Enhanced Intelligent Smart Home Control and Security System Based on Deep Learning Model

Security of lives and properties is highly important for enhanced quality living. Smart home automation and its application have received much progress towards convenience, comfort, safety, and home security. With the advances in technology and the Internet of Things (IoT), the home environment has witnessed an improved remote control of appliances, monitoring, and home security over the internet. Several home automation systems have been developed to monitor movements in the home and report to the user. Existing home automation systems detect motion and have surveillance for home security. However, the logical aspect of averting unnecessary or fake notifications is still a major area of challenge. Intelligent response and monitoring make smart home automation efficient. This work presents an intelligent home automation system for controlling home appliances, monitoring environmental factors, and detecting movement in the home and its surroundings. A deep learning model is proposed for motion recognition and classification based on the detected movement patterns. Using a deep learning model, an algorithm is developed to enhance the smart home automation system for intruder detection and forestall the occurrence of false alarms. A human detected by the surveillance camera is classified as an intruder or home occupant based on his walking pattern. The proposed method’s prototype was implemented using an ESP32 camera for surveillance, a PIR motion sensor, an ESP8266 development board, a 5 V four-channel relay module, and a DHT11 temperature and humidity sensor. The environmental conditions measured were evaluated using a mathematical model for the response time to effectively show the accuracy of the DHT sensor for weather monitoring and future prediction. An experimental analysis of human motion patterns was performed using the CNN model to evaluate the classification for the detection of humans. The CNN classification model gave an accuracy of 99.8%.

Do households with home security systems at home have a lower perceived probability of victimization?

Using the dataset of the first nationwide victimization survey in Azerbaijan, the current paper analyzed whether home security systems have a relationship with the perceived probability of property crime victimization. The ordinal logistic regression analysis was employed. The first set of results examining the distribution of security systems identified that despite the impact of certain demographics correlates, perceived risk is not a significant correlate of possession or absence of security systems. The presence of security measures at home was associated with a higher perceived probability of victimization level for acquisitive crimes. This study shows the importance of incorporating home security level as an independent variable. The choice of home as a unit of analysis when studying fear of victimization allowed us to ascertain not only the impact of home security systems on perceived victimization risks, but also to find out whether it is dwelling, demographic or neighborhood features that matter more in affecting sense of safety. The key findings of the study are that a) the level of home security systems is positively correlated with PPV, irrespective of individual-level characteristics, and b) perceived risk is not a statistically significant factor when it comes to a decision to purchase security systems.

IoT Applications in Smart Home Security

Robotization has changed into a fundamental piece of our lives. Everybody is completely subject to mechanization whether it is an extraordinary bundling or home robotization. So as to bring home automation into thought, everybody now needs a heterogeneous state security, and in our task on residential robotization, such high security highlights are completely on the best possible consumption. Piezoelectric sensors are compelling for sharpening appropriated wellbeing checking and structures. An intrusion detection system (IDS) is a structure that screens for suspicious movement and issues alarms when such advancement is found. Some obstruction divulgence structures are fit to take practice when poisonous improvement or peculiar action is perceived.

Smart Home Multi-Factor Authentication Using Face Recognition and One-Time Password on Smartphone

Recently, the adoption of smart home technology has been on the rise and becoming a trend for home residents. The development of Internet-of-Things (IoT) technology drives the smart home authentication system with biometric systems such as facial recognition, fingerprint, and voice control techniques. In the context of homeowners, security is always the primary concern. However, conventional home security and the existing smart home security system have some limitations. These techniques use single-factor authentication, which provides limited protection for home security. Therefore, this project proposed a design for smart home multi-factor authentication using facial recognition and a one-time password sent to smartphones for a home security system. Rapid application development was the methodology for conducting this study. A usability evaluation suggested that the proposed smart home multi-factor authentication is acceptable, but some usability issues can be improved in the future.

Thief Detection and Prevention: Home security using IoT (Internet of Things)

Nowadays the crime has raged to a new height and providing security frames is the most important part of life. When it comes to word security first thing that comes in the mind is house. Providing security to the house gets much easier with IOT sensors. This project’s aim is to provide security to house without any human interference. This system also focuses on not only detection of some unauthorized access, but also prevents the house from unauthorized access using sensors. In the past technology the system just used to detect the unauthorized access in the house or bank etc., but in this system it also prevents the house from thieves. Keywords: Internet of things (IOT), Arduino, Sensors, Security, Node MCU, Relay Module

PROTOTYPE SECURITY SYSTEM OPENS DOORS WITH TELEGRAM BOT INTERNET-BASED OF THINGS

AbstrakTechnological advances today are very developed, so it can help the community in monitoring home security. One example is offices or houses owned by themselves. In this research I am interested in doing research and development in the field of IoT where this research aims to build a system that can help homeowners to provide security at the door of the house during remote travel. With fingerprint system equipped with door monitoring using botTelegram this system itself is implemented with the concept of internet of things (IoT). In this study using the concept of internet of things implemented in the automatic door system using microcontroller that serves as a prototype regulator so that the prototype can run properly. In this system has an interface in the form of Telegram-based applications by utilizing BotTelegram that serves as a communication tool between users and the system. In this system, users can monitor the security of the door when traveling or at home. Based on the results of the study, it can be concluded that the system can run with a percentage of 85% where the system can open automatic doors with fingerprint configuration data that has been registered in the fingerprint module. Keywords: Internet of things, fingerprints, telegram bots, nodemcu, Door Security