Garage Door Research Articles

Performance evaluation of multiclass classification models for ToN-IoT network device datasets

Internet of things (IoT) technology has empowered tangible objects to establish internet connections, facilitating data exchange with computational capabilities. With significant potential across sectors like healthcare, environmental monitoring, and industrial control, IoT represents a promising technological advancement. This study explores datasets from ToN-IoT’s IoT devices, focusing on multi-class classification, including normal and attack classes, with an additional aim of identifying potential attack sub-classes. Datasets comprise various IoT devices, such as refrigerators, garage doors, global positioning systems (GPS) sensors, motion lights, modbus devices, thermostats, and weather sensors. Comparative analysis is conducted between two prominent multiclass classification models, extreme gradient boosting (XGBoost) and light gradient boosting machine (LightGBM), utilizing accuracy and computational time metrics as evaluation criteria. Research findings highlight that the LightGBM model achieves superior accuracy at 78%, surpassing XGBoost 74.31%. However, XGBoost demonstrates an advantage with a shorter computational time of 1.23 seconds, compared to LightGBM 6.79 seconds. This study not only provides insights into multiclass classification model selection but also underscores the crucial consideration of the trade-off between accuracy and computational efficiency in decision-making. Research contributes to advancing our understanding of IoT security through effective classification methodologies. The findings offer valuable information for researchers and practitioners, emphasizing the nuanced decisions needed when selecting models based on specific priorities like accuracy and computational efficiency.

Home Automation Using AI Tool

Home automation is becoming increasingly popular as people seek to simplify their lives and improve their living spaces. In this project, we explore how Husky Lens, an AI-powered image recognition sensor, can be used to control home automation systems with the help .of an Arduino board and a relay. By training Husky Lens to recognize different hand gestures or objects, we can use these gestures or objects to control various appliances or devices in our homes. For instance, a user can simply wave their hand in front of the Husky Lens sensor to turn on or off the lights or fan, or use an object such as a Smartphone to activate a door lock or a garage door. The system is built using an Arduino board to communicate with the Husky Lens and relay module, which in turn controls the appliances or devices. This provides a low-cost and customizable solution for home automation that can be easily expanded to incorporate additional features or devices.

Computer vision based first floor elevation estimation from mobile LiDAR data

First Floor Elevation (FFE) of a house is crucial information for flood management and for accurately assessing the flood exposure risk of a property. However, the lack of reliable FFE data on a large geographic scale significantly limits efforts to mitigate flood risk, such as decision on elevating a property. The traditional method of collecting elevation data of a house relies on time-consuming and labor-intensive on-site inspections conducted by licensed surveyors or engineers. In this paper, we propose an automated and scalable method for extracting FFE from mobile LiDAR point cloud data. The fine-tuned yolov5 model is employed to detect doors, windows, and garage doors on the intensity-based projection of the point cloud, achieving an mAP@0.5:0.95 of 0.689. Subsequently, FFE is estimated using detected objects. We evaluated the Median Absolute Error (MAE) metric for the estimated FFE in Manville, Ventnor, and Longport, which resulted in values of 0.2 ft, 0.27 ft, and 0.24 ft, respectively. The availability of FFE data has the potential to provide valuable guidance for setting flood insurance premiums and facilitating benefit-cost analyses of buyout programs targeting residential buildings with a high flood risk.

Open Globe Injuries From Garage Door Springs.

We describe seven patients who were attempting to repair their garage door when a spring dislodged at high velocity, resulting in open globe injury. All patients were seen at Massachusetts Eye and Ear between the years 2008 and 2023. Their final visual acuities ranged from 20/125 to no light perception. Open globe injury appears to be a risk of attempts to repair a garage door by people who are inexperienced in doing so. [Ophthalmic Surg Lasers Imaging Retina 2023;54:666-669.].

Rancang Bangun Purwarupa Alat Pembuka Pintu Garasi Menggunakan Limit Switch dan Fingerprint Berbasis Arduino

In everyday life, many activities are commonly performed manually. Therefore, it is not surprising that microcontroller-based technology is now being implemented, allowing manual processes to be automated. This research aims to address the dependency on human labor in manually opening and closing garage doors. A garage door is a mechanical device used to enclose the parking space for vehicles inside a house. The problem often faced by garage owners is the need for human assistance in opening the garage door. In this research, an innovative automatic device is proposed to open and close the garage door using a pressure sensor using limit switch and fingerprint sensor based on the Arduino microcontroller. By utilizing this technology, garage owners can easily operate the garage door without human intervention. The pressure sensor detects the position of the vehicle near the door, while the fingerprint sensor is used to provide restricted access only to authorized users.

A new Intrusion Detection System for Secured IoT/IIoT Networks based on LGBM

The Internet of Things (IoT) is one of the technologies used in many fields today. Cyber attacks against IoT/Industrial IoT (IIoT) networks, which are increasingly used thanks to the convenience it provides, are constantly increasing. Detection of attacks against IoT/IIoT networks is one of the popular topics recently. The development of a dataset for IoT applications is essential for the intrusion detection in IoT networks. In this context, the ToN_IoT dataset created in the laboratory of UNSW Canberra (Australia) is one of the most comprehensive datasets that can be used to detect cyber attacks on IoT networks. In this study, fridge, garage door, GPS tracker, modbus, motion light, weather, thermostat datasets related to IoT sensors from ToN_IoT datasets were used. The datasets used were subjected to multi-class classification with the Light Gradient Boosting Machine (LGBM) classifier proposed in the study. The obtained results were compared with the literature and it was seen that the proposed method provided the highest classification performance in the literature. It has been determined that the proposed method is effective in preventing cyber attacks on IoT/IIoT networks.

Automatic Garage Door and Smart Lighting System

The inconvenience of manually opening your garage door, driving your car out, and then closing the door while you are in a rush is what our application aims to solve. By interacting with an IoT network, smart garage doors may control your garage door using the power of your smartphone. Additionally, it does away with the trouble of physically locking the door. This will also provide intelligent notifications for enhanced monitoring, are some of the system's remarks. The goal of this project is to design and build a low-cost system that monitors garage doors and transmits their status to a receiver unit located in a convenient location inside the user's home. This allows the user to monitor their garage doors from the comfort of their own home, rather than having to go outside and inspect the garage. The receiver unit includes a screen for displaying system information as well as LEDs for viewing the status of the garage door from a distance. The system has enough range to place the receiver anywhere in the house while providing accurate garage door status. By designing and building this, we hope to increase home security.

A deep transfer learning approach for IoT/IIoT cyber attack detection using telemetry data

The rise of internet connectivity across the globe increases the count of IoT (internet of things)/IIoT (industrial internet of things) devices exponentially. The objects/devices which are connected to the internet are always prone to malicious attacks at various levels, such as physical, network, fog, and applications, which exist in the IoT architecture. Many researchers have addressed this issue and designed their own solutions based on machine and deep learning techniques. It is undeniable that deep learning outperforms machine learning (ML), but it necessitates a massive amount of datasets with appropriate labels. In this work, the deep transfer learning (TL) technique has been adapted for gated recurrent unit (GRU). Each model is trained using a dataset that belongs to one source IoT device (source domain), and this trained model is used to classify the malicious traffic in another dataset that belongs to some other IoT device (target domain). This approach is used for binary classification. These transfer learning models have been evaluated using an IoT/IIoT telemetry dataset called ToN IoT which comprises the sensor data generated from the seven different types of IoT devices. The highest accuracy achieved by IoT garage door was upto 99.76% as a source domain by fixing IoT thermostat as target domain. These models were also evaluated using some more metrics such as precision, recall, F1-measure, training time and testing time. By implementing transfer learning based GRU model, the accuracy of the model is improved from 69.20% to 99.76%. Moreover, to prove the efficiency of the proposed model, it is compared with state of art deep learning model and its results were analyzed in a detailed manner.

Design and Fabrication of a Low-Cost System for Smart Home Applications

Smart systems and security got impressive attention and development in recent years, which have been appeared in the terms of smart homes, intelligent security, and the Internet of Things (IoT). Home automation comprises the controlling of the electrical appliances in the home wirelessly or automatically. Many different integrated circuits, sensors, modules, and embedded systems are available to be compatible to integrate with smart homes. In order to apply the concept of smart homes, many issues should be considered like as providing a user-friendly, reliable, secure, and cost-effective. In this paper, an effective and low-cost smart home system is designed and implemented based on the Arduino microcontroller boards with its compatible modules. The proposed work employed many types of sensors to carry out the tasks for the smart home for a couple of the essential segments, the first one is home security and the latter one is home automation. The antitheft home segment is based on the laser source directed on the light-dependent resistor and infrared sensor; once the thief tries to cut the laser or passes beside the sensor, the alarm will be switched on. The later segment aims to detect the fire occurring by the means of the flame sensor, gas leakage detection by the MQ-05 sensor, servo motor to opening/closing the garage door, LCD to display the status of the all-utilised sensors, and finally, the Bluetooth module to controlling the garage door wirelessly. To increase the system performance and reliability the Arduino Nano and the Arduino Leonardo board are utilised.

Automatic Control System for Vehicle Parking

Abstract: Now a day’s vehicle parking is an important issue and day by day its necessity is increasing. In India we are still using the manual vehicle parking system and that is why we are facing problems like wastage of time and fuel finding free space around the parking ground when we need to park our car which requires a good amount of lighting. Another issue is chaos that happens while parking because there is no particular system anyone can park anywhere that sometime causes damage to the vehicles while moving out or in the parking lot. Security is also an issue there. To solve these problems we are introducing new car parking system. The system works as follows: The driver will place the vehicle in front of the garage door and there will be a monitor available where the number of available parking slots will be displayed. Keywords: Parking Slot, Arduino Uno, LCD display, IR sensor etc.

The Wound of Time

The Wound of Time

Access Control and Security System Via Bluetooth Application on Android Smartphone

Smartphone technology can be applied not only to establish communication needs but also to support other purposes. One of them is related to personal safety and security functions. It is undeniable that criminal acts can occur anytime and anywhere. Even in a private or residential area, theft could happen. Smartphone and sensor technology can be used as a solution to encounter this problem. In this case, it can be utilized to improve the security control system of the gate or garage door at home. This research presents a prototype of a gate and garage door control and security system that operates through an application on an android smartphone. The application of HC-05 Bluetooth is used to send signals from the smartphone to the Arduino Uno microcontroller, while the micro servo acts as a locking mechanism on the gate itself. The buzzer function is presented to notify homeowners when the gate or garage door is open for more than 15 seconds. This prototype can control gates and garage doors with an average connection time of only about 5 seconds. Thus, this prototype is feasible to use as an alternative to control and improve housing security systems.

Securing the IoT System of Smart City against Cyber Threats Using Deep Learning

The idea of a smart city is to connect physical objects or things with sensors, software, electronics, and Internet connectivity for data communication through the Internet of Things (IoT) devices. IoT enhances productivity and efficacy intelligently using remote management, but the risk of security and privacy increases. Cyber threats are advancing day by day, causing insufficient measures of security and confidentiality. As the hackers use the Internet, several IoT vulnerabilities are introduced, demanding new security measures in the IoT devices of the smart city. The threads concerned with IoT need to be reduced for efficient Intrusion Detection Systems (IDSs). As a result, machine learning algorithms generate correct outputs from a large and complicated dataset. The output of machine learning could be used to detect anomalies in IoT‐network systems. This paper employed several machine learning classifiers and a deep learning model for intrusion detection using seven datasets of the TON_IoT telemetry dataset. The proposed IDS achieved an accuracy of 99.7% using Thermostat, GPS Tracker, Garage Door, and Modbus datasets via voting classifier.

Why Utilities Are Crucial to All Smart Cities

The transition from urban centers as we know them today to the city of the future—a “smart” city—is inevitable. We've witnessed such fundamental shifts in all walks of life. Analog phones went digital and became “smart.” Household devices evolved from being push button garage doors, thermostats, and ovens, to sensor‐driven controllable technologies. Today, we are seeing electric water and gas meters, streetlights, waste bins, along with a litany of other municipal equipment becoming “smart” as well.

Benefits of Garage Door Repair Services

Benefits of Garage Door Repair Services

Review: Eileen Gray, Designer and Architect

Review: <i>Eileen Gray, Designer and Architect</i>

Cost Effective Automatic Garage Door Mechanism With Variable Speed

The aim of our project is to provide safety as well as security to people from the largest electromechanical objects which are available at home. An automatic garage door mechanism was selected as a subject of our research as it is the complex electro-chemical object at home. As technology increases day by day every households installs this automatic garage door mechanism. The major problem with the conventional garage door system was child entrapment and people are in wheelchair are also affected by the conventional garage door mechanism. We are planning to eliminate the problems which are available in the conventional garage door mechanism by providing some extra safety and security features to our automatic garage door mechanism.an automatic garage door mechanism is the combination of mechanical and electrical system in which mechanical system guides the system as well as direct the shutter movement While, electrical system is used to provide energy to mechanical part. Sectional overhead doors have become the standard door type for garages. Our motive behind this study is to increase the safety and security of the automatic garage doors as well as make it to operatable at variable speed. Safety of the equipment is going to be increased by an area presence sensor which will reduce the child entrapment cases. Only authorized people can access the garage, that will increase the security and use of the VFD-variable frequency drive will make the garage door operatable at owner desired working speed. These shutters can be used for home applications as well as industrial applications.

Virtual Reality Interactive Method and Device Based on Wireless Communication Tracking

Virtual reality is a computer system that creates a virtual world and then experiences through multiple senses. It is generated by a computer and stimulated by perception systems such as hearing, vision, touch, taste, and smell, providing users with a personal experience. Human‐computer interaction is one of the core technologies of virtual reality. Wireless communication is the transmission of communications over long distances between multiple nodes without propagation through conductors or cables and can be carried out using radios, radios, etc. Wireless communication includes a variety of fixed, mobile, and portable applications such as two‐way radios, mobile phones, personal digital assistants, and wireless networks. Other examples of radio wireless communication are GPS, garage door remotes, wireless mice, etc. Most wireless communication technologies use radio, including Wi‐Fi with distances of just a few meters, but also deep space networks that communicate with Voyager 1 and distances of over millions of kilometers. With the continuous development of sensors and other supporting hardware facilities, the current development of human‐computer interaction in virtual reality has made rapid progress. In the research to be conducted in this article, the virtual reality system used in this article cleverly integrates the three characteristics of immersion, interactivity, and conception, so that the experimenter can obtain more realistic data in comparison. To this end, this article first gives a general introduction to virtual reality technology and wireless communication tracking technology and then explains how to use wireless communication tracking technology to make the virtual reality interactive system smoother and smoother, as well as the introduction of its devices. This article explores and analyzes the possible or existing problems of wireless communication tracking technology in virtual reality interaction, hoping to contribute to the wider application of wireless communication tracking technology in virtual reality interaction. The positioning experiment on the wireless mobile signal identification points can be obtained. Among the 40 sensor nodes that are randomly deployed, when the interval of adjusting the mobile signal identification point to broadcast the current position information is 5 s, the average positioning error of the node is about 1.5 m; when the interval is 3 s, the average positioning error of the node is about 1.76 m. It can be seen that the positioning error of the node increases as the interval between the mobile signal identification points increases, which is consistent with the simulation detection result. When the node position of the target signal identification point is chosen to calculate does not just stay on the node communication circle, it introduces a certain localization distance difference, and the further the target signal identification point is from the position of the signal circle, the greater the error. Irregularity of RSS due to environmental changes analyzes the maximum error and provides the factors influencing the error and analyzing the maximum error and provide the factors that influence it.

Monsters

What does it mean to be a monster? How do you teach people to become accustomed to seeing things they don’t initially like or understand? Whose obligation is it to break through stereotypes and create a deeper understanding, the person who is afraid, or the object of their fear? In this work of philosophical short fiction, a mother takes her child out to the park even though there are ongoing rumors of “monsters” that roam their suburban neighborhood. Those fears seem to be true, and seem to imply this is a unique world, as the driver of an ice-cream truck suffers from severe, and grotesque, physical deformities. He says the reason he works a job and goes out in public is to help others get used to seeing “people” like him. On their walk home the narrator is continually concerned about the monsters that lurk in the neighborhood as she questions if leaving the house was a good idea. She feels she is being stalked by one of the monsters as they rush back to their home. They reach relative safety when her husband sees them and brings them onto their property. That’s when she sees, newly spray painted on the garage door of their suburban house “Whites only, negros get out.” Only then do we realize that the “monsters” are those that live all around them as they are the first to integrate their suburban neighborhood. They are the ones forcing others to get used to seeing “people” like them.

Exploring The Security Issues in Home-Based IoT Devices Through Denial of Service Attacks

Internet of Things (IoT) has infiltrated the digital realm, and critical efforts are being made to create robust security for these technologies. However, with increasingly sophisticated attacks, it is essential to understand IoT device security in depth. To understand the device vulnerabilities from the network level, we orchestrated Denial of Service (DoS) attacks for four different IoT devices through network flooding. We conducted our experiment in the lab environment using other IoT devices that include the Amazon Echo, a smart light-bulb, a smart camera, and a smart garage door opener. To conduct the DoS attack, we used Raspberry Pi as the main target to access other network devices with different protocols. We generated the DoS attack using Kali Linux installed in a virtual environment. This experiment demonstrated that hackers might exploit sensor vulnerabilities to gain unauthorized network access and use user data through various IoT devices. We proposed an effective Intrusion Detection technique using a combination of machine learning classifiers and deep learning. Some of the machine learning models include the logistic regression, decision tree, random forest and support vector machine to detect and mitigate the attack. The outcomes show the algorithm which presents the highest degree of attack detection accuracy. Our findings also show that DoS attacks continue to be a significant concern even with improved technologies and security protocols. Finally, we provide design implications to address such critical security flaws.