Smart Homes: A Meta-Study on Sense of Security and Home Automation

Abstract

This review examines advancements in smart home security through the integration of home automation technologies. Various security systems, including surveillance cameras, smart locks, and motion sensors, are analyzed, highlighting their effectiveness in enhancing home security. These systems enable users to monitor and control their homes in real-time, providing an additional layer of security. The document also examines how these security systems can enhance the quality of life for users by providing greater convenience and control over their domestic environment. The ability to receive instant alerts and access video recordings from anywhere allows users to respond quickly to unexpected situations, thereby increasing their sense of security and well-being. Additionally, the challenges and future trends in this field are addressed, emphasizing the importance of designing solutions that are intuitive and easy to use. As technology continues to evolve, it is crucial for developers and manufacturers to focus on creating products that seamlessly integrate into users’ daily lives, facilitating their adoption and use. This comprehensive state-of-the-art review, based on the Scopus database, provides a detailed overview of the current status and future potential of smart home security systems. It highlights how ongoing innovation in this field can lead to the development of more advanced and efficient solutions that not only protect homes but also enhance the overall user experience.

1. Introduction

Over the past few decades, significant advancements have been made in technology, particularly in the use of technology in the home. Nowadays, virtually all modern activities involve the use of Internet-related services. In recent times, the Internet of Things (IoT) has become increasingly prevalent in our lives. Technological environments are created inside the home. This is accomplished by utilizing the Internet and smart devices. Along with social change, the development of technology has changed the concept of housing. New technologies appear, such as IoT smart devices like tablets and smartphones [1]. A smart home is an Internet of Things technology-based system that connects and remotely or automatically controls smart devices. In the case of home security, IoT technology can be adopted to make home security systems smarter, safer, and automated [2].

The use of smart devices has made it possible to work on home security from various perspectives. Several studies about security in smart homes have been carried out. In the work of Arar et al. [1], an analysis is proposed to understand the preferences and needs of smart home technologies, aiming to comprehend the behaviors of elderly users and the factors affecting the acceptance of technology. Similar to this work, Yao et al. [3] examined the privacy concerns of people regarding smart homes and their desired ways of mitigating these concerns; their focus was primarily on the end users or device owners. Prathima and Prasad [4] depict the plan of a push-home security structure. In this strategy, passage availability has been controlled from the perspective of the guest character by considering the human improvement zone and remotely monitoring the progress. And in the work of Chen et al. [5], video cameras are used to identify humans and differentiate potential risks.

However, Wilkowska et al. [6] examine the acceptance and privacy perceptions of a video-based technology for lifelogging in home environments among German and Turkish users, using a multi-method empirical research approach. It is one of the few works that focuses on the perception of security and the use of video cameras. Uddin et al. [7] seeks to better understand the different types of home environmental sensor-based monitoring technologies for the elderly. It is a work focused on older people, but it aims to analyze how users perceive the security of their homes. Padilla et al. [8] proposed a level-based visualization scheme to provide visual privacy when human intervention is necessary, such as in telerehabilitation and safety assessment applications.

Generally, the articles found in the literature discuss security in smart devices, health security for the elderly, and operational safety. Some documents discuss devices that make a house a smart home.

Despite the rapid growth in the development and deployment of smart home technologies, particularly those related to security and automation, the existing literature tends to focus predominantly on technological capabilities or device-specific implementations. However, there is a noticeable gap in studies that integrate user perception, privacy concerns, and adoption behavior into the evaluation of smart home security systems. This lack of interdisciplinary synthesis limits our understanding of how individuals experience and adopt these technologies in real-world domestic contexts.

To address this gap, the present document aims to provide a comprehensive review of the literature, guided by the following research questions:

• What are the dominant technological approaches to smart home security, and how are they integrated into modern home ecosystems?
• How do users perceive the safety, reliability, and intrusiveness of these systems?

Through this approach, we aim to provide a more comprehensive understanding of smart home security by integrating technical advancements with human-centered considerations.

This review mentions the importance of working on the following:

• The perception of security of people who live in smart homes.
• Difference between the perception of security with technology and the concept of technological adoption.
• The importance of using smart devices such as video cameras to monitor home security.

The rest of this review is organized as follows. Section 2 presents a literature review. Section 3 provides an in-depth description of the smart home ecosystem. Section 4 presents a discussion about the findings of the literature review. Conclusions are finally presented in Section 5, along with recommendations for future research.

2. Literature Review

The search was conducted in the Scopus database using the keywords: smart home, security, technology, home automation, perspective, and video camera. Scopus was chosen as the primary database for this review due to its broad interdisciplinary coverage, rigorous indexing standards, and inclusion of high-impact, peer-reviewed publications. Its advanced filtering tools enabled a focused and comprehensive search, ensuring the selection of relevant and up-to-date studies across domains such as Computer Science, Engineering, Social Sciences, and Environmental Studies—fields directly related to smart home security.

To guide the search process, the keywords were selected following an iterative approach. Initially, a preliminary scan of recent publications was conducted to identify recurring terms in titles, abstracts, and author-defined keywords related to smart home security. Based on the relevance and conceptual coverage, a refined set of terms was then established to ensure inclusion of the literature addressing not only technological foundations but also user perception and surveillance components.

The advanced query performed in the Scopus database was as follows: smart-home AND security AND technology AND home-automation AND perspective AND video AND camera AND (LIMIT-TO (DOCTYPE, “ar”) OR LIMIT-TO (DOCTYPE, “re”)) AND (LIMIT-TO (SUBJAREA, “COMP”) OR LIMIT-TO (SUBJAREA, “ENGI”) OR LIMIT-TO (SUBJAREA, “SOCI”) OR LIMIT-TO (SUBJAREA, “PSYC”) OR LIMIT-TO (SUBJAREA, “ENVI”)). The criteria used for the selection of works were based on the PRISMA methodology [9] and can be seen in Figure 1.

Disruptions to the sense of security stemming from potential criminal acts trigger physical transformations in urban morphology, including the closure of ways, the installation of protective grills on houses, and the increased use of alarms, guard dogs, and other [10]. On the other hand, it also leads to changes in human behavior, as individuals alter their usual routes or avoid specific areas and times. People increasingly retreat into their homes, gradually abandoning public spaces. Could it be that perceptions of security reshape human behavior? How do we utilize technology as a tool to improve our surroundings and create a sense of reassurance in environments marked by insecurity? Might there be a correlation between technological integration—specifically smart home systems—and perceived security? Is it possible that the greater the technological implementation in housing, the higher the sense of security experienced by its residents? Questions such as these may contribute to generating evidence that links perceptions of residential security in urban areas with the adoption of home automation systems. Such evidence could serve as the foundation for designing appropriate urban habitats using suitable technologies and optimizing the resources required for implementation, ultimately encouraging the integration of these technologies into daily life.

As part of this broader research objective, a meta-study on sense of security and home automation may be conducted. A bibliometric analysis of 82 studies published between 2013 and 2025 and indexed in Scopus suggests that China and the United States have produced the most papers on security in smart homes, with 13 each. In second place is the United Kingdom with 12 papers, and India ranks third with 11. The most popular research area is Computer Science (65 articles—32.8%), followed by Engineering applications (45 articles—22.7%), and general reviews (28 articles—33%). The most studied areas are IoT applications among smart devices (39 articles—47%), home (23 articles—28%), home healthcare (17 articles—20%), and education (1 article—1%).

Based on an analysis using VOSViewer software of the keywords suggested by the authors to represent their articles, the most commonly occurring keywords were automation, smart homes, intelligent buildings, and home automation. In this analysis, it is possible to observe that there is little concurrence between words such as human, security, and monitoring.

To construct Figure 2, a keyword co-occurrence analysis was conducted using VOSviewer version 1.6.20, based on a bibliographic dataset retrieved from the Scopus database. The dataset comprised 82 documents identified through a structured search that employed a predefined set of keywords. These keywords were selected through an iterative refinement process that examined the relevance of terms across the recent literature.

Author-defined keywords were extracted from the selected articles to identify patterns of conceptual association. Before analysis, duplicate or semantically redundant terms were removed, and a minimum occurrence threshold of 17 was established to ensure that only the most salient keywords were included in the final network. The co-occurrence relationships were mapped using the association strength method and visualized via the modularity clustering technique. In the resulting figure, node size reflects the frequency of keywords, while the distance and thickness of edges denote the strength of co-occurrence between terms.

A significant challenge in this domain is that the majority of the existing literature focuses on studies of IoT and automation. It is worth noting that numerous studies focus on home security through smart devices, while others concentrate on automating home processes. The vast majority of these studies examine the technological aspects, rather than the human relationship with home security. Some studies focus on the adoption of security technology, but not on users’ perceptions of security. We believe that the integration of home automation systems in residential spaces could significantly enhance residents’ perception of security, compared to perceptions in the absence of smart home technologies.

Figure 2 is divided into two distinct sections, both of which aim to represent concepts related to the IoT visually. In Figure 2a, a network graph is displayed, illustrating a complex structure of interconnected nodes. The main nodes are highlighted in distinct colors: blue for “automation”, green for “home automation”, and red for “Internet of Things”. Each connection in the network graph represents a conceptual relationship between terms, enabling the visualization of how ideas are interwoven within the IoT ecosystem. For instance, “home automation” may be linked to concepts such as sensors, remote control, or energy efficiency, whereas “automation” is associated with industrial processes or artificial intelligence. This representation facilitates the understanding of the complexity and interdependence of the elements that constitute the IoT domain.

In Figure 2b, a word cloud is presented to reinforce the concepts depicted in the network graph. The largest and most central terms, including the Internet of Things, automation, and smart homes, indicate their prominence or frequency. Surrounding these are more specific terms such as intelligent buildings, energy efficiency, and wearable devices, which broaden the thematic scope.

Together, the two visualizations provide a comprehensive overview of the IoT ecosystem, emphasizing both the conceptual interconnections and the relevance of key terms. This visual tool is handy for understanding the interrelationships between automation, smart homes, and other emerging technologies within this field.

Figure 3 presents a visualization of the number of documents published over time. A variation in publication volume is observed from 2013 to 2025, allowing for the analysis of trends in the research line’s production. This information is valuable for assessing growth in technological study areas and the impact of new methodologies or technologies on knowledge generation. Throughout the analyzed period, fluctuations in the number of published documents are evident. In certain years, a significant increase is recorded, which may be attributed to technological advancements, increased research funding, or even changes in academic policies. Conversely, other years reflect a decline in publication volume, possibly due to factors such as economic crises, shifts in research priorities, or difficulties in data collection and analysis.

Figure 4a presents a distribution of publications by subject area, providing a clear overview of academic output. It is observed that the discipline with the highest number of documents is Social Sciences, followed by Computer Science and Engineering, indicating a growing focus on research related to technology and society. Identifying patterns in document publication can help researchers and academics anticipate future developments and collaboration opportunities across various disciplines.

Figure 4b presents a distribution of academic publications by country or territory, highlighting regions with the highest scientific output. It is observed that countries such as China, the United States, the United Kingdom, and India exhibit a high concentration of published documents, reflecting their substantial investment in research and development within this field. These data allow for the identification of leading nations driving knowledge generation. Additionally, this distribution provides insights into potential global research collaborations.

3. Smart Home Ecosystem

Today, technology has impacted many aspects of our lives, including our homes. Devices are developed to meet our needs within the home. These devices typically enter the market for users to purchase. The governments of each country normally regulate these types of devices. Each country often encounters challenges in these regulations. Such regulations, as mentioned by Prabowo et al. [11], focus on the technological adoption of systems and the challenges governments often encounter. The integration of advanced technologies into cities is a key component of the transition to smart cities. Alabdali et al. [12] aim to improve the quality of life of their inhabitants. Gharaibeh et al. [13] include devices with the IoT to generate data that can be used to improve services such as mobility, energy management, and the management of various resources. The benefits of implementing these devices are directly related to the environment and, more directly, to the economy of each city. The aim of incorporating these technological solutions into cities is to ensure that the benefits are accessible to all sectors of society.

Smart cities, like smart homes, have been closely linked with IoT technologies playing a central role in both domains [14]. Both concepts aim to enhance the quality of life by utilizing technological devices. The devices appearing on the market are the result of the evolution of the smart home concept [13,15] throughout history. Owens et al. mention this in their work, where 355 definitions have been found in related works [16]. In the same way that these concepts have been explored throughout history, smart homes have also sought to impact ecological practices [17], promote sustainable practices, and improve resource utilization. These devices can help improve the environment in various areas, such as those mentioned by Lakshman et al. [15], where agriculture, health, and safety are key aspects of these devices’ focus.

The evolution of IoT has progressed significantly, expanding from its initial definitions and applications in smart cities to broader implementations in smart homes. The idea of connecting devices via the Internet sounds so familiar to us that it is hard to imagine life without them. The first devices focused on industrial automation [18], where process information was collected and then sent for analysis. These were the first attempts to standardize these developments, which were hindered by limited data processing and storage capacity. This has evolved thanks to the development of new technologies, new ways of processing information, and storing data in the cloud. Nowadays, the number of devices has grown exponentially, but they have also entered different fields, not only in industry but also in homes. Today, devices are not limited to just collecting data; they can also make decisions to improve the user experience and security.

However, the increase in devices has also heightened concerns about user security and the privacy of information, so efforts must be made to protect information and ensure the trust of those using the devices. Technology has come to change our lives, as we have seen, from seeking improvements focused on the city to directly addressing smart homes. However, any technological change comes with social changes, which can define the way new technology is adopted and designed. Studies such as that by Garg and Cui [19] discuss the experience of using IoT in smart homes through interviews with users before and after using the technology. It is worth noting that a direct perception approach is not employed; instead, the document focuses on the adoption of the technology.

3.1. IoT Devices

IoT devices are changing the way we interact with our surroundings. These devices collect, process, and share information via the Internet. These devices are designed to improve the efficiency of the methods they serve or for which they were intended.

The implementation of IoT devices in different areas has been a driving force behind the development of diverse research projects on these devices. Other types of processing, applications, and technologies have been developed using these IoT devices.

The most recent publications refer to innovative platforms that, based on user usage scenarios, can improve the operability of their systems [20,21]. Although these devices are based on IoT, new applications [22] and new forms of information processing that allow better use of resources have been proposed, as is the case with the work of Syu et al. [23], who used artificial intelligence (AI) to control the electrical consumption of the devices, or the work of Ahmad et al. [24], where a methodology is proposed to reduce latency and energy consumption in IoT devices. In their works, Yadav et al. [25] and Kong et al. [26] proposed IoT applications that require short responses in time and consequently can control their energy consumption.

In the works of Vestias et al. [27] and Domingo [28], deep learning is utilized as part of the algorithms for information processing. In the work of Atitallah et al. [29], they use deep learning to analyze the information generated by IoT devices. Cerruela et al. [30] conducted a series of studies that utilize the Bluetooth protocol to provide a solution for managing big data generated by IoT devices, such as energy consumption. In the work of Shi et al. [31] and that of Zhang et al. [32], the trend of utilizing edge computing for improved device information processing is evident, and current research also explores fog computing as a complementary approach to edge computing in IoT environments [33].

Some devices are focused on health improvement and care [34,35]. Irfan et al. [36] proposed an algorithm that reduces the amount of medical history information that can be shared on the Internet. Other articles focused more specifically on the COVID-19 pandemic [37] and its influence on these devices to help combat the spread of the virus. Some others focused on generating a strategy that allows energy efficiency [38] in smart buildings or efficiency in some other devices such as computers [39]. Different smart devices have been developed for educational purposes, as seen in the work presented by Abichandani et al. [40].

As we can see, there are numerous benefits and applications where IoT-based devices can be applied, and this also entails significant challenges, especially in terms of security.

3.2. Smart Home with IoT

We have seen IoT devices emerge over the years in response to various needs. In this review, we analyze advancements in smart home security through the integration of home automation technologies. Smart homes offer several benefits, including comfort and personalization, allowing users to control various aspects such as lighting, room temperature, and appliance usage. These types of actions not only make daily life easier but also create an adaptive environment for users.

Table 1. Summary of reviewed works related to smart home and IoT applications.

Title	Year	Purpose	Methodological Design	Technological Tendency
[41]	2019	Integrate human-centered approaches in smart buildings	Literature review on technologies optimizing building-user interaction	IoT/AI
[42]	2021	Review security in smart IoT-cloud systems	Case study-based analysis of vulnerabilities and mitigation strategies	IoT/Cloud computing
[1]	2021	Understand elderly users’ needs for smart homes	Survey on preferences such as fall detection and health monitoring	Smart sensors
[43]	2021	Analyze trust and privacy in automation adoption	Quantitative study on automation acceptance and data handling	Smart home technology
[25]	2021	Comprehensive review of multimodal HAR systems	Classification method analysis, applications, and future research	IoT/ML/DL
[44]	2022	Enable remote diagnosis and close monitoring via IoT	Reviews non-invasive health monitoring and challenges in healthcare informatics	IoT/Deep learning
[45]	2022	Develop intelligent living system for elderly monitoring	Fall detection and indoor localization with a cost-effective design	IoT/Camera
[46]	2022	Address user authentication in IoT systems	Literature review of schemes like biometrics, web-based, and hardware-based methods	IoT
[47]	2022	Model home energy consumption non-invasively	SHEMS using GPU-accelerated neurocomputing for forecasting	Smart Home Energy
[48]	2022	Study WSN and WMN integration in IoT	Literature review of mesh and sensor network interconnection	IoT/Mesh topology
[49]	2023	Review IoT in home automation and healthcare	Highlights wearable devices and information security in recent research	IoT
[50]	2023	Reveal potential of OWC-based IoT networks	Top-down survey with comparative domain analysis and hybrid tech integration	IoT
[51]	2024	Review the applications of Raspberry Pi in various domains	Explores its use in studies on home automation, agriculture, healthcare, and industrial control	Raspberry Pi
[52]	2024	Explore the usage and impact of IoT-based smart home technology	Semi-structured interviews focusing on remote control, surveillance, sensing, entertainment, and communication	IoT
[53]	2024	Analyze the MQTT protocol and related security mechanisms	Discusses application-layer challenges and prospects for securing MQTT-based IoT systems	IoT
[54]	2024	Examine the role of trust and privacy in ambient health tech	Online survey of individuals needing healthcare assistance	Privacy and security

provides a summary of reviewed works related to smart home and IoT applications, highlighting key advancements and research directions in this field. It is in chronological order for ease of use. The studies analyzed focus on various aspects, including automation, security enhancements, energy efficiency, and user interaction within smart environments. Many of these works emphasize the role of IoT technologies in improving home management through interconnected devices, allowing seamless communication between sensors, actuators, and cloud-based systems. Additionally, the summary outlines methodologies for integrating artificial intelligence and machine learning to optimize security and predictive analytics in smart home applications. By examining these contributions, the table offers valuable insights into the evolution of IoT-based residential systems and the potential future developments aimed at improving their efficiency, adaptability, and security.

3.3. Smart Home Security

Conducting security studies in a smart home is crucial to ensure the protection of residents and their most valuable assets. Efforts have been made to identify connected devices and their integration with users. In this section, we will review the security studies found in the state-of-the-art literature that are related to smart homes.

In the study by Erlina and Fikri [2], a monitoring system for small businesses in Indonesia is presented, utilizing a single-board computer and the YOLOv4-tiny algorithm to identify and categorize visitors as human or non-human. With an accuracy of 89.21%, the system greets human visitors with a speaker and notifies the establishment owner via a Telegram bot, differentiating between potential customers and intruders. The work by [55] systematically reviews the applications and user perceptions of monitoring devices, differentiating between wearable and non-wearable devices. The perceptions were based on utility, ease of use, and privacy.

The works of Simões, Wang, and Cheng et al. share a common focus on using advanced technologies to enhance the quality of life for their users, particularly in terms of security. The work of Simões et al. [56] focuses on indoor positioning systems to assist visually impaired individuals, improving their navigation and localization in complex environments. Wang et al. [57] address home automation systems and the security challenges associated with implementing IoT technologies, emphasizing the importance of protecting users from cyber threats. Cheng et al.’s research [58] proposes a smart home solution to monitor and improve the well-being of dogs, utilizing emotion recognition and communication between pets via smartphones. All these studies highlight the integration of innovative technologies to create safer, more efficient environments tailored to the specific needs of their users.

Oguntala et al. [59] evaluate various indoor positioning technologies, such as Wi-Fi, RFID, and Bluetooth, which are crucial for IoT applications. Security is addressed by assessing the robustness and reliability of these technologies, ensuring that localization systems are accurate and secure for use in critical environments. Garg and Cui [19] examine how smart devices can adapt to social contexts within the home, highlighting the importance of security in the interaction between these devices. The focus is on resolving user conflicts and providing appropriate information to maintain security and privacy in the home. Putrada et al. [60], although primarily focused on energy efficiency and user comfort, also considers security by utilizing activity recognition techniques. Proper identification of activities can contribute to home security by detecting unusual or potentially dangerous behaviors. Surantha et al. [61] propose a security system that utilizes the YOLO V3 algorithm, as updated in [2], to detect intruders with an accuracy of 98.58%. The system sends real-time notifications to the homeowner, significantly enhancing security through rapid detection and response to potential threats.

Hsu et al. [62] propose a system that uses sensors installed above the stove to detect flames, high temperatures, or gas leaks, activating a device that cuts off the gas supply. Additionally, an audible and visual alarm alerts residents, while a notification system sends messages via the Internet and unlocks the front door to allow entry for relevant personnel. An IP camera enables residents to monitor the gas stove from their mobile phones and cut off the supply if necessary. Meanwhile, Purboyo et al. [63] analyze home security as a system that provides comfort and protection to inhabitants and can prevent crimes. The study reviews and compares different security systems to determine which is most suitable for the home.

Uddin et al. [7], however, focus on elderly care at home, utilizing non-wearable environmental sensors to monitor the resident’s activities and environment in real-time, with sensors that detect motion, pressure, video, object contact, and sound. This helps create a safe space within the home. The interaction of elderly individuals is emphasized, knowing that a simple way to do this is through the use of voice commands, as mentioned by Moriuchi [64] and camera-based monitoring systems have also been evaluated for their acceptance among older adults [65]. Consumer motivation significantly influences the perceived value of voice-controlled smart technologies, with a primary focus on ease of use and utility. Studies show that these factors have a significant mediating effect on the relationship between motivation and perceived utilitarian value. Prathima and Prasad [4] found similar results. This article depicts the plan of a push-home security structure. In this strategy, passage availability has been controlled from the perspective of the guest character by considering the human improvement zone and remotely monitoring the progress. This leads to the concept of a smart home as discussed in the review conducted by Batalla et al. [66], which systematically examines the challenges faced by such environments, particularly the limited capacity of small sensors and system heterogeneity. The study compiles current practices and outlines future directions aimed at improving the management and security of smart homes, highlighting the role of big data integration and cloud computing in daily applications.

Quadruped robots, as presented by Owoeye et al. [67], represent a promising advancement in home security and automation. Equipped with high-definition cameras, motion sensors, and object recognition software, these robots can detect intruders, track their movements, and capture real-time video for remote monitoring. They operate autonomously, thanks to advanced artificial intelligence algorithms that detect anomalies and respond quickly to security threats. Additionally, integration with smart home systems allows seamless communication with other connected devices.

In the article by Prajapati et al. [68], a method for a perimeter security system is proposed, utilizing specific data rates and signal formats for authorization and recognition functions. This system can manage security and monitor entry in large peripheral areas.

These systems, as identified in the systematic review conducted by Stoyanova et al. [69], exhibit several challenges that hinder their seamless integration into security frameworks.

Although IoT data can be a rich source of evidence, forensic professionals face various challenges, including the diversity of devices and non-standard formats, as well as the complexities of multi-tenant cloud infrastructure and multi-jurisdictional litigation. Despite the potential richness of IoT data as evidence, forensic professionals encounter diverse challenges, including the variety of devices, non-standard formats, multi-tenant cloud infrastructure, and multi-jurisdictional litigation.

Due to these challenges in security and legality, users are often reluctant to accept smart technologies in the home, as mentioned by Nascimento et al. in their article [70]. The increasingly affordable new smart technologies can transform homes into intelligent paradigms. This article identifies the key trends affecting user acceptance of these technologies, based on the UTAUT2 model. The results show that the constructs of ‘attitude’ and ‘performance expectancy’ are the most decisive for the acceptance of smart technologies in the home.

The ecosystems of smart devices, in their various forms of use, from legal, technological, and acceptance perspectives, often face common challenges and issues, as found in the works of Heartfield et al. [71], Albayaydh and Flechais [72], and Ye et al. [73] regarding cybersecurity in smart homes. The adoption of IoT technologies, cloud computing, and artificial intelligence has made smart homes more practical but also attractive targets for cyberattacks. The privacy of passersby in smart homes examines the design challenges of protecting the privacy of individuals who pass by. It provides recommendations to enhance data protection through technical, social, commercial, and legal interventions.

In Ahmad et al.’s work [74], a home security system is described that uses passwords or fingerprints to allow access to family members. At the same time, a hidden camera captures images of visitors and sends email alerts. The system enables remote control of door accessibility and surveillance via a smartphone, ensuring home security through visitor identification and authorization. On the other hand, Chaparro et al. [75] focus on remote healthcare, highlighting the importance of data transmission security and patient privacy protection. Advanced technologies and technological paradigms play a crucial role in enabling remote healthcare and assisted living, addressing potential challenges and proposing solutions to improve the security and efficiency of the healthcare system. Meanwhile, Sidiropoulos et al. [76] review feature extraction methods applied to finger vein recognition, a biometric system that enhances security through the unique identification of individuals. The research highlights the growing interest in these biometric systems and the application of convolutional neural networks to improve accuracy and security in identification. Blythe and Johnson [77] present a comprehensive review of the security vulnerabilities linked to the Internet of Things (IoT), systematically identifying the mechanisms by which malicious actors exploit connected devices. The review categorizes a range of crimes facilitated by IoT technologies, including theft, harassment, sexual offenses, and even state-level threats.

The mentioned articles share several common points regarding home security and related technologies. All the articles highlight the use of advanced technologies such as sensors, cameras, artificial intelligence, and connected devices to enhance security and efficiency in the home. The protection of privacy and data security is a recurring theme. Articles on remote healthcare and the IoT address the importance of safeguarding sensitive information and ensuring security in data transmission. The protection of privacy and data security is a recurring theme. Articles on remote healthcare and the IoT address the importance of safeguarding sensitive information and ensuring security in data transmission.

Video cameras have emerged as a fundamental technology in the development of smart homes, providing a wide range of applications from security and surveillance to home automation. This analysis examines various articles that explore the use of video cameras in smart home environments. As technology advances, the integration of video cameras in smart homes focuses not only on security but also on privacy and user acceptance. This analysis provides a comprehensive overview of trends in the use of video cameras in smart homes.

Table 2. Review of video and image processing technologies applied in smart environments.

Title	Year	Objective	Summary
[8]	2015	Ensure visual privacy in telerehabilitation applications.	Proposes a level-based visualization scheme for visual privacy in intelligent monitoring and safety assessment.
[78]	2022	Propose facial emotion-based security for smart homes.	Introduces a system with CMOS camera and PIR sensor using a deep fusion model (EmoFusioNet) for facial emotion-based threat detection.
[79]	2023	Evaluate privacy-preserving visualization modes for AAL technology.	Compares different visualizations in AAL; finds “Avatar” mode most effective for privacy while maintaining usability.
[5]	2024	Identify behaviors of interest using sound-steered cameras.	Proposes a new direction-of-arrival (DOA) algorithm to detect multiple sound sources. Uses a state-transition SVM for behavior identification and an MRF function based on localized contour sequence.
[80]	2024	Highlight the role of image processing in IoT for energy efficiency and security.	Reviews 36 publications on image processing in IoT. Proposes a mind map outlining solutions to achieve secure and energy-efficient systems.
[81]	2024	Develop an automation and surveillance system for smart homes.	Describes a system using a gateway for Wi-Fi/ZigBee conversion, with infrared sensors, motion detectors, and IP cameras for surveillance.
[6]	2024	Examine privacy and acceptance of lifelogging technologies.	Studies German and Turkish users’ acceptance of video lifelogging, emphasizing cultural influences on technology perception.
[82]	2024	Review smart city and video surveillance technologies.	Analyzes 213 articles on smart cities and surveillance, discussing technological challenges and future opportunities.

presents a comprehensive review of video and image processing technologies applied in smart environments, highlighting their role in enhancing automation, security, and user interaction. The studies analyzed in the table explore various techniques, including object recognition, facial detection, and behavior analysis, which contribute to real-time monitoring and decision-making in intelligent systems. Additionally, the reviewed works examine the integration of artificial intelligence and deep learning for improved accuracy in image processing, enabling more effective threat detection and adaptive responses in security applications.

The first group of articles [5,80,81] focuses on technical and infrastructural aspects aimed at enhancing the functionality of smart environments. The study by Chen et al. [5] introduces a direction-of-arrival (DOA) algorithm for detecting multiple sound sources, complemented by machine learning techniques such as Support Vector Machines (SVMs) and Markov Random Fields (MRFs) to identify behaviors and events. Al-Ghaili et al. [80] present a systematic review of 36 studies on image processing in IoT, highlighting its role in energy efficiency and security, and propose a mind map that synthesizes the most common detectors and algorithms. Meanwhile, Ali et al. [81] propose a communication architecture based on Wi-Fi and ZigBee to interconnect sensors and actuators in home automation systems, representing a significant advancement in device interoperability.

The second group of articles [6,79] addresses the social and ethical dimensions of these technologies, particularly in relation to privacy. Wilkowska et al. [6] examine the acceptance of continuous video recording technologies in Germany and Turkey, revealing cultural and personal concerns regarding privacy. In contrast, Offermann et al. [79] focus on data visualization in active assisted living (AAL) technologies, comparing various methods and concluding that the “avatar mode” is the most accepted by older adults, as it balances privacy and usability.

Kaushik et al. [78] introduce an innovative approach by integrating facial emotion recognition with passive infrared (PIR) motion sensors to enhance security in smart homes. Their method employs a combination of convolutional neural networks (CNNs) and Gaussian Mixture Models (GMMs) within a deep fusion framework, enabling the detection of threats based on facial expressions. This proposal represents a convergence of artificial intelligence, computer vision, and home security.

Padilla-López et al. [8] focus on visual privacy in tele-rehabilitation applications, an emerging field that has gained relevance with the rise in remote healthcare services. Their proposed visualization method aims to protect patient identity and privacy during remote rehabilitation sessions, which is crucial for fostering the adoption of these technologies in clinical settings.

Finally, Sharma and Kanwal [82] provide a macro-level perspective by reviewing 213 publications on smart cities and video surveillance systems. This work identifies key technological challenges and suggests future research directions, making it a valuable reference for the development of data-driven public policies and urban strategies. Collectively, the table offers a rich and diverse overview of the current state of research in IoT, privacy, automation, and smart cities. This is particularly relevant in the context of Ambient Assisted Living initiatives supported by European funding [83].

4. Discussion

The integration of IoT devices in smart homes has significantly transformed residential security by enabling automation, real-time monitoring, and intelligent threat detection. These innovations contribute to enhanced comfort and efficiency while also introducing new security and privacy challenges. IoT-based security systems, such as surveillance cameras and motion sensors, provide an additional layer of protection, yet their connectivity increases exposure to potential cyber threats. Ensuring robust encryption, secure data transmission, and privacy-aware implementation is crucial to mitigating vulnerabilities associated with interconnected smart devices. As smart homes become increasingly advanced, addressing these challenges will be essential in maintaining user trust and ensuring the long-term adoption of these technologies.

In Figure 5, main concepts such as cloud computing, edge computing, machine learning, deep learning, IoT, wireless sensor networks, digital storage, and automation are presented. Each of these technologies contributes to enhancing surveillance, threat detection, and response to security incidents in residential environments. The interconnectivity between smart devices enables efficient, real-time management of security systems, providing increased protection and convenience for residents.

Among these technologies, IoT and wireless sensor networks play a fundamental role in integrating security devices such as smart cameras and motion detection systems. Their implementation strengthens home security automation, ensuring proactive threat monitoring and seamless communication between interconnected systems.

Additionally, Cheng et al. [58] argue that trust and perceived risk are pivotal in determining how users engage with home automation technologies. These dynamics are especially relevant when such systems are entrusted with safety-related functions, and they support the review’s emphasis on psychological and social factors influencing adoption.

The importance of aligning technologies with specific user groups is also underscored by Arar et al. [1], whose research highlights how older adults’ preferences and security perceptions shape their interaction with smart home solutions. Their findings help contextualize the discussion on user diversity and the need for inclusive design approaches.

Finally, Simões et al. [56] identify a lack of interdisciplinary methodologies in previous studies, reinforcing the need for integrated approaches that consider both technical performance and user experience. This observation complements the review’s call for future research that bridges technological development with social and perceptual dimensions.

One of the main advancements in home security is the development of AI-powered surveillance cameras that incorporate facial recognition, object identification, and behavioral analysis which are part of a broader evolution from video surveillance to activity recognition and health monitoring [84]. These systems utilize deep learning algorithms to distinguish between normal and anomalous events, thereby effectively reducing false alarms triggered by routine activities, such as pet movements or passing vehicles. By continuously learning from behavioral patterns, AI-enhanced surveillance systems adapt to evolving security needs, providing context-aware threat detection with increased precision and efficiency. In addition to improving accuracy, these systems contribute to proactive security management, allowing homeowners to receive alerts and take preventive measures before incidents occur.

Another pivotal innovation in smart home security is the interconnectivity of surveillance cameras with other IoT devices, fostering a cohesive security ecosystem. Cameras can synchronize with door and window sensors, lighting systems, and automated alarms, enabling real-time responses to potential threats. For instance, upon detecting movement in restricted areas, the system can automatically activate exterior lights, trigger alarms, or notify homeowners remotely. Furthermore, the incorporation of cloud-based storage ensures secure and accessible data archiving, allowing users to review footage from anywhere while minimizing the risk of physical data loss or unauthorized access. Despite the rapid advancement of emerging security technologies, such as IoT-based surveillance systems, artificial intelligence-driven threat detection, and automated response mechanisms, they often fail to consider how users perceive security within their homes. The presence of advanced security features does not necessarily translate to an increased sense of safety for homeowners. Many individuals may still feel vulnerable due to factors such as system complexity, lack of transparency in data handling, and concerns over cyber threats. Without addressing these aspects, smart home security solutions risk being underutilized or even mistrusted by users, diminishing their overall effectiveness.

The adoption of home automation systems is influenced not only by their technical capabilities but also by how secure users feel when interacting with them. A security system may provide real-time threat detection and automated responses. Yet, if homeowners perceive the technology as intrusive or unreliable, they may be hesitant to rely on it entirely. This gap between perceived security and system functionality highlights the importance of designing technologies that are not only efficient but also intuitive and reassuring to users. Without proper education, user-friendly interfaces, and clear communication regarding the reliability of these systems, security innovations may fail to foster a true sense of safety among residents. Furthermore, the absence of a direct focus on security perception in the development of smart home systems can lead to disparities in user engagement. Some individuals may feel overwhelmed by the technological complexity and opt for traditional security measures despite having access to more sophisticated solutions. Others may overestimate the effectiveness of automated security features and overlook essential protective measures, such as regularly monitoring access logs or updating security settings. Bridging this gap requires integrating human-centered design principles into emerging security technologies, ensuring that users not only benefit from advanced protection but also feel genuinely secure in their homes.

Despite these advancements, the use of surveillance footage in legal trials presents several limitations that may affect its admissibility and reliability. Legal considerations require that recordings be obtained lawfully without infringing upon individual privacy rights. Moreover, factors such as image quality, camera angles, and lighting conditions influence the accuracy of facial identification and event interpretation. The potential for video manipulation or subjective evaluation further complicates its role as definitive evidence. Addressing these concerns demands standardized regulations and enhanced verification mechanisms to ensure the credibility of video-based forensic analysis. As home security systems become increasingly reliant on data-driven approaches, establishing clear ethical guidelines and robust legal frameworks will be crucial in maintaining transparency and fairness in security applications.

The integration of IoT devices and AI-driven surveillance systems has fundamentally reshaped smart home security, enabling real-time monitoring, automated threat detection, and informed decision-making based on data-driven insights. These advancements provide homeowners with a level of security and convenience that was previously unattainable, allowing for intelligent systems that not only detect threats but also anticipate risks based on learned behavioral patterns. As a result, security frameworks are evolving from reactive mechanisms to proactive and adaptive systems, ensuring more effective protection against intrusions and unauthorized access.

Despite these technological improvements, the widespread adoption of smart security solutions is closely tied to user perception and acceptance. Security concerns, particularly regarding data privacy, unauthorized access, and potential cyber threats, influence how users engage with and trust these systems. Developers and manufacturers must prioritize the implementation of transparent security protocols, ensuring that data is encrypted, stored securely, and processed in an ethical manner. Additionally, educating users about best practices, such as configuring security settings and recognizing potential vulnerabilities, plays a crucial role in fostering confidence and encouraging broader adoption of these innovations.

Looking ahead, future advancements in smart home security are likely to focus on strengthening cybersecurity measures, refining AI-driven automation, and enhancing interoperability between security devices. Enhanced encryption methods, decentralized data storage solutions, and advanced threat detection models will be essential in mitigating risks associated with smart security networks. Furthermore, integrating adaptive AI algorithms that continuously learn and refine security responses will contribute to more sophisticated and resilient security frameworks. By seamlessly combining AI, IoT, and automation, smart home security systems will transition toward a more autonomous and self-regulating approach, minimizing human intervention while maximizing efficiency. As these technologies continue to evolve, security and ethical considerations must remain at the forefront of their development. Ensuring compliance with data protection regulations, fostering accountability in AI-driven surveillance, and promoting responsible implementation will be crucial in shaping a future where security innovations enhance home protection without compromising user rights. The success of smart security systems ultimately depends not only on their technical capabilities but also on how effectively they align with societal expectations of privacy, reliability, and ethical use.

5. Conclusions

The integration of IoT devices into home security has proven to be an effective solution for enhancing protection and surveillance. IoT-based security systems, such as surveillance cameras and motion sensors, provide an additional layer of security, enabling users to monitor their homes in real-time from anywhere.

The perception of security among individuals living in smart homes is shaped by multiple factors, including the reliability of automated systems, the level of control users feel over their environment, and concerns about cyber threats. While smart security devices, such as motion sensors, AI-enhanced surveillance cameras, and automated locks, are designed to provide enhanced protection, their effectiveness is not solely determined by their technical capabilities. Psychological aspects, such as user trust, ease of use, and the ability to remotely monitor security, play a crucial role in determining how safe residents feel in their connected homes. Without a clear understanding of system functionalities and risks, users may experience uncertainty, which can lead to hesitation in fully relying on smart security solutions.

The difference between security perception and the concept of technological adoption lies in the emotional and psychological factors that influence user behavior. Technological adoption refers to the willingness and ability of individuals to integrate new systems into their daily lives, often driven by convenience and perceived benefits. However, perception of security is more subjective and depends on personal experiences, trust in digital security measures, and awareness of potential vulnerabilities. A homeowner may adopt IoT security devices for their practicality but still feel uncertain about their effectiveness in preventing threats. Bridging this gap requires improved cybersecurity measures, user education, and intuitive system designs that instill confidence in both functionality and protection.

The use of smart devices such as video cameras is crucial in modern home security, as they provide continuous monitoring and real-time threat detection. Unlike traditional security measures, smart cameras integrate AI-driven capabilities such as facial recognition, object tracking, and automated alerts, enhancing situational awareness for homeowners. These devices not only deter potential intruders but also enable users to remotely verify security events, ensuring peace of mind even when they are away. Additionally, cloud-based storage solutions ensure video footage is securely archived, mitigating risks associated with tampering or physical damage to local storage units. As security threats become more sophisticated, incorporating advanced surveillance technologies ensures proactive defense against unauthorized access and enhances overall home protection.

Surveillance cameras have undergone significant evolution with the incorporation of advanced technologies, including artificial intelligence and machine learning. According to several studies reviewed, these innovations enable more accurate detection of suspicious activities, as well as facial recognition and object identification, thereby improving the efficiency and responsiveness of smart home security systems. This synthesis highlights a recurrent theme in the literature, illustrating how real-time data analysis is reshaping threat detection and emergency response in domestic environments.

User security perception is a crucial factor in determining the acceptance and success of IoT technologies in residential settings due to its strong intrinsic connection with the emergence and application of new technologies in relation to the human sense of safety and reassurance, as highlighted by the bibliographic analysis. If technological features can enhance this perception of security in the spaces we inhabit, the potential increases even further when these innovations enable new forms of control, monitoring, care, and protection of our belongings. To fully embrace such advancements, users must feel safe and confident in the protection these devices offer. Transparency in the implemented security measures and education on data handling are essential to foster trust and ensure successful adoption. The adoption of IoT devices in the home has a significant impact on users’ daily lives. From remote management of appliances to health monitoring, these devices offer greater convenience and control. However, it is also essential to consider the psychological and social impact of living in a highly connected and monitored environment and to ensure that technologies are used in ways that enhance quality of life without compromising privacy.

The future of smart homes with IoT is promising, with continuous advancements in artificial intelligence, machine learning, and communication technologies. Future homes are expected to be even smarter, with systems that can learn and adapt to the needs and preferences of users. The integration of emerging technologies, such as cloud computing and 5G, will also play a crucial role in developing more advanced and efficient solutions. Investigating user security perception can reveal concerns and expectations that are not always evident from a technical perspective. For example, while a surveillance camera may offer high resolution and advanced detection capabilities, users may feel uncomfortable if they are unsure about how their personal data is handled. By understanding these concerns, developers can design technologies that are not only technically robust but also address the emotional and psychological needs of users.

The findings of this review provide valuable guidance for professionals involved in planning, implementing, and regulating smart home security systems. By synthesizing prevailing technologies and user-focused considerations, this study presents a conceptual framework that enables decision-makers to align technical solutions with real-world expectations. Special attention should be given to the integration of user-friendly interfaces, transparent data practices, and responsive surveillance systems to enhance trust and usability in domestic environments.

A key limitation of this review lies in the scarcity of prior studies that directly examine the relationship between perceived security and smart home technologies. As a result, making direct comparisons or drawing parallels with similar research is inherently constrained. The review synthesizes findings from diverse domains, including surveillance technologies, user experience, and home automation; however, few existing works explicitly integrate these dimensions. This highlights both a gap in the literature and an opportunity for future interdisciplinary studies to deepen the understanding of how perceptions of safety influence and are influenced by domestic technological adoption.

Recent findings by Ghobakhloo et al. [85] provide a structured roadmap for understanding the adoption of Industry 4.0 technologies among SMEs, emphasizing the interplay between technological, organizational, and environmental factors. Although their study focuses on industrial contexts, the identified barriers, such as limited digital competencies, infrastructure gaps, and organizational resistance, offer valuable parallels for the smart home domain. These insights suggest that the successful integration of home automation and security technologies may similarly depend on users’ readiness, contextual conditions, and perceived value. Incorporating such multidimensional frameworks into future research could help bridge the gap between technological innovation and user-centered adoption in domestic environments.

Future studies may extend this work by conducting empirical investigations that assess how smart home security technologies are perceived and adopted in practice, particularly across diverse sociocultural and regulatory contexts. Building on the gaps identified in the literature, future research could explore the relationship between perceived security and smart home technologies from a social and anthropological perspective, considering factors such as cultural norms, household dynamics, and trust in technology. Such an approach would provide deeper insights into how individuals and communities construct meanings around security and how these perceptions influence the acceptance and use of home automation systems.

Research on security perception can guide the development of new technologies that are more intuitive and user-friendly. Users need to feel secure and confident in their ability to control and manage these technologies. By involving users in the design and development process, solutions can be created that are more aligned with their expectations and real needs. Ultimately, investigating user security perception not only enhances the acceptance of existing technologies but also drives innovation and the development of new solutions that truly improve the quality of life in smart homes.