Intelligent Home Environment Research Articles

A Next-Generation Iot-Integrated Arduino System for Intelligent Home Environment Monitoring

To stay informed about home living conditions, a home environment monitoring device was developed using an Arduino platform. Utilizing the Arduino UNO hardware and an array of sensors, the device captures real-time data on indoor temperature, humidity, light levels, and the presence of combustible gases. This information is then uploaded to a monitoring platform, allowing for adjustments to the surrounding environment based on the collected data. Testing has demonstrated that the design effectively gathers and reliably uploads home environment data, meeting the requirements for remote monitoring of living conditions. The device offers several advantages, including low cost, minimal power consumption, a simple structure, and easy installation. DOI: https://doi.org/10.52783/pst.797

IOT based Intelligent Home Safety Control Centre

This research focuses on the development of an IOT (IoT) intelligent home safety control centre. System consists of a user side, a safety control centre, and a terminal node, with each component having specific functionalities to enhance the safety and safety of the intelligent home environment. Key modules include data encrypting/decrypting, safety communication, control centre of user access and verification of node identity, reliable platform for credibility verification, and log inspect and alarm. The system ensures data safety, authentication, credibility analysis, and system monitoring, thereby improving the safety performance and running efficiency of the intelligent home system.

Machine-learning-assisted wearable PVA/Acrylic fluorescent layer-based triboelectric sensor for motion, gait and individual recognition

Flexible sensors with precise perception ability play an important role in environmental sensing. The organic fusion between the sensitive structure and sensitive material presents some challenges for the overall design and integration of the sensor, including designing multiple sensitive branch structures, simplifying system design, and sensing multiple stress modes. Here, based on a same-plane interdigital electrode design, we developed a triboelectric sensor and its array (TSA) that mimics the synaptic structure of a neural cell. The interdigital electrode structure can effectively simulate the synaptic structure of neural cells and achieve effective perception of multi-directional stress. The enhancement of the triboelectric effect is achieved through layer-by-layer self-assembly technology between PVA film and acrylic fluorescent layer. The interface effect between PVA film and acrylic fluorescent layer can effectively enhance the electrostatic effect. Further analysis with deep learning CNN can extract higher-level motion features. A single sensor can be used to identify which part of the body is producing the movement (98.89%). A TSA with different numbers of sensors can achieve high-precision recognition of gesture numbers (99.25%), foot shape, gait (99.23%), and individuals (99.75%). The sensor can be flexibly manufactured and integrated according to specific application scenarios. The long-term goal is to effectively help limb-injured patients with rehabilitation therapy and build intelligent home environment.

Self-Adaptive PDLC Control Strategy With Smart Light Intensity Adjustment Using Photovoltaic-Thermoelectric Hybrid Energy Supply Technology

As an electronically controlled glass whose transmittance varies with voltage, polymer dispersed liquid crystal (PDLC) has the advantages of good privacy, high transmittance, fast response speed, and good heat shielding performance. It can be applied to electronically controlled building windows. However, PDLC needs to consume extra power, and electronically controlled PDLC has not yet been integrated with intelligent home environment control. In this paper, we proposed an adaptive variable voltage step-size fast control algorithm in Field Programmable Gate Array (FPGA)-based home environment control system, which is powered by photovoltaic-thermoelectric (PV-TEG) hybrid sources. The power supply voltage of the PDLC can be dynamically changed through the adaptive Pulse width modulation (PWM) duty cycle adjustment technology to realize intelligent transmittance control, thereby improving the indoor lighting environment. Experimental results show that by applying the proposed algorithm, the indoor temperature is reduced by 2°C, and the adaptive and stable regulation of the optimal indoor illumination of 500 Lux can be completed in less than 2.5 seconds. This paper provides a feasible solution for the low-energy intelligent integrated control and monitoring of the modern home environment.

A DCNN-LSTM based human activity recognition by mobile and wearable sensor networks

Systems for recognizing human activities are being developed as part of a larger framework that will allow for continuous monitoring of human behavior in intelligent home environments for elderly care, ambient assisted living, rehabilitation, sports injury detection, surveillance, and entertainment. The most challenging component of the sensor-based human activity detection pipeline for mobile and wearable devices is extracting relevant features. Using mobile and wearable sensors, the most challenging step in the pipeline for recognizing the behaviors of humans is extracting relevant features. The algorithm's performance is affected by feature extraction, which also reduces computation complexity and time. However, existing methods of recognizing human activity rely on hand-crafted characteristics that cannot handle complicated behaviors. There are several known systems for recognizing individual activities, including deep learning, but more must be developed to recognize transitions between actions. To solve these challenges, a new deep learning-based system is proposed for recognizing human actions in this research. Initially, a fuzzy logic-based genetic algorithm (FGA) is used for extracting features from sensor data. The introduced feature selection approach, called PSOACO, aims to enhance the particle swarm optimizer (PSO) algorithm's performance through the ant colony optimizer (ACO) operators. Then, the deep learning approach, named Deep Convolutional Neural Network and Long Short Term Memory (DCNN-LSTM), is used to classify the activities. F1 Score, Recall, Precision, and Accuracy are used to analyze the implementation's effectiveness. The overall recognition accuracy of the proposed model for UCI-HAR, PAMAP2, and WISDM datasets is 99.92%, 99.86%, and 99.94 %. According to the implementation, the HAR with privacy performs better than existing methods done on the Python platform.

Smart Home Simulation in CoppeliaSim Using C# Through WebSocket

Purpose: The increasing integration of intelligent devices and automation technologies in our daily lives has led to the development of smart homes, where various devices and systems are interconnected to provide enhanced comfort, convenience, and energy efficiency. Simulation tools are vital in designing, testing, and validating innovative home systems before their physical implementation. This research paper presents an intelligent home simulation framework implemented in CoppeliaSim. The simulation framework utilizes the C# programming language and leverages WebSocket communication to establish real-time interactions between the simulation environment and external applications. It discusses the architecture and implementation details of the simulation framework, highlighting the integration of CoppeliaSim, C#, and WebSocket. Design/Methodology/Approach: CoppeliaSim was chosen as the simulation platform due to its robust features, flexibility, and support for robotics and automation simulations. The simulation framework was developed using the C# programming language. C# provides a rich set of libraries and tools for efficient development, and its integration with CoppeliaSim allowed seamless communication and control of the simulated intelligent home environment. WebSocket communication established real-time interactions between the simulation environment and external applications. Findings/Result: The simulation framework successfully created a realistic, intelligent home environment, accurately representing various components and systems in typical smart homes. It effectively replicated real-world behaviors and interactions of intelligent home devices. The innovative home simulation framework highlighted its ability to accurately simulate an intelligent home environment, facilitate real-time interactions, and evaluate system performance. Originality/Value: The integration of CoppeliaSim, C#, and WebSocket communication in developing an intelligent home simulation framework presents a novel approach. While CoppeliaSim is a widely used simulation platform, combining C# and WebSocket communication provides an innovative and efficient way to create a brilliant home environment and enable real-time interactions with external applications. Paper Type: Simulation-based Research.

Deep learning assisted ternary electrification layered triboelectric membrane sensor for self-powered home security

In the era of the Internet of Things (IoT), home security has become increasingly critical. Here, we have developed a ternary-electrification-layered triboelectric membrane sensor (TEL-TMS) as a cost-effective approach for self-powered home security. This triboelectric membrane sensor holds a collection of compelling features, including decent flexibility and transparency, enabling a tight attachment to the curved surfaces. By integrating it into household devices such as doors, windows, and safe cases, comprehensive monitoring coverage for the entire home can be achieved. The results showed that the TEL-TMS has a speed range detection of 5–165 mm/s, a response time of 0.32 s, an error rate of less than 1%, and exceptional stability (>10,000 cycles). With the further introduction of machine learning algorithms, the sensor can identify different motion states and activity patterns with a classification accuracy of up to 99.2%. Moreover, it is easily detachable and reusable, offering wide applicability. To facilitate practical applications, a custom mobile application (APP) based on built-in algorithms has been developed for one-click status monitoring and intelligent home environment recognition. The development of this TEL-TMS represents a solid step towards self-powered smart home security.

Smart home healthcare for chronic disease management: A scoping review.

Chronic disease is the leading cause of morbidity and mortality on a global scale. Recent use of intelligent home environments for remote health monitoring has allowed patients to feel safe at home. By encouraging patient engagement and, if necessary, care delivery, smart home environments may be a useful method for managing chronic diseases at home. The purpose of this study is to synthesize the evidence on the usage of smart healthcare in the home for chronic illness management. We conducted a scoping review using the Joanna Briggs Methodology and searched three databases from 2017 to 2022 for original research papers on smart healthcare, smart home technology, home-based technology, home monitoring, and physiological monitoring for chronic illness management. We did a descriptive study on the pertinent data we collected, as well as an analysis of whether the devices met its objectives. The final analysis included nine papers, the majority of which were randomized controlled trials. All of the studies were carried out in developed countries. The gadgets or smart healthcare in these studies are categorized based on the technology used and the outcomes measured. Respiratory, weight, and ballistocardiograph measurements, as well as changes in questionnaire ratings, hospitalization, activity monitoring, device acceptability, medication adherence, exercise capacity, and body function, were all measured. Smart healthcare applications boost health by monitoring health and wellness, recording physical activity and rehabilitation, and improving overall quality of life. Not all smart home applications, however, served their intended purpose. As a result, more research into the efficacy of smart healthcare is needed to improve its application for chronic illness treatment.

Smart home design based on Alibaba Cloud

With the continuous progress of science and technology, the pace of social informatization is also accelerating, people's demand for a safe, comfortable and controllable intelligent home environment is growing. In this paper, we design a iot based on ali cloud platform control system of smart home solutions, choose stm32 microcontroller as the main control unit, ali as a hardware device, a time server and related sensors for home fire alarm, intelligent entrance guard, infrared detection, temperature and humidity detection display, light intensity, and other functions. The ESP8266 module is used as the wireless communication module to realize the data communication between hardware and software. A WeChat small program is designed as the upper computer of the user terminal to realize the intelligent management of the user terminal for home. Users can check the running status of related devices at home at any time in the WeChat mini program, such as temperature and humidity, smoke status, and whether someone has invaded.

Certificateless Authentication Scheme Based on Blockchain in Smart Home Network

The traditional centralized intelligent home network security authentication schemes have security problems such as integrity and confidentiality, while the distributed schemes have the problem of delayed authentication. These schemes are not suitable for the intelligent home environment of edge computing. To solve these problems, a certificateless smart home network authentication scheme based on blockchain and certificateless cryptosystem is proposed to realize mutual authentication among users, intelligent terminals (ITs), and intelligent gateways (IGs). The aggregation signature scheme of certificateless identity is introduced into the authentication of intelligent terminals in smart home network. The IG only needs to generate a signature to complete the identity authentication of multiple ITs. Compared with other authentication schemes, the security verification and performance analysis show that the proposed scheme uses less computation overhead and achieves more security features.

Design of Remote Real-Time Monitoring and Control Management System for Smart Home Equipment Based on Wireless Multihop Sensor Network

With the intensification of the pace of modern urban life and the increase of work pressure, people have higher requirements for the safety and comfort of home. However, the rapid development of Internet of Things technology, wireless communication technology, embedded technology, and data fusion technology has made this desire possible, thus making intelligent and safe. Using wireless sensor network, intelligent home system can realize the real-time collection of object location and state information, remote operation of home equipment, as well as the monitoring of the information of personnel activities, etc. However, how to efficiently analyze and integrate these information to be applied to the intelligent home system is one of the problems that need to be solved; further study of the smart home system of personal behavior identifies fire monitoring and remote monitoring of mining applications. Such information collection and integration of technology and method are given based on wireless sensor network transfer of key technologies; intelligent home security monitoring system based on Bayesian networks is proposed and a card. According to the theoretical fire monitoring method, and based on the Bayesian belief network personal behavior identification model, this paper provides the theory and method for the remote safety monitoring and control in the intelligent home environment. In this paper, based on the multisensor data fusion of smart home security, real-time monitoring alarm platform is built. Through the multisensor detection module for the home fire gas human infrared monitoring, the detected data is transmitted to the data fusion center for processing; processed results are uploaded to the cloud through the Internet. When abnormal conditions occur, users can log in to the mobile phone client to view abnormal alarm results.

Smart-Home Environment to Support Homework Activities for Children

The Central Nervous System of humans continuously evolves when children engage in new activities. These activities are progressing from learning to eat as a baby over playing during childhood up to homework in all its dimensions. These activities, which are meaningful and relevant to everyone, constitute what occupational therapists call “occupations”. The successful execution of these occupations makes the development of new roles as well as the construction of a correct state of health possible. Due to these reasons, occupational performance is a fundamental part of development throughout childhood. Homework is complex and extends beyond the school context to the home. For many children, the performance of these homework items is a difficult challenge or even impossible to overcome without the help of an adult. This article presents the design, implementation, and functional validation of an intelligent home environment that uses homework activities as a support tool for children with or without attention disabilities. In this project, the Internet of Things (IoT) paradigm is combined with the development of robotic assistance to implement an intelligent home environment. In this environment, we have included intelligent things (where the usual study chairs and desks become smart objects) that determine in real-time the child's behavior during the development of homework and a robotic assistant which interacts with the children providing the necessary accompaniment (supervision and guidelines) just as a therapist would do. This development has been functionally validated by tests on several school-aged children without pathologies. In a later phase of the study, the proposal will be validated with children with different pathologies with an impact on learning, including Attention Disorder Hyperactivity Disorder (ADHD). The goal is the generation of intelligent places for therapeutic purposes within the home as assistance for children having difficulties to work with their homework assignment due to ADHD.

Intelligent Home Communication Design Based on Home Computer

Along with the continuous development of computer technology, the increase in the number of computer popularization, especially the microcomputer used in the home environment, has brought a lot of convenience to human life. In recent years, intelligent home has developed rapidly, and intelligent home environment provided higher spiritual material experience for users. However, in the remote control of smart homes, most of them use network communication chips such as 3G and 4G, which wastes computer resources in the home environment. Therefore, this paper proposes an intelligent home design based on home computer, hoping to reduce the development cost of existing smart home technology and eventually accelerate the popularization of smart home.

Smart Home Automation System based on Arduino

<p>Intelligent home environments are environments that attempt to ease the user's life in different ways and make it more comfortable using the technology. This paper present the principle of realizing a home control interface based on an. Arduino UNO R3 board equipped with a programmable ATmega328 microcontroller. The sketch to be uploaded to the Arduino is written in C language. A desktop application was created in C # language to communicate with the Arduino via the serial port, thus facilitating repetitive tasks such as lighting control, opening / closing doors. A temperature / humidity sensor, an alarm (motion sensor + buzzer), garden lighting and automatic control of the TV via an IR receiver + IR transmitter have been added without forgetting manual control</p>

Real-Time Remote Health Monitoring Systems Using Body Sensor Information and Finger Vein Biometric Verification: A Multi-Layer Systematic Review.

The development of wireless body area sensor networks is imperative for modern telemedicine. However, attackers and cybercriminals are gradually becoming aware in attacking telemedicine systems, and the black market value of protected health information has the highest price nowadays. Security remains a formidable challenge to be resolved. Intelligent home environments make up one of the major application areas of pervasive computing. Security and privacy are the two most important issues in the remote monitoring and control of intelligent home environments for clients and servers in telemedicine architecture. The personal authentication approach that uses the finger vein pattern is a newly investigated biometric technique. This type of biometric has many advantages over other types (explained in detail later on) and is suitable for different human categories and ages. This study aims to establish a secure verification method for real-time monitoring systems to be used for the authentication of patients and other members who are working in telemedicine systems. The process begins with the sensor based on Tiers 1 and 2 (client side) in the telemedicine architecture and ends with patient verification in Tier 3 (server side) via finger vein biometric technology to ensure patient security on both sides. Multilayer taxonomy is conducted in this research to attain the study's goal. In the first layer, real-time remote monitoring studies based on the sensor technology used in telemedicine applications are reviewed and analysed to provide researchers a clear vision of security and privacy based on sensors in telemedicine. An extensive search is conducted to identify articles that deal with security and privacy issues, related applications are reviewed comprehensively and a coherent taxonomy of these articles is established. ScienceDirect, IEEE Xplore and Web of Science databases are checked for articles on mHealth in telemedicine based on sensors. A total of 3064 papers are collected from 2007 to 2017. The retrieved articles are filtered according to the security and privacy of telemedicine applications based on sensors. Nineteen articles are selected and classified into two categories. The first category, which accounts for 57.89% (n = 11/19), includes surveys on telemedicine articles and their applications. The second category, accounting for 42.1% (n = 8/19), includes articles on the three-tiered architecture of telemedicine. The collected studies reveal the essential need to construct another taxonomy layer and review studies on finger vein biometric verification systems. This map-matching for both taxonomies is developed for this study to go deeply into the sensor field and determine novel risks and benefits for patient security and privacy on client and server sides in telemedicine applications. In the second layer of our taxonomy, the literature on finger vein biometric verification systems is analysed and reviewed. In this layer, we obtain a final set of 65 articles classified into four categories. In the first category, 80% (n = 52/65) of the articles focus on development and design. In the second category, 12.30% (n = 8/65) includes evaluation and comparative articles. These articles are not intensively included in our literature analysis. In the third category, 4.61% (n = 3/65) includes articles about analytical studies. In the fourth category, 3.07% (n = 2/65) comprises reviews and surveys. This study aims to provide researchers with an up-to-date overview of studies that have been conducted on (user/patient) authentication to enhance the security level in telemedicine or any information system. In the current study, taxonomy is presented by explaining previous studies. Moreover, this review highlights the motivations, challenges and recommendations related to finger vein biometric verification systems and determines the gaps in this research direction (protection of finger vein templates in real time), which represent a new research direction in this area.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents’ wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident’s feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

Intelligent Home Automation and Environment Creation Using Arm9

Intelligent Home Automation and Environment Creation Using Arm9

Functional organization of an artificially intellectualized home environment based on mandami and takagi-sugeno procedures

A substantial amount of scientific research and publications in the smart home domain deal with different aspects of fuzzy logic application. However, we feel there is a general lack of complex modelling done of the whole smart home environment. So, the main goal of this paper is threefold: 1) to present a virtual model of a smart home environment to be intellectualized, 2) to demonstrate the efficiency of Mamdani type inference procedure in modelling of intellectual behaviour of the environment and 3) to show that it is possible to minimize the quantity of fuzzy rules by taking the hierarchical approach and adding the Takagi- Sugeno inference procedure to empower it. The results of computerized modelling and simulation are delivered in the paper demonstrating the practical viability and efficiency of the theoretical approach. The model of the intelligent home environment was used in two projects. Since a formalized analytical method for evaluating the sensitivity of system parameters still does not exist, the experimental sensitivity simulation in our case is performed and presented in this paper. Characteristics obtained in the modelled virtual environment can be easily expanded and used in a real home environment by changing pixels into real coordinates and the light intensity into other real variables.

Development of a Multisensor Embedded Intelligent Home Environment Monitoring System Based on Digital Signal Processor and Wi-Fi

Wi-Fi has been widely adopted by families in Taiwan, for it has the advantage of designing family monitoring systems with its wide usage. Therefore, there are many advantages for designing family monitoring systems. Based on our previous research architecture, we implemented a monitoring system for family environments based on Wi-Fi and sensor technologies. Humidity, gas, smoke, and temperature sensors are applied for designing and developing the intelligent home environment monitoring system in this paper. A system prototype is proposed in this research, and the experimental results are discussed. The results showed that the system is workable and remarkable for environment monitoring. The contribution of this is to propose a framework for future home environment monitoring system developers to refer to.

Hardware Design of Smart Home System based on zigBee Wireless Sensor Network

In this paper, the design of the smart home system based on ZigBee technology and GSM / GPRS network, the smart home system the hardware circuit, using the CC2430 ZigBee wireless sensor networks, real-time acquisition of the parameters of temperature, humidity, th ree tables, infrared, smoke, gas, fire, theft alarm, home appliances Appliances such as home environment, through the wireless networking of multiple monitoring devices, household appliances in the home environment remote control and image of remote monitoring system by combining the formation of a new type of smart home system, provides a feasible method for intelligent home environment.