Smart Home Automation Research Articles

Wireless Home Security Using IOT

Abstract: We are living in the fourth industrial revolution. Our life is becoming more comfortable and smarter with the help of rapid upgrade of technology. Internet of things (IOT) is playing a massive role in this. One of the major sides of IOT is a smart Electrical Appliances. As we are in the era of never-ending growth of the internet and its application, smart home system or home automation system is highly increasing to provide comfort in life and improving the quality of life. In this project, we present an IOT based low cost smart electrical equipment automation system. This system is based on a web portal which controlled by an ESP32 WI-FI module. Also, a custom-made private web server is developed for maintaining the current states of electrical devices appliances.

The Enhanced Speech Recognition in Automated Home Lighting System using Adaptive Time-Frequency Domain Noise Removal Algorithm Filter

Numerous studies have explored speech recognition performance in Smart Home environments. However speech recognition accuracy diminishes when voice commands are captured in noisy areas of the home. This study aims to enhance speech recognition performance in such noisy environments. Instead of relying on remote control signals, a Bluetooth system is employed for short-range wireless communication to identify speech commands. Various sound levels are measured in decibels (dB) at different distances using the Smart Noise Application. A filter algorithm with Adaptive Filtering is used to minimize unwanted noise. The algorithm uses Adaptive Time-Frequency Domain Noise Removal (TFDNR) to mitigate background noise. Overall, the integrated system comprising Smartphone, Bluetooth, Arduino microcontroller, and noise detection software exhibits improved performance compared to previous studies, highlighting its potential for seamless smart home automation.

An Audio - Visual Virtual Personal Assistant

Current traits in smart assistants and smart home automation are currently attracting the interests of customers and researchers. Speech enabled smart virtual assistants (named smart speakers) offer a wide sort of network orientated services and, in some instances, can connect to smart environments, accordingly improving them with new and effective user interfaces. But, such gadgets also reveal new desires and a few weaknesses. Specially, they constitute faceless and blind assistants, not able to reveal a face, and therefore an emotion, and not able to ‘see’ the person. For this reason, the interaction is impaired and, in a few cases, ineffective. One of the goals of artificial intelligence is the realization of natural talk among humans and machines. In latest years, the dialogue systems, known as interactive conversational systems are the fastest developing vicinity in AI. Many companies have used the dialogue systems technology to set up various sorts of VPAs based on their application areas, including Apple’s Siri, Amazon Alexa, etc. To triumph over such troubles, in this project we combine a number of advance techniques. The proposed Assistant is powerful and resource-efficient, interactive and customizable. We use the multi-modal dialogue structures and screen projection which process two or more combined user input modes, which includes speech, image, video, touch, manual gestures, body movements with the intention to design the NextGeneration of VPAs. The new version of VPAs can be used to increase the interaction between humans and machines by the usage of distinctive technologies, consisting of gesture recognition, image/video recognition, speech recognition, dialogue system, conversational information base, and the overall knowledge base. Furthermore, this VPA device can be utilized in different areas such as education, medical assistance, disabilities systems, home automation and security access control.

Guardians of the iot realm: a comparative analysis of cryptographic security solutions for bolstering iot device networks

The "Internet of Things" (IoT) is an emerging technology that allows electronic devices and sensors to connect with one another over the Internet. The IoT is a network of smart devices such as sensors that are connected through cables or wirelessly. Today, IoT devices are commonly used in medical science, industrial automation, and smart home automation, among other applications. Easy accessibility and the open-source environment of IoT devices are major threats to privacy and security. In this paper, we comprehensively discuss areas of IoT, including architecture, details of IoT layers with respect to security algorithms, protocols, attacks, and their mitigation. The primary purpose of this research is to provide an efficient and cryptographically proven algorithm scheme with appropriate hardware to improve IoT device network security. The presented research enhances the network security of IoT devices by presenting an algorithmic framework fortified by cryptography and supported by the finest hardware options. After evaluating several cryptographic algorithms and hardware options, a more secure solution for IoT infrastructure is proposed. The article in particular, proposes a detailed strategy that addresses security at every level of the IoT system. This research is an important step towards ensuring the integrity and security of IoT devices in a networked environment, with the primary goal of increasing the protection of sensitive data and interactions.

Cadio Interfaced Smart Home with IoT Enabled Automation Systems

Smart home automation integrates advanced technologies for efficient, convenient, and secure residential living. It employs sensors and actuators to collect data and automate physical tasks. Connectivity protocols Wi-Fi enable seamless communication among devices. This project presents a simpler IoT based home automation system using ESP8266 and Cadio app. This project includes security features include smart user behaviour lighting, motion detection, automatic water level control and locks, enhancing real-time monitoring and remote access. Voice control via virtual assistants like Alexa and google assistant adds a hands-free dimension to device management. Artificial Intelligence teaches user behaviour, enabling the system to automate routines over time. Customization options allow users to create personalized scenarios and routines tailored to their preferences. Here there is no code needed and it is easy to integrate and control. Using this IOT based home automation system reduces electricity bill of consumer and reduces carbon footprint at lower cost by implementation of this home automation project. Key Words: Cadio, Smart Home, Home Automation, IoT, Sensor, NodeMCU

Home Automation System

The world is rapidly moving towards automation as people have less time to handle all the work that needs to be done. Automation is an easy solution to control any product or machine to suit our needs. This article presents the development and implementation of a new home automation system unit using Arduino Nano as the main processing unit. The system integrates multiple sensors such as passive infrared (PIR) sensors for motion detection, gas sensors for monitoring gas issues, temperature sensors for detecting temperature changes, and light sensors for monitoring ambient light, as well as Bluetooth modules for seamless wireless communication and monitoring capabilities. The primary objective of this research is to create a smart and flexible home automation solution that enhances comfort, security, and energy efficiency.

Automated Domesticity: Exploring the Realm of Home Automation Systems

This research paper presents the development and implementation of a comprehensive smart home automation system comprising a smart switchboard, smart smoke, flame, and gas detector, and a smart door lock system. Leveraging innovative technologies such as voice control, sensor networks, and Bluetooth Low Energy connectivity, the system offers users enhanced convenience, safety, and energy efficiency within residential environments. The smart switchboard enables intuitive voice-controlled operation alongside traditional manual control options, while the smart smoke, flame, and gas detector provide real-time monitoring and alerting capabilities. Additionally, the smart door lock system ensures secure access control through voice password authentication and manual lock and key options. Through this integration, our research demonstrates the transformative potential of smart home automation in revolutionizing household management and enhancing quality of life

How to Keep Balance between Interaction and Automation? Toward User Overall Positive Experience of IoT-Based Smart Home Design

Advances in Internet of Things (IoT) technologies have had a profound impact on the experiential tasks of the smart home, particularly automated tasks supported by sensors, embedded systems, and communication devices that are capable of autonomously performing a range of tasks previously performed by humans. However, the improvement in the level of smart home automation has not necessarily promoted more meaningful, relaxing, and positive experiences for users. This is largely due to the fact that it often overlooks the meaningful involvement of humans in the process. This paper proposes a conceptual design model for a smart home through two studies that aim to maintain the benefits of automation while ensuring positive experiences with user engagement tasks. Study 1 involved qualitative data using semi-structured interviews to understand what tasks lead to positive user experiences (P-UX) during the use of the smart home and what kind of factors influence these positive experiences. Study 2, using an online questionnaire, quantitatively investigated the varying impact of these factors on positive experiences in both automation and human–machine interactions. The results of both studies show that there are nine factors that influence positive user experience in the smart home, with instrumentality, convenience, and flexibility playing an important role in the positive experience of automation, aesthetics, immersion, association, and memory, having a greater effect on the positive experience of user engagement in interactions, and customization and emotion contributing to both paradigms. In future smart home design, this model will help designers rationally allocate tasks between automation and human–machine interaction as a way to enhance the overall positive user experience.

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Integration of Machine Learning and Iot based Multi-Layer Wireless Sensor Networks for Seamless Smart Home Automation

The integration of machine learning algorithms with Internet of Things -based multi-layer wireless sensor networks offers a transformative solution to modern smart home automation and security. This paper seeks to explore both the Sensor Data Processing and Machine Learning Components used. In essence, the machine learning model leverages aggregate sensor data to improve the efficiency and safety of modern living environments. Through attention to detail during the preprocessing and feature engineering of sensor data, the system analyzed learned patterns of household activity, including motion intensity, door/window interactions, and normal occupancy. Subsequently, the features were utilized as input variables in a number of models, including Convolutional Neural Networks , Support Vector Machines, Recurrent Neural Networks, and K-Nearest Neighbors. By simulating both vacation and occupant mode, the models processed the available data using to detect potential threats or other deficiencies in the patterns. Overall the results led by the model suggest that the Convolutional Neural Network model is the best amongst all of the aforementioned algorithms. It demonstrated the best accuracy out of all the other models due to the advanced image processing and facial recognition features, solidified by its 98.56% accuracy rate in recognizing a potential intruder. Other models were also effective or reasonably accurate, with the following results: SVMs – 95.45%; RNNs – 93.4%; and KNNs – 91.23%. The evaluation process also involves the calculation of precision, recall, and F1 score, which also indicates the overall strength of the model. The aggregation of these results implies a considerable potential for internet-of-things systems in combination with machine learning methods to cultivate smarter and safer living environments.

Security assessment and improvement of building ethernet KNXnet/IP protocol

KNXnet/IP is a KNX-building automation protocol that communicates through an IP network. It is mainly used in the field of smart home and building automation control, allowing remote access to KNX devices through an IP network, so that building functions can be managed and controlled anywhere through an Internet connection. However, with the development of smart homes and building automation, such physical devices based on IP communication are more and more frequently in contact with the Internet, resulting in more and more security issues for home devices and buildings exposed to the Internet. This paper uses the formal analysis method-colored Petri net (CPN) combined with the CPN Tools to model the KNXnet/IP protocol and analyze the protocol interaction process. Based on the Dolev-Yao attacker model, the security of the KNXnet/IP protocol is evaluated and tested, and it is verified whether there are three types of attackable vulnerabilities in the protocol: replay, tampering, and spoofing. After CPN modeling analysis and verification, it is found that tampering and replay vulnerabilities in the original protocol. Therefore, we introduce timestamp and hash to strengthen the security mechanism of the protocol, which ensures the integrity, confidentiality, and freshness of the security mechanism of the protocol. After the final analysis and verification, the improvement scheme proposed in this paper can effectively improve the security performance of the protocol.

Emotion-oriented recommender system for personalized control of indoor environmental quality

Indoor Environmental Quality (IEQ) significantly influences health, cognitive abilities, and even moods. With the rise of smart homes and the Internet of Things (IoT), the need for personalized IEQ control has become paramount. Traditional systems, primarily focusing on explicit feedback (i.e., preference), often neglect the role of emotions in shaping this feedback. Therefore, this study presents the Emotion-oriented Recommender System for personalized control of IEQ (ERS-IEQ), based on the R-E-C-S ontology, focusing on (i) Recognizing user's continuous emotional states, (ii) Estimating emotional similarity between users, (iii) Collecting user's feedback on IEQ conditions, and (iv) Systemizing an emotion-oriented recommender system using a graph neural network. In order to confirm the predictive performance of the ERS-IEQ and verify its higher level of excellence compared to traditional recommender systems, a private dataset composed of IEQ conditions, users' explicit feedback, and users' emotional states was built via a human participant experiment using a climate chamber. As a result, the ERS-IEQ significantly enhances the recommender system's predictive performance, particularly in thermal preferences. The number of linguistic terms of emotional similarity has a profound effect on the system's predictions, with four terms proving most effective. In the near future, ERS-IEQ will play a role as a personal assistant in smart home automation, offering emotion-based recommendations. It addresses key challenges in traditional recommender systems, such as the cold start problem and rating sparsity, and ensures personalized adjustments to IEQ conditions in both private and public spaces.

ViObject

Knowing the object grabbed by a hand can offer essential contextual information for interaction between the human and the physical world. This paper presents a novel system, ViObject, for passive object recognition that uses accelerometer and gyroscope sensor data from commodity smartwatches to identify untagged everyday objects. The system relies on the vibrations caused by grabbing objects and does not require additional hardware or human effort. ViObject's ability to recognize objects passively can have important implications for a wide range of applications, from smart home automation to healthcare and assistive technologies. In this paper, we present the design and implementation of ViObject, to address challenges such as motion interference, different object-touching positions, different grasp speeds/pressure, and model customization to new users and new objects. We evaluate the system's performance using a dataset of 20 objects from 20 participants and show that ViObject achieves an average accuracy of 86.4%. We also customize models for new users and new objects, achieving an average accuracy of 90.1%. Overall, ViObject demonstrates a novel technology concept of passive object recognition using commodity smartwatches and opens up new avenues for research and innovation in this area.

Voice Activated Lamp using Arduino

In the realm of smart home automation, this project introduces a Voice-Controlled Lamp System utilizing Arduino Nano, a Bluetooth module, and a single-channel relay. The system enables remote lamp control through a dedicated mobile application, leveraging natural language voice commands for intuitive interaction. By integrating cutting-edge hardware and software components, the project aims to enhance user experience, convenience, and accessibility in home environments. Through rigorous testing and validation, the Voice-Controlled Lamp System demonstrates the potential of Arduino-based solutions in crafting user-friendly and efficient smart devices, contributing to the evolution of modern home automation technologies. This project presents the development of a Voice-Controlled Lamp System, utilizing Arduino Nano, a Bluetooth module, and a single-channel relay. The system offers users the ability to remotely control the lamp through a mobile application, employing natural language voice commands for seamless interaction. By integrating state-of-the-art hardware and software components, the project addresses the growing demand for intuitive and hands-free home automation solutions. Through extensive testing and validation, the Voice-Controlled Lamp System showcases the efficacy of Arduino-based technologies in creating innovative and user- centric devices for modern living environments.

Anti-Theft Flooring System using ESP32

The ESP32-powered anti-theft flooring system represents an innovative and cost-effective solution designed to enhance security across various settings, including homes, businesses, and diverse locations. At its core, the system comprises a network of strategically placed pressure sensors concealed beneath a flooring mat, seamlessly integrated with the ESP32 microcontroller featuring built-in Wi-Fi and a camera. When an object exceeding a predetermined weight threshold traverses the mat, the embedded sensors swiftly activate the ESP32, prompting it to capture a photograph of the event. This image is then promptly forwarded to a pre-configured email address, offering real-time visual insights into potential security breaches. Furthermore, the ESP32 can be programmatically configured to activate an audible alarm, providing an additional layer of security and acting as a powerful deterrent. The system’s design emphasizes user-friendliness, ease of installation, and adaptability to various environmental needs. Its customization options enable users to tailor the system to specific requirements, ensuring a seamless integration into any given space. Importantly, safety measures have been incorporated to prevent false alarms triggered by smaller entities, as the pressure sensors only respond to objects surpassing a predefined weight. Beyond its primary function of theft prevention, the ESP32 anti-theft flooring system unveils a realm of creative possibilities. Its applications extend to smart home automation, traffic monitoring, and even livestock tracking, showcasing the system’s versatility and adaptability in addressing diverse challenges. This multifaceted approach positions the ESP32 system not only as a robust security solution but also as a catalyst for innovation in various technological domains.

Implementasi Smart Home pada Platform Apple Homekit dan Google Home dengan Raspberry Pi 4B

This research examines the significant impact of technological advances, especially in the Internet of Things (IoT) paradigm, on various aspects of human life strongly manifested during the In-dustrial Revolution Era 4.0. The main focus of this research is on the application of advanced and innovative IoT concepts in the context of smart homes, integrating popular platforms such as Apple HomeKit and Google Home. The temperature sensor (DHT11) and light sensor (LDR) play a key role as important input elements, enabling the optimization of smart home automation functions. Raspberry Pi 4B was chosen as the main platform, the "brain" of the system, providing reliable computing capabilities. Using four-channel relays, this research specifically aims to increase ef-ficiency and integration in controlling devices in the smart home ecosystem. By emphasizing the concept of optimization, this research proposes a smart solution that is expected to not only provide an integrated and efficient experience for smart home users but also unlock the potential for further development of IoT technology. As a result, the implementation of the solutions proposed in this research is expected to create a smart home that is not only technologically smart but also responsive to the needs and preferences of its occupants, paving the way for further in-novation in the development and application of the Internet of Things in various contexts of human life.

An Integrated Gesture Framework of Smart Entry Based on Arduino and Random Forest Classifier

Gesture-based systems have emerged as a prominent breakthrough in the field of smart access control, effectively integrating security measures with user comfort. This study presents a novel gesture detection framework for smart entry systems that combines the computational capabilities of a Random Forest Classifier with the practicality of Arduino-based hardware. Central to methodology is the utilization of MediaPipe, an advanced computer vision library, to extract hand motion landmarks from live video streams. The selected landmarks function as a comprehensive dataset for training a Random Forest Classifier, which has been specifically chosen due to its high level of accuracy and efficiency in managing intricate classification jobs. The model exhibits outstanding competence in the categorization of gestures in real-time, attaining high levels of accuracy that are crucial for ensuring dependable entrance control. The Arduino microcontroller plays a vital role in the execution of the entry mechanism as it serves as the intermediary between the gesture detection software and the tangible entry control hardware. The incorporation of gesture recognition technology facilitates a cohesive and prompt user experience, wherein identified motions are directly converted into input commands. The system's practical use is demonstrated through a series of detailed tests, which highlight its dependability and efficiency across diverse climatic circumstances. The findings underscore the system's capacity as a flexible and safe solution for contactless access in many environments, including both private homes and highly protected establishments. Furthermore, the study makes a substantial contribution to the larger domain of human-computer interaction by showcasing the practicality of advanced gesture detection systems in many everyday contexts. The suggested framework presents a novel approach to smart entry systems and also paves the way for further investigation in the domains of smart home automation and interactive systems. In these areas, gesture-based interfaces have the potential to deliver user experiences that are both intuitive and efficient.

Development and Assessment of Internet of Things-Driven Smart Home Security and Automation with Voice Commands

With the rapid rise of digitalization in the global economy, home security systems have become increasingly important for personal comfort and property protection. The collaboration between humans, the Internet of Things (IoT), and smart homes can be highly efficient. Interaction considers convenience, efficiency, security, responsiveness, and automation. This study aims to develop and assess IoT-based home security systems utilizing passive infrared (PIR) sensors to improve user interface, security, and automation controls using voice commands and buttons across different communication protocols. The proposed system incorporates controls for lighting and intrusion monitoring, as well as assessing both the functionality of voice commands and the precision of intruder detection via the PIR sensors. Intelligent light control and PIR intruder detection with a variable delay time for response detection are unified into the research methodology. The test outcomes examine the average effective response time in-depth, revealing performance distinctions among wireless fidelity (Wi-Fi) and fourth- and fifth-generation mobile connections. The outcomes illustrate the reliability of voice-activated light control via Google Assistant, with response accuracy rates of 83 percent for Thai voice commands and 91.50 percent for English voice commands. Moreover, the Blynk mobile application provided exceptional precision regarding operating light-button commands. The PIR motion detectors have a one hundred percent detection accuracy, and a 2.5 s delay is advised for PIR detection. Extended PIR detection delays result in prolonged system response times. This study examines the intricacies of response times across various environmental conditions, considering different degrees of mobile communication quality. This study ultimately advances the field by developing an IoT system prepared for efficient integration into everyday life, holding the potential to provide improved convenience, time-saving effectiveness, cost-efficiency, and enhanced home security protocols.

Ageing in Place: Ensuring Home Safety and Adaptations for the Well-Being of Seniors

This paper investigated the living considerations in ageing, emphasizing the importance of ensuring home safety and adaptations for senior well-being. When ageing in place, seniors' desire to remain in their familiar homes is explored to preserve independence and autonomy. This study delved into the existing literature on home safety for seniors, employing methodologies such as home safety assessments and fall incident surveys. The literature review emphasized the significance of home safety for seniors, particularly in preventing falls, a leading cause of injuries among older adults. Strategies such as home modifications, including the installation of grab bars and non-slip flooring, are examined, along with the integration of assistive technology like wearable devices and smart home automation. The study's methodology involves a comprehensive evaluation of home safety through standardized assessment tools, surveying seniors and analyzing the impact of home modifications and assistive technology on safety and well-being. The study revealed variations in home safety assessment scores among seniors, with higher scores corresponding to safer home environments. The number of fall incidents reported by participants in the past year underscores the need for proactive fall prevention strategies. Home modifications and usage of assistive technology, including wearable devices and smart home automation, positively influenced home safety scores. Recommendations include regular home safety assessments, prioritizing fall prevention strategies, education and awareness, individualized solutions, and continuous monitoring. The study concluded that by implementing these recommendations, seniors' safety and quality of life could be enhanced, enabling them to age comfortably and independently while reducing the risks associated with ageing.

Building a Secure IoT Platform for Smart Home Automation: A Comprehensive Integration

Abstract: The creation and use of a novel Internet ofThings (IoT) platform designed for smart home automation arepresented in this research study. The platform combines powerful hardware elements with cutting-edge software technologies. It makes use of Docker ptimization on, an Ubuntu-based Linux server, and essential utilities like Traefik, MySQL, Node-RED, Mosquitto, Adminer, and InfluxDB. The infrastructure is painstakingly built to provide scalable deployment, effective data management, and smooth communication. The core of the technique is the creation of a Linux server running Ubuntu. A flexible reverse proxy called Traefik ptimizat web traffic and makes sure that components communicate with one other effectively. Docker ptimization on is used for the modular deployment of services,such as the central orchestrator Node-RED, the lightweightMQTT broker services provided by Mosquitto, and MySQL for structured data storage. Time-series data processing and database administration are improved by the combination of Adminer and InfluxDB. The visual programming tool Node- RED acts as the central data flow orchestrator, allowing databases and devices to be connected with ease. Real-time datainterchange is ensured via MQTT implementation usingMosquitto, which is essential for Internet of Things applications.Network protocols, Docker best practices, and optional SSL/TLS implementation are just a few of the security features that provide a stable and secure environment. Security audits are planned on a regular basis to find and fix such weaknesses. Hardware for home automation control includes DHT11 sensors, 4-channel relay modules, and ESP32 development boards. Beyond simple automation, the platform includes functionalities for sensor data collecting, remote device administration, and intuitive appliance control via a Node-RED Dashboard. Applications include temperature, humidity, and air quality monitoring, as well as security and surveillance withbuilt-in cameras and motion sensors.