IoT-based Smart Home Research Articles

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.

IoT based Smart Home Energy Tracker and Saver

The knowledge about the amount of energy used every month or day at home is the initial step to reduce the energy usage. So, the proposed research focusses on developing a smart energy management system using the IoT to track the daily electricity usage and enhance the energy efficiency at home. The system employs network of sensors placed throughout the home to monitor energy usage in real-time. The information gathered is transmitted to the Blynk Cloud and visualized through the Blynk app. The chart displayed in the Blynk app enables the user to identify the excessive energy usage and reduce both consumption and the electricity bills. By integrating IoT technology and user-friendly control interfaces, this research aims to provide homeowners with actionable insights to minimize energy waste, reduce utility bills, and contribute to environmental conservation.

Design and Implementation of an IOT-based Smart Home Automation System in Real World Scenario

INTRODUCTION: Automation developments have significantly increased general convenience in modern technology. Our study focuses on developing and deploying an Internet of Things (IoT)–enabled smart home automation system that maximizes energy efficiency and improves convenience in residential settings. Our technology provides homes with an intelligent environment by smoothly combining several sensors, actuators, and communication protocols. We explore the challenges of creating a reliable and useful smart home systemthat works well in everyday situations. We investigate the difficulties, architectural issues, communication protocols, and security features specific to these systems.OBJECTIVES: This project aims to use a database server and Wi-Fi module to develop an effective and reasonably priced smart home automation system. It improves accessibility and convenience by enabling smartphone voice control of household appliances.METHODS: This project was developed using various implementation techniques, including wireless home automation via GSM technology, speech recognition-based systems, Blynk, and the Internet of Things. These techniques added up to a reliable and effective smart home system.RESULTS: Although it improved user routines, the IoT-based smart home system had latency problems. Interoperability issues are still present. Improvements were guided by user input. Using Blynk, our software manages loads remotely. CONCLUSION: A home automation system that is inexpensive and locally supplied can effectively operate household appliances. It is adaptable, scalable, and a component of future smart homes. IoT applications in real life revolutionize convenience and efficiency in living environments.

A living lab to develop smart home services for the residential welfare of older adults

Owing to the decline in the physical and social functions of older adults, the residential environment substantially impacts the psychological and emotional stability and life satisfaction for older adults. Recently, there has been a growing interest in providing residential welfare services related to the health and independent living of the older adults. The primary objective of this research was to identify the needs and perceptions of older adults regarding smart home services and associated sensors and evaluate the indoor environment conditions of older adults through a living lab. Survey results indicate that older adults felt the burden of electricity bills in summer and winter and had relatively high safety-related smart home service needs. Focus group interviews with the participants in the living lab confirmed that older adults positively perceived the benefits and privacy of the living-lab sensors. In addition, the analysis of indoor environment data over one year from a case study household showed that approximately 66.4 % during the winter season and 13.1 % during the summer season have fallen within the neutral thermal comfort range according to the proposed approach (i.e., predicted mean vote (PMV) calculated using heart rate). This study adopted the proposed PMV method (hereafter PMV-P) that takes into account personal factors (e.g., metabolic rate of older adults that can be calculated using heart rate) and environmental factors (e.g., temperature, humidity) by using the real-time data collected from IoT (Internet of Things)-based sensors. Furthermore, this study proposed a new home energy management system (EMS) algorithm on the basis of model predictive control for reducing heating energy consumption while satisfying the thermal comfort. The new EMS was demonstrated to achieve 5–12 % heating energy savings depending on the thermal comfort preferences of occupants. Overall, the results of multidisciplinary research not only provided the preliminary evidence regarding the needs and perceptions of older adults toward IoT-based sensors but also showcased the development of IoT-based smart home service system through the living lab.

Development design of an IoT-based smart home monitoring system with security features

<div style="text-align: start;" align="center"><p>A smart home is a system that has been programmed and can work automatically by utilizing internet of things (IoT) technology, this system can control various electronic devices in the home. This paper presents a design for developing an IoT-based smart home monitoring system with the addition of security features. This research aims to design and develop a smart home monitoring system that uses the IoT which operates via the web and improves the security aspects of the system. This research includes the development of hardware and software that enables efficient and safe monitoring and control of various aspects of the home via smartphone or computer-based devices using resources from solar power plants. This system relies on the use of a Raspberry Pi as a microcontroller and several sensors. In this context has important value in maintaining user security, and privacy and supports the growing development of the smart home technology industry.</p></div>

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.

Evaluation of Smart Home Platform Based on Blockchain

A smart home consists of various sensors and actuators that can communicate with each other. In addition, various home appliances have similar capabilities. Therefore, the smart home became one of the applications of the Internet of Things (IoT). In this application, arise the challenges caused by the integration of various devices that came from various vendors have a different data format and keeps the data's security. Blockchain has been proposed as one solution to address these challenges. In this paper, we investigate and evaluate the performance of the blockchain-based solution for smart home IoT. Our experiment results show that the Blockchain can secure the IoT-based smart home, but it took47% longer for packet delivery.

Enhancing IoT-Based Smart Home Security Through a Combination of Deep Learning and Self-Attention Mechanism

Enhancing IoT-Based Smart Home Security Through a Combination of Deep Learning and Self-Attention Mechanism

IoT-based Smart Home Security System with Machine Learning Models

The Internet of Things (IoT) has various applications in practice, such as smart homes and buildings, traffic management, industrial management, and smart farming. On the other hand, security issues are raised by the growing use of IoT applications. Researchers develop machine learning models that focus on better classification accuracy and decreasing model response time to solve this security problem. In this study, we made a comparative evaluation of machine learning algorithms for intrusion detection systems on IoT networks using the DS2oS dataset. The dataset was first processed to feature extraction using the info gain attribute evaluation feature extraction approach. The original dataset (12 attributes), the dataset (6 attributes) produced using the info gain approach, and the dataset (11 attributes) obtained by eliminating the timestamp attribute was then formed. These datasets were subjected to performance testing using several machine learning methods and test choices (crossfold-10, percentage split). The test performance results are presented, and an evaluation is performed, such as accuracy, precision, recall, and F1 score. According to the test results, it has been observed that high accuracy detection rates are achieved for IoT devices with limited processing power.

Synoptic crow search with recurrent transformer network for DDoS attack detection in IoT-based smart homes

Synoptic crow search with recurrent transformer network for DDoS attack detection in IoT-based smart homes

IoT-Based Smart Home Automation Using ESP32 and Blynk with Enhanced Reliability

IoT-Based Smart Home Automation Using ESP32 and Blynk with Enhanced Reliability

Brain-Computer Interfaces Can Significantly Improve The Quality of Life for Aged People - A Review

As the global population ages, numerous programs have emerged to enhance the quality of life for seniors, addressing factors such as mental and physical well-being, mental illnesses, concurrent health issues, loneliness, and social interactions. Cognitive decline in aging poses a significant public health concern, impacting families and communities emotionally and financially. This review explores the effectiveness of healthcare surveillance combined with Brain-Computer Interfaces (BCIs) in improving memory and focus among a group of healthy seniors. Maintaining a high quality of life for the elderly is crucial, fostering resilience and perseverance. The review data suggests the potential of non-invasive BCIs, integrated with IoT-based Smart Home Technology (SHT), to enhance the Quality of Life (BCI-QOL) for seniors. The review focuses on advancing non-invasive BCI technology to support the health and well-being of older individuals, addressing dementia and physical impairments. It also discusses supportive BCI technologies for smart homes catering to seniors with motor control deficits. This comprehensive review aims to assess the immediate and long-term impact of regional features on the well-being and quality of life of seniors. The BCI-QOL-based system holds promise in enhancing cognition and focus, aiming for security, intuitiveness, and appropriateness for older users.

LEVERAGING IOT AND NATURAL LANGUAGE PROCESSING TO IMPROVE WELL-BEING OF CHRONIC PATIENTS

Abstract According to the Centers for Disease Control and Prevention, in the United States, about 80% of older adults - individuals aged 65 years and older, have at least one chronic health condition. Chronic diseases require ongoing medical attention or limit activities of daily living, which in turn, affects quality of life. Heart diseases, cancer, and diabetes are the leading chronic diseases. Literature shows that timely and frequent follow-ups by providers significantly reduce readmissions of chronic patients and contribute to their well-being. However, health care provides do not have adequate bandwidth for regular patient follow-ups, which is vital for chronic patients. One solution for this growing problem, is to leverage smart home monitoring technologies facilitating self-care, which in turn can contribute to patients’ well-being. The goal of this research is to build and leverage an IoT-based smart home monitoring platform and assess its impact on the well-being of chronic patients. The platform included an mHealth mobile app with daily journaling capability, which allowed patients to blog about their symptoms, daily activities, and feelings. Analyzing patients’ daily blog data with Natural Language Processing (NLP) methods, this research revealed a strong correlation between a patients’ blog entries and their well-being score. The results also demonstrated that patients’ well-being scores can be predicted from such journal entries analyzed at real time, collected remotely from their home environment, leading to timely diagnosis and intervention opportunities. A field trial was conducted involving five heart disease and eight cancer patients for 180 days to validate the conclusions.

Smart Home System With Battery Backup and Internet of Things

This research enhances Smart Home Systems by integrating an Automatic Transfer Switch (ATS) for seamless power source switching between the grid and a backup battery, ensuring uninterrupted operation during power disruptions. An Automatic Battery Charging (ABC) system optimizes battery charging based on its condition, improving energy storage and efficiency. The system provides on-site electrical equipment control and sensor data access via a Human Machine Interface (HMI). Remote monitoring and control through the Blynk app offer convenience. Additionally, an energy consumption estimation feature allows users to estimate billing costs, with the Battery State of Charge (SoC) indicating the remaining battery capacity. Hardware testing showed the system's reliability with a 2-4 second ATS response and ±2-second ABC response. This research offers homeowners reliable power continuity and energy optimization. It contributes to IoT-based smart home systems, demonstrating ATS and ABC effectiveness, advancing both theory and practice for modern smart living.

A Survey on Conflict Detection in IoT-based Smart Homes

As the adoption of IoT-based smart homes continues to grow, the importance of addressing potential conflicts becomes increasingly vital for ensuring seamless functionality and user satisfaction. In this survey, we introduce a novel conflict taxonomy, complete with formal definitions of each conflict type that may arise within the smart home environment. We design an advanced conflict model to effectively categorize these conflicts, setting the stage for our in-depth review of recent research in the field. By employing our proposed model, we systematically classify conflicts and present a comprehensive overview of cutting-edge conflict detection approaches. This extensive analysis allows us to highlight similarities, clarify significant differences, and uncover prevailing trends in conflict detection techniques. In conclusion, we shed light on open issues and suggest promising avenues for future research to foster accelerated development and deployment of IoT-based smart homes, ultimately enhancing their overall performance and user experience.

A new method for vulnerability and risk assessment of IoT

In this paper, we propose a generic vulnerability and risk assessment method for IoT-enabled systems. The main aim is to provide risk detection and vulnerability assessment for IoT-based systems. We present three phases of risk assessment methodology: graph construction, attack path detection, and attack path filtering for high-level attack paths. We give attack path detection, risk level computing, and attack path removing procedures to validate these phases. We represent the IoT-based network as a graphical structure. Then, we construct the topology for a given IoT-based system. The smart home system is considered as a case scenario to present a realistic instance. The National Vulnerability Database (NVD), Common Vulnerability Scoring System (CVSS), and Common Vulnerability Exposures (CVE) metrics are used to assign vulnerabilities to devices. We formulate risk factors to compute risk levels for each node, attack path, and entire graph. We use the modified Depth First Algorithm (DFS) to find all attack paths for a source and target nodes. In addition, we compute risk levels using computing procedures. Further, we filter detected attack paths considering dominance level using computational metrics. We perform the simulation on a custom Python simulator considering the designed IoT-based smart home system. We compare our proposed methods with the state of the art. According to the experimental results, the proposed methods outperform existing vulnerability-based risk assessment models regarding running time complexity and operational cost.

Smart Home Remote Control System Prototype Using Internet of Things (IoT) Based ESP8266 Microcontroller

Technology is developing very rapidly in the current era, there will be several impacts that will result from the development of technology. Electronic equipment control can be done remotely in real time using the IoT system. The problem faced is the concern when leaving home. Because when leaving the house, homeowners sometimes often forget about electronic devices whether they are turned off or still on. IoT can be utilized at home as a smart home system by connecting an electronic device to an android application. This IoT-based smart home system uses an ESP8266 microcontroller which is used to connect between hardware and software that is controlled through the Blynk application. Blynk application is used as an interface that functions to provide input and produce output. This system consists of four sensors, namely the MQ2 sensor to detect gas leaks in the kitchen, the DHT11 sensor to determine the level of humidity and temperature in the bedroom, the PIR sensor to detect human movement at the front entrance, the ULTRASONIC sensor as a detector of the water level in the water reservoir and using three relays as a connector for lights and other electronic components. And using the Blynk application as a control system on the ESP8266 microcontroller.

Fortifying Smart Home Security: A Robust and Efficient User-Authentication Scheme to Counter Node Capture Attacks.

In smart home environments, the interaction between a remote user and devices commonly occurs through a gateway, necessitating the need for robust user authentication. Despite numerous state-of-the-art user-authentication schemes proposed over the years, these schemes still suffer from security vulnerabilities exploited by the attackers. One severe physical attack is the node capture attack, which allows adversaries to compromise the security of the entire scheme. This research paper advances the state of the art by conducting a security analysis of user-authentication approaches regarding their vulnerability to node capture attacks resulting in revelations of several security weaknesses. To this end, we propose a secure user-authentication scheme to counter node capture attacks in smart home environments. To validate the effectiveness of our proposed scheme, we employ the BAN logic and ProVerif tool for verification. Lastly, we conduct performance analysis to validate the lightweight nature of our user-authentication scheme, making it suitable for IoT-based smart home environments.

IoT Based Smart Home Automation and Security

Abstract: Over the past few decades, home automation systems have been increasingly popular because they increase comfort and quality of life. A microcontroller and a smartphone are the main components of most contemporary home automation systems. A smart phoneapplication is used to manage and monitor the home appliances via various types of communication. Due of their narrow scope, the current techniques have limitations. The implementation of this project, "IOT- based smart security and smart home automation," has been completed in order to overcome these problems. The concept focuses on "home automation," which is the control of lights and fans, as well as smart security by sending a picture taken by email to the owner via the internet when an object is recognized. This project will be carried out using the "Raspberry Pi" module. This will be especially beneficial to the crippled and elderly.

Evaluation of IoT-Based Smart Home Assistance for Elderly People Using Robot

In the development of Internet-of-things (IoT)-based technology, there is a pre-programmed robot called Cyborg which is used for assisting elderly people. It moves around the home and observes the surrounding conditions. The Cyborg is developed and used in the smart home system. The features of a smart home system with IoT technology include temperature control, lighting control, surveillance, security, smart electricity, and water sensors. Nowadays, elderly people may not be able to maintain their homes appropriately and may feel uncomfortable performing day-to-day activities. Therefore, Cyborg can be used to carry out the activities of elderly people. Such activities include switching off unnecessary lights, watering plants, gas control, monitoring intruders or unknown persons, alerting elderly people in emergency situations, etc. These activities are controlled by web-based platforms as well as smartphone applications. The issues with the existing algorithms include that they are inefficient, require a long time for implementation, and have high storage space requirements. This paper proposes the k-nearest neighbors (KNN) with an artificial bee colony (ABC) algorithm (KNN-ABC). In this proposed work, KNN-ABC is used with wireless sensor devices for perceiving the surroundings of the smart home. This work implements the automatic control of electronic appliances, alert signal processors, intruder detection, and performs day-to-day activities automatically in an efficient way. GNB for intruder detection in the smart home environment system using the Cyborg human intervention robot achieved an accuracy rate of 88.12%, the Artificial Bee Colony algorithm (ABC) achieved 90.12% accuracy on the task of power saving in smart home electronic appliances, the KNN technique achieved 91.45% accuracy on the task of providing a safer pace to the elderly in the smart home environment system, and our proposed KNN-ABC system achieved 93.72%.