, these studies introduce a range of smart home healthcare systems that aim to enhance the monitoring, safety, and well-being of patients. The systems leverage various sensors, wearables, and remote control functionalities to enable remote monitoring, data collection, and automation of essential tasks.

Table 2 presents highlights of related studies on smart home automation taking into consideration the methodology adopted, the IoT technologies utilized, the application areas, and the strengths and weaknesses identified in the study. As shown in the studies, the early designs of home automation systems did not integrate artificial intelligence for users' preferences and multi-modal input commands. At this stage, SHAS lacks the functionality of personalized services, and the ability to learn and adapt to environmental conditions.

2.6 Artificial Intelligence in Smart Home

The integration of artificial intelligence into smart home sensors and actuators fosters enhanced interaction among household appliances, thereby yielding valuable data for understanding the habits of home occupants. Data generated within a smart home environment can be harnessed and processed to forecast user behavior. Moreover, artificial intelligence equips home appliances to better serve the needs of the elderly and disabled, fostering security awareness, and inclusivity and ensuring safety within smart home environments. The applications of artificial intelligence extend across various domains within smart home automation and environment. In-home security, artificial intelligence contributes to face detection [103], automated movement pattern detection [64], voice-controlled security systems [104], and anti-theft measures [105]. For smart home safety, artificial intelligence facilitates swift detection and notification of fire and smoke incidents [106]. Furthermore, artificial intelligence models and algorithms play a crucial role in efficient smart energy management, monitoring, and consumption [107, 108].

Similarly, recent advancements in smart home automation are evident in the integration of voice commands to control home appliances and the application of artificial intelligence techniques to rapidly process the generated data. This facilitates the extraction of valuable insights, unveiling hidden patterns that offer actionable insights for decision-making. Adopting a machine learning approach allows for the prediction of smart home occupants' behavior and preferences, as well as the estimation of future energy consumption during peak periods, tasks that are challenging for traditional methods of trend analysis.

Table 3 provides a comparative overview of recent publications on AI-driven and IoT smart home solutions. Numerous authors have documented recent advancements in the utilization of IoT and artificial intelligence in smart home designs, either through survey studies or experimental investigations, as outlined in the table. Artificial intelligence serves as the fundamental technology adopted to make smart home automation learn, adapt to personalized services, and make intelligent decisions. For example, natural language processing has impressively gained attention for AI-powered voice assistants in smart home automation.

3.1 Smart Home Automation Layers

The smart home automation system functions based on a layered architecture. Several layers, components, and infrastructures have been presented in previous research articles. For instance, Zaidan and Zaidan [124] presented an approach for achieving a home automation system. The approach encourages the use of intelligence by incorporating deep learning. However, cloud computing, which is an important aspect of SHAS, was left out. Also, Liu, Yang, and Liu [125] presented naming and profiling services for the IoT sensory environment. The service infrastructure includes naming addresses, networking, and storage. Their work proposed a middleware for easier integration of existing applications. However, cloud storage was not factored into their consideration. Based on this and extensive reading, we propose a six-layered architecture for an IoT smart home. The layers are the smart device and sensor layer, a communication and gateway layer, the intelligent management and decision layer, the cloud computing layer, the presentation and control layer, and the security layer. These six layers are detailed in the following subsections.

3.2 Smart Home Application Areas

A smart home can be described as a residence that is equipped with intelligent devices and automated services that offer users convenience, safety, comfort, and security while minimizing resource wastage. The advancements in smart home technology with the integration of IoT aims to enhance the overall quality of life, enable independent living for the elderly, provide a comfortable and convenient lifestyle for individuals with health conditions, reduce energy consumption, and offer other essential services that contribute to enhanced living comfort [45, 46]. To cater to the diverse needs of users and achieve these objectives, a smart home is divided into various application areas. Figure 6 provides a concise graphical representation of the taxonomy of these application areas. Additionally, Figure 7 illustrates a typical scenario showcasing different smart home application areas and their key functions. In the subsequent subsections, we will provide detailed explanations for each of these areas, highlighting their significance and the services they offer.

3.3 Smart Home Automation Communication Protocol

3.4 Smart Home Microcontroller

These microcontroller boards, along with sensors, relay boards, and connectors, form the foundation of smart home design, enabling communication, monitoring, and control of devices and sensors within the home [146].

3.5 Sensors

4.1 Security and Information Privacy

The smart home environment has sensors and devices that frequently generate data. Most data generated in home environments are private and, if attacked, can leak vital information about the user and his home [150]. Security of data and information transmitted over the channels should be ensured. The security issues of smart home automation are network attacks, data security, data privacy, cybersecurity, and information security. Network and data attacks can either be passive or active. Passive attacks are discreet attacks that do not directly affect the system's functionality while data are being fetched from the system. Examples of such attacks are eavesdropping, spying, footprinting, and spying on networks. Active attacks are direct attacks on the network, leaving a trail or damage to the network or system. Active attacks include worms, denial of service attacks, malware, passwords, and phishing [151].

Data privacy is another challenge mitigating against users of IoT smart home systems. Most developed systems for home automation do not put measures in place for data handling and protection. Transmission of data in IoT home automation systems, healthcare, and energy management are there for justifiable reasons. However, sensitive data are involved and hence should be protected. Medical history data, passwords, and energy usage are information transmitted in a smart home environment. If attackers lay hold of such information, the user's life might be in danger.

To overcome security and information privacy challenges, we suggest that developers go beyond the security measures that come with the hardware used to integrate additional security measures to prevent attacks, breaches, stolen data, and privacy intrusion. Also, the users should ensure that the systems they are installing in their homes have authentication, authorization, confidentiality, secured access, and other security mechanisms incorporated into them. The government can also assist by setting up safety laws and rules governing the deployment and home automation systems. A framework termed EclipseIoT presented by Anthi et al. [134] also provides solutions to security attacks and heterogeneity in smart home automation.

4.2 Internet Connectivity

Smart home technology, enabled by the Internet of Things, relies heavily on a robust and reliable Internet connection to operate effectively and fulfill the needs of end users. Nonetheless, in societies where access to Internet services remains a luxury, realizing the full potential of IoT smart home automation becomes increasingly distant. Challenges such as poor Internet connectivity or restricted access to Internet services may arise due to various factors, including insufficient or inadequate network infrastructure, unreliable electricity supply, and the high cost of subscribing to available services, among others [118].

4.3 Incompatibility of Devices

Developers use a variety of hardware from diverse manufacturers to design and develop home automation systems. Therefore, most home automation systems are not scalable, interoperable, and ubiquitous. The incompatibility of devices also leads to communication and connectivity issues, as most IoT manufacturers adopt different manufacturing hardware standards [152]. The inability of a system not to be able to communicate with devices from different manufacturers can limit the growth of home automation systems and lead to a high cost of the system for the users. To overcome the challenges associated with device incompatibility, developers can create an avenue for middleware in the architectural design of smart home systems as proposed by authors in [153, 154] for solving heterogeneity in sensors, devices, and networks of smart home automation.

4.4 High Energy Consumption

Smart home environments are filled with sensors, actuators, and devices requiring a power supply. This, in turn, will affect the home's energy consumption level. Efficient smart home automation is always expected to be available and controlled. Connection of all sensors, appliances, and devices to the home and use during peak periods will pose a challenge of high energy utilization and defeats one of the aims of a smart home automation system (energy efficiency). To overcome this challenge, we propose using solar systems or batteries as a source of energy for some of the devices to reduce the load on the energy. Also, the use of machine learning algorithms for load balancing could be incorporated into home automation systems. Developing scheduling algorithms or processes can also enhance energy consumption in smart home applications. Table 6 shows challenges identified with application areas of smart home automation.

4.5 Computational Resources

Recent innovations in smart home automation embrace artificial intelligence techniques for comprehensive processing of the sensed data. This approach allows learning, adaptation, and personalized service delivery based on the users' preferences thereby guaranteeing inclusivity for all users. However, the implementation of artificial intelligence techniques requires sufficient computational resources for high performance. These resources include high-computing power, sufficient storage space, stable electricity, and supporting core network infrastructure. These resources are fundamental for the successful integration of AI technology in smart home applications. To overcome these challenges, optimized AI algorithms that leverage cloud resources can be considered for implementation in SHAS, especially in low-computational resource regions.

4.6 Integration With Existing Infrastructure

Older homes were not designed with the integration of modern technologies in sight. Retrofitting these homes with modern devices and other home appliances to achieve a smart home automation system can be challenging. As identified in Section 4.3, the challenge of incompatibility of devices is partly occasioned by the difficulty in integrating modern smart devices in older homes because of the notable lack of features and devices that the older homes are designed with. In realizing the full potential of smart home devices, improved efforts in collaboration should be promoted among smart device vendors and home designers for a seamless and user-friendly smart home system.

4.7 Standards and Regularizations

The lack of standard protocols for communication and regularizations of smart devices that are utilized in designing SHAS does not favor the interoperability of smart home devices. In large-scale smart automation systems such as industrial automation and smart cities, establishing industry standards and regulations that guarantee data privacy, security, and interoperability is essential in building the trust of end users on smart home applications. Key stakeholders from the industry, regulatory bodies, and researchers play a crucial role in the early stages of designing, developing, and implementing these standards effectively. Moreover, such collaborations can derive innovations while addressing specific challenges that are related to compatibility, security, and user satisfaction in smart home ecosystems.

4.8 Lack of User Education and Supports

Prospective users of SHAS may not have a basic understanding of smart home systems and their potential. It is difficult for intended end users of any innovative system to believe in the functionality of the system if adequate information on the system's usage is not provided. In low-resource regions where technological developments evolve, the gap in user education and support hinders the development of SHAS in such regions. Creating awareness and providing relevant information to the target users of smart home applications can facilitate the decisions of users to embrace the technology. Smart home automation systems play critical roles in the realization of smart cities. Relevant government authorities and other stakeholders in the advancements of modern technologies for improved service delivery should prioritize prospective users' education and support frameworks that facilitate users' acceptance of applications such as SHAS.