Home Security Research Articles

Examining the effectiveness, feasibility, and acceptability of an advocacy program for Latinx families of transition-aged autistic youth.

To support Latinx families of autistic youth navigate school-based transition services and adult disability services, we provided a family advocacy program entitled, ASISTIR (Apoyando a nueStros hIjo/as con autiSmo obTener servIcios de tRansición; Supporting our Children with Autism to Obtain Transition Services). The ASISTIR program consisted of six, two-hour sessions and included the following topics: school-based transition planning, person-centered planning, Supplemental Security Income, Vocational Rehabilitation, and Home and Community-Based Medicaid Waiver. Twenty-nine Latinx family members completed the cohort-based ASISTIR program. After participating family members demonstrated increased knowledge of school-based transition planning and adult disability services. Participants also demonstrated increased empowerment and advocacy.

Knowledge-based Cyber Physical Security at Smart Home: A Review

Smart-home systems represent the future of modern building infrastructure as they integrate numerous devices and applications to improve the overall quality of life. These systems establish connectivity among smart devices, leveraging network technologies and algorithmic controls to monitor and manage physical environments. However, ensuring robust security in smart homes, along with securing smart devices, presents a formidable challenge. A substantial number of security solutions for smart homes rely on data-driven approaches (e.g., machine/deep learning) to identify and mitigate potential threats. These approaches involve training models on extensive datasets, which distinguishes them from knowledge-driven methods. In this review, we examine the role of knowledge within smart homes, focusing on understanding and reasoning regarding various events and their utility toward securing smart homes. We propose a taxonomy to characterize the categorization of decision-making approaches. By specifying the most common vulnerabilities, attacks, and threats, we can analyze and assess the countermeasures against them. We also examine how smart homes have been evaluated in the reviewed literature. Furthermore, we explore the challenges inherent in smart homes and investigate existing solutions that aim at overcoming these limitations. Finally, we examine the key gaps in smart-home-security research and define future research directions for knowledge-driven schemes.

The Future of Smart Home Security: Generative AI and LLMs for Intelligent Event Detection and Personalized Notifications

Abstract—Smart home security cameras are becoming more common, but their usefulness can be diminished by notification fatigue from too many alerts about minor incidents. This paper examines the gaps of existing event detection and notification systems in security cameras and then recommends using Generative AI and Large Language Models (LLMs) to add intelligence which would improve user experience. Generative AI can be leveraged to classify events more accurately and assist with anomaly detection. LLMs can further be used to create notifications that are tailored to the context and personalized to users behavior, helping to reduce notification fatigue and provide meaningful user alerts. The paper also looks into wider applications of these technologies to add intelligence and improve other related experiences like automated video summarization, proactive security measures, and improved privacy controls. The integration of Generative AI and LLMs with smart home security camera systems advances the smart cameras capabilities and offers enhanced security, personalized user experiences. Keywords—Smart home security, Generative AI, Large Language Models (LLMs), Event detection, Anomaly detection, Notification fatigue, Context-aware notifications, Personalized security, Reinforcement Learning from Human Feedback (RLHF), Internet of Things (IoT).

IoT-Enabled Home Security Systems: Advanced Technology for Enhanced Safety and Efficiency

IoT-Enabled Home Security Systems: Advanced Technology for Enhanced Safety and Efficiency

PicPatrolling: Smart Surveillance with ESP32CAM

Robotics is an exciting and developing branch of science. Robots are now common in society because of their high efficiency and durability, and they have many users. Security and surveillance robots, especially those with wheeled movements and agility, are important for multiplexes in security and surveillance domains, including public security, military, facilities, and home security. These robots carry built-in high-end sensors, cameras, and communication equipment that simplify real-time data collection in indoor and outdoor environments. "PicPatrolling: Smart Surveillance with ESP32CAM” is a technique that attempts to improve the current concept of security and surveillance. The proposed system incorporates ESP32CAM for image capture and transmission without delay and GPS for location tracking. The system includes sending photo e-mail alerts, making immediate reports, and guaranteeing adequate area coverage. The robot can move during operation in a complex environment using computer vision, machine learning, and sensor fusion to recognize objects, faces, and changes and monitor important regions.. Application areas for surveillance robots include commercial and populated regions, transport points and architectural structures, other soft regions that people cannot access, and other dangerous areas that humans cannot monitor for various reasons.

Home Door Security System with Face Recognition Using ESP 32 Cam

The development of science and technology, especially in the fields of Electronics and Informatics, is expected to enhance room or home security systems, particularly home door security systems. Many door security technologies have been developed to prevent and ensure the comfort of homeowners when they leave their homes. Some commonly used door security systems include CCTV cameras, fingerprint sensors, voice sensors, and RFID. However, with technological advancements, these security systems have shown vulnerabilities, such as frequent errors in fingerprint and voice sensors. Based on this, to create a more secure and innovative home door security system, the authors developed a security system with face detection and a remote warning notification system using Telegram. The researchers created a door security system using the ESP 32-CAM for face detection and as a controller for the Internet of Things (IoT) system. Additionally, a manual system using a keypad for password input was also created. The results of this study include the accuracy and speed of face recognition and the speed of sending security system warning information through the Telegram application. Keywords— Door Lock System, Face Recognition, ESP32 Camera, Microcontroller, Internet of Things.

Developing Independent Living Support for Older Adults Using Internet of Things and AI-Based Systems: Co-Design Study.

The number of older people with unmet health care and support needs is increasing substantially due to the challenges facing health care systems worldwide. There are potentially great benefits to using the Internet of Things coupled with artificial intelligence to support independent living and the measurement of health risks, thus improving quality of life for the older adult population. Taking a co-design approach has the potential to ensure that these technological solutions are developed to address specific user needs and requirements. The aim of this study was to investigate stakeholders' perceptions of independent living and technology solutions, identify stakeholders' suggestions on how technology could assist older adults to live independently, and explore the acceptability and usefulness of a prototype Internet of Things solution called the NEX system to support independent living for an older adult population. The development of the NEX system was carried out in 3 key phases with a strong focus on diverse stakeholder involvement. The initial predesign exploratory phase recruited 17 stakeholders, including older adults and family caregivers, using fictitious personas and scenarios to explore initial perceptions of independent living and technology solutions. The subsequent co-design and testing phase expanded this to include a comprehensive web-based survey completed by 380 stakeholders, encompassing older adults, family caregivers, health care professionals, and home care support staff. This phase also included prototype testing at home by 7 older adults to assess technology needs, requirements, and the initial acceptability of the system. Finally, in the postdesign phase, workshops were held between academic and industry partners to analyze data collected from the earlier stages and to discuss recommendations for the future development of the system. The predesign phase revealed 3 broad themes: loneliness and technology, aging and technology, and adopting and using technology. The co-design phase highlighted key areas where technology could assist older adults to live independently: home security, falls and loneliness, remote monitoring by family members, and communication with clients. Prototype testing revealed that the acceptability aspects of the prototype varied across technology types. Ambient sensors and voice-activated assistants were described as the most acceptable technology by participants. Last, the postdesign analysis process highlighted that ambient sensors have the potential for automatic detection of activities of daily living, resulting in key recommendations for future developments and deployments in this area. This study demonstrates the significance of incorporating diverse stakeholder perspectives in developing solutions that support independent living. Additionally, it emphasizes the advantages of prototype testing in home environments, offering crucial insights into the real-world experiences of users interacting with technological solutions.

IOT-Based Smart Door Selector For Double Security: Integration of Rfid and Blynk App for Economical Solution

This research aims to design and develop an Internet of Things (IoT)-based smart door lock system by utilizing Radio Frequency Identification (RFID) technology and the Blynk application. The system is designed to improve the level of home security by providing a variety of flexible access methods, including the use of RFID cards, smartphone apps, and emergency buttons. The prototype method is applied in system development, which involves creating a physical model to evaluate its performance and functionality. The test results showed that the system was operating properly, where RFID only responded to the registered card to unlock, thus increasing access security. Hardware design that is carried out with an economical approach and simple use of components makes this system more affordable compared to commercial smart door lock products on the market. This research shows great potential in increasing efficiency and comfort in managing home security systems and offers ease of implementation in various types of housing. Thus, this research makes a significant contribution to the development of innovative and practical solutions to improve home safety at a lower cost while providing a sense of security for its occupants.

Towards Securing Smart Homes: A Systematic Literature Review of Malware Detection Techniques and Recommended Prevention Approach

The exponential growth of the Internet of Things (IoT) sector has resulted in a surge of interconnected gadgets in smart households, thus exposing them to new cyber-attack susceptibilities. This systematic literature review investigates machine learning methodologies for detecting malware in smart homes, with a specific emphasis on identifying common threats such as denial-of-service attacks, phishing efforts, and zero-day vulnerabilities. By examining 56 publications published from 2019 to 2023, this analysis uncovers that users are the weakest link and that there is a possibility of attackers disrupting home automation systems, stealing confidential information, or causing physical harm. Machine learning approaches, namely, deep learning and ensemble approaches, are emerging as effective tools for detecting malware. In addition, this analysis highlights prevention techniques, such as early threat detection systems, intrusion detection systems, and robust authentication procedures, as crucial measures for improving smart home security. This study offers significant insights for academics and practitioners aiming to protect smart home settings from growing cybersecurity threats by summarizing the existing knowledge.

Deep-Learning-Based Real-Time Passive Non-Line-of-Sight Imaging for Room-Scale Scenes.

Non-line-of-sight imaging is a technique for reconstructing scenes behind obstacles. We report a real-time passive non-line-of-sight (NLOS) imaging method for room-scale hidden scenes, which can be applied to smart home security monitoring sensing systems and indoor fast fuzzy navigation and positioning under the premise of protecting privacy. An unseen scene encoding enhancement network (USEEN) for hidden scene reconstruction is proposed, which is a convolutional neural network designed for NLOS imaging. The network is robust to ambient light interference conditions on diffuse reflective surfaces and maintains a fast reconstruction speed of 12.2 milliseconds per estimation. The consistency of the mean square error (MSE) is verified, and the peak signal-to-noise ratio (PSNR) values of 19.21 dB, 15.86 dB, and 13.62 dB are obtained for the training, validation, and test datasets, respectively. The average values of the structural similarity index (SSIM) are 0.83, 0.68, and 0.59, respectively, and are compared and discussed with the corresponding indicators of the other two models. The sensing system built using this method will show application potential in many fields that require accurate and real-time NLOS imaging, especially smart home security systems in room-scale scenes.

Face Recognition-Based Door Lock Security System Using TensorFlow Lite

A door security system utilizing face recognition technology based on TensorFlow Lite has been developed to enhance access security and convenience. This research aims to design a system capable of accurately recognizing faces in real time, integrating it with door lock devices, and ensuring user data security. Employing the waterfall method, the system was implemented using an ESP32-CAM microcontroller and deep learning algorithms. Testing results demonstrated a face recognition accuracy of 91% in identifying and processing commands from 200 trials with ten facial variations. Successful integration with door lock devices was achieved through serial communication. The system also features activity log recording for monitoring purposes. This solution offers greater practicality and security than RFID systems as it eliminates the need for physical cards. This research contributes to developing more sophisticated and user-friendly home security systems, with the potential for further enhancements in recognition capabilities under various lighting conditions and integration with other biometric technologies

Smart Home

The concept of a smart home integrates wireless electronic systems that connect household devices to a central interface, enabling remote control via smartphones or wall panels through Wi-Fi networks. This paper explores the core components of smart homes, including smart devices such as speakers, thermostats, lighting, and security systems, as well as the role of mobile applications in managing these devices. The paper discusses the various applications of smart homes, from home automation and security monitoring to energy management and health-focused initiatives.

Comprehensive analysis of services towards Data Aggregation, Data Fusion and enhancing security in IoT-based smart home

Data aggregation and sensors data fusion would be very helpful in a number of developing fields, including deep learning, driverless cars, smart cities, and the Internet of Things (IoT). An advanced smart home application will test the upgraded Constrained Application Protocol (CoAP) using Contiki Cooja. Smart home can enhance people’s comfort. Secure authentication between the transmitter and recipient nodes is essential for providing IoT services. In many IoT applications, device data are critical. Current encryption techniques use complicated arithmetic for security. However, these arithmetic techniques waste power. Hash algorithms can authenticate these IoT applications. Mobile protection issues must be treated seriously, because smart systems are automatically regulated. CoAP lets sensors send and receive server data with an energy-efficient hash function to increase security and speed. SHA224, SHA-1, and SHA256 were tested by the CoAP protocol. Proposed model showed that SHA 224 starts secure sessions faster than SHA-256 and SHA-1. The ChaCha ci. This study proposed enhanced ChaCha, a stream cipher for low-duty-cycle IoT devices. For wireless connections between the IoT gateway and sensors with a maximum throughput of 1.5 Mbps, the proposed model employs a wireless error rate (WER) of 0.05; the throughput rises with an increase in the transmission data rate.

Arduino Based Automatic Door Lock Design Using Personal Identification Number Pin

Current developments require more innovation in everyday life, one of which is home or office door security. Initially, home or office door security was only with ordinary padlocks or door keys found in most houses in Indonesia which had shortcomings in terms of security and effectiveness. In the current era of digitalization, almost every line of human activity is no exception, technology is currently continuously developing more rapidly. This can be observed through the many sophisticated equipment that utilizes technology so that the work system can run automatically. Of course, this makes it much easier for someone to carry out their activities and the work they do is also more efficient. This research aims to design a door lock system that is able to increase security, effectiveness and comfort in accessing rooms, both home and office. This system utilizes an Arduino microcontroller as the brain of the system, a keypad as input for entering the PIN code. Users are allowed to open the door simply by entering a predetermined PIN code. The main advantage of this system is its ease of use and a better level of security compared to conventional door locks. The results of this research are a pin-based door lock system using Arduino and keypad. This system uses the Arduino IDE development application for writing program code and Proteus for work simulation.

Developing a Micro-Credential Curriculum in IoT for Malaysian Community Colleges: Key Topics and Projects

The Internet of Things (IoT) is a crucial course in Information Technology programs, merging electronic circuit concepts with programming skills to create interconnected devices. This study aimed to design a Malaysian Community College micro-credential curriculum for IoT by identifying relevant theoretical and practical topics and mini-projects. A need for a micro-credential module with suitable instructional materials was identified, enabling learners to apply theoretical concepts through mini-projects. Using the Fuzzy Delphi (FD) method, data from 19 experts via semi-structured interviews were analyzed to design the Fuzzy Delphi instrument. This instrument was distributed to 30 industry experts and academic practitioners, who ranked the elements on a five-point Likert scale. Fuzzy evaluation to establish consensus for inclusion requires an average expert agreement of 75%, a d-construct threshold of 0.2, and an average fuzzy number of 0.5. Consensus was reached for six out of nine theoretical topics and nine out of 13 practical topics. “Fundamental concepts of IoT” was deemed the most critical theoretical topic with 97% consensus, while "basic connectivity of IoT hardware" was the top practical topic with 100% consensus. For mini-projects, experts accepted two out of nine theoretical topics and nine out of 13 hands-on topics. The top authentic projects were “Air Quality Monitoring” and “Smart Home Security”, with 83% and 80% consensus, respectively. This study lays the groundwork for developing learning outcomes and assessment strategies for a micro-credential course on IoT at Community Collegesto equip TVET students with industry-relevant competencies, thus making them more employable.

Real-Time Home Automation System Using BCI Technology.

A Brain-Computer Interface (BCI) processes and converts brain signals to provide commands to output devices to carry out certain tasks. The main purpose of BCIs is to replace or restore the missing or damaged functions of disabled people, including in neuromuscular disorders like Amyotrophic Lateral Sclerosis (ALS), cerebral palsy, stroke, or spinal cord injury. Hence, a BCI does not use neuromuscular output pathways; it bypasses traditional neuromuscular pathways by directly interpreting brain signals to command devices. Scientists have used several techniques like electroencephalography (EEG) and intracortical and electrocorticographic (ECoG) techniques to collect brain signals that are used to control robotic arms, prosthetics, wheelchairs, and several other devices. A non-invasive method of EEG is used for collecting and monitoring the signals of the brain. Implementing EEG-based BCI technology in home automation systems may facilitate a wide range of tasks for people with disabilities. It is important to assist and empower individuals with paralysis to engage with existing home automation systems and gadgets in this particular situation. This paper proposes a home security system to control a door and a light using an EEG-based BCI. The system prototype consists of the EMOTIV Insight™ headset, Raspberry Pi 4, a servo motor to open/close the door, and an LED. The system can be very helpful for disabled people, including arm amputees who cannot close or open doors or use a remote control to turn on or turn off lights. The system includes an application made in Flutter to receive notifications on a smartphone related to the status of the door and the LEDs. The disabled person can control the door as well as the LED using his/her brain signals detected by the EMOTIV Insight™ headset.

Development of Internet of Things-Based Room Security using Telegram

Security plays a very important role in various aspects, one of which is the security of a home room which is a building for a residence. Currently, there are still many home security system devices circulating on the market that are considered ineffective in their use and cost. In addition, the home security system cannot provide warning notifications when home security is threatened and send the latest conditions of the house. This study designs a home room security system using the Internet of Things (IoT) based on Telegram Messenger. When the Passive Infrared Receiver (PIR) sensor detects movement, the OV2460 camera on the ESP32-Cam module will take a picture and then send it to the homeowner as a form of home security warning via a bot on Telegram Messenger. When the Flame Sensor detects a fire, the Buzzer will sound and then send a notification in the form of a fire warning. The results of this study showed that the home room security that was created was able to detect movement at an effective distance of the PIR sensor of 1 - 6 m and the presence of fire on the Flame Sensor which has a wavelength between 760nm - 1100nm. For the next researcher, they can develop a camera that can detect faces (face detection) and face recognition.

The Role of Environmental and Socioeconomic Factors in Mental Health Well-being: Insight from Jordan

Background and aims: Depression, anxiety, and stress are among the most common psychological disorders affecting people worldwide. These conditions can influence mental well-being, resulting in sustained feelings of sadness, worry, and hopelessness. This study aims to examine the relationship between environmental factors and psychological disorders (i.e., depression, anxiety, and stress) and to explore the relationship between socioeconomic factors and these psychological disorders, methods: Depression, anxiety, and stress were measured using the Arabic version of Depression, Anxiety, and Stress Scale - 21 items (DASS-21). Spearman's Rank Correlation was used to examine the relationship between environmental and socioeconomic factors and (anxiety, depression, and stress). The results were considered significant at P value ˂ 0.05, and the confidence interval (CI) was 95%. Results: The study included 409 participants. with a mean age of 36.42 years (SD ± 13.259). The majority were female (59.4%), married (57.2%), had a bachelor’s degree (46.5%), and were employed full-time (51.3%). The majority had an income of less than 500 JOD per month (53.5%). A negative significant relationship was found between Depression, anxiety, and stress and all environmental and socioeconomic factors except the proximity of green areas Conclusion: Environmental and socioeconomic factors were found to be significantly associated with mental health, indicating that both environmental and personal financial safety play crucial roles in influencing psychological well-being. These findings suggest that interventions aimed at improving access to natural environments and enhancing home security may be beneficial in promoting mental health and reducing depression, anxiety, and stress levels within the population.

Cost-Efficient Automated Intrusion Detection and Reporting System for Homes in Nigeria

Substantial investments are made to mitigate the persistent threat to lives and assets posed by various forms of intrusion. Automated security systems have emerged as crucial tools for safeguarding homes against intrusions in recent times. A major advantage of this is its independent ability to report human activities around homes without direct observation. However, the cost of implementing the system is not pocket-friendly to an average Nigerian given the prevailing economic situation. This research therefore seeks to develop and implement an intrusion detection and reporting system using low cost materials while maintaining a balance between quality and cost at the same time. The system architecture employs the passive infrared sensor (PIR) for motion detection, the ultrasonic sensor measures intruders’ location from the home entrance while the NodeMCU ESP8266 microcontroller is responsible for coordinating the reception and relaying of signals within the system. The system was implemented with varying distances of human presence from the security device. Reports of potential intrusion alerts were obtained within reasonable time frames (between 11 seconds and 49 seconds) on the homeowner’s mobile phone via the Blynk application. This performance demonstrates the reliability of the system for home security. The system is equally cost-efficient relative to most similar state-of-the art IoT based home security systems considered in this work. Our work therefore contributes to knowledge by proposing an affordable home security solution for a low budget Nigerian user.

Private Cctv Liability Under Biometric Data Protection Rules

The penetration of technology that is increasingly developing has a lot of impact on human life. Increasing technology also results in the rapid development of home security equipment, especially Closed-Circuit Television cameras (hereinafter mentioned as CCTV). The right to privacy, to which everyone is entitled to personal self- protection Doctrinal legal research is a crucial aspect in recognizing various societal problems. Doctrinal research combines a systematic analysis of the provisions of legislation and the legal principles contained in it or derived from it, but also the logical and rational order of legal propositions and principles. Doctrinal legal research methods involve the analysis of legal propositions or legal concepts on which the main study is based. Case base approach demands the researcher to understand the reasons that used by the judges or another adjudicator until it reaches the final decision. Most personal data protection laws around the world distinguish between regular personal data and biometric data. At the beginning, Article 4 paragraph (1) of EU GDPR regular personal data commonly define on the laws as a set of data that able to identify a natural person either from electronic or non-electronic devices. Under the data protection laws regime especially GDPR, UK Data Protection Law and Indonesia Data Protection Law, mitigation during a data protection failure involving household CCTV systems typically involves taking immediate steps to minimize the impact of the breach Furthermore, stated in the Fairhurst v Woodard case homeowner could be held liable for failure to protect the personal data if they use camera on the outside boundary of their house and they could face hefty fine and/or tools removal. Finally, this writing contributes to undergoing discourse on cyber law field especially in data protection law and provide insights for academician, practitioner, and society.