AI-Enabled Smart Parking Space Detection and Allocation System using IoT and Computer Vision

In this digital era, integrating Internet of Things (IoT) technology with mobile applications has revolutionised various industries, including weather monitoring. This project presents the development of a weather monitoring app that harnesses the power of IoT and Kotlin programming language. The app utilizes a weather API to obtain real-time weather data and seamlessly communicates with an IoT device, enabling users to access and display weather information conveniently. project begins with a detailed analysis of requirements, outlining the desired features and functionalities of the app. The weather API selection process is discussed, and the chosen API is integrated into the Kotlin app to fetch weather data. Additionally, location services are implemented to offer location-based weather updates. An IoT device, such as a Raspberry Pi or Arduino, is selected for displaying the weather data. The IoT device is connected to the internet and programmed to fetch weather data from the app. Communication protocols between the Android app and the IoT device are established, ensuring smooth data exchange. The app's user interface is designed to provide an intuitive experience for users, with weather data and location information prominently displayed. Error handling mechanisms are implemented to deal with potential API downtimes or communication failures between the app and the IoT device. Through comprehensive testing, the app's functionality, performance, and reliability are assessed. Security measures are implemented to safeguard user data and privacy during data transmission. The result is a robust and user-friendly weather-monitoring app that seamlessly integrates IoT technology with Kotlin's efficiency and flexibility. The app empowers users to access real-time weather updates, enhancing their preparedness for weather changes and enabling informed decision-making for various outdoor activities. With the proliferation of IoT and its impact on various sectors, this project serves as a valuable example of leveraging IoT and Kotlin to develop innovative mobile applications for everyday use. Keywords – Weather Monitoring App, Kotlin, IoT, Real- time Weather Data, Weather API

Dynamic parking space allocation at urban scale: Problem formulation and resolution

Urban parks and green spaces are managed by local authorities, and 8 local authorities are utilizing the park and green space management system in Korea. However, in each local government, park and green space data, system components, main function, and tasks were established in different forms and their utilization was also very low. In this context, this study suggests a general-purpose model of the park and green space management system that can be used commonly by local governments for park and green space management. Based on the requirements that the GIS (geographic information system) should be used for continuous management in the “park and green space master plan guideline”, and the creation of an urban park inventory in the “act on urban parks, green areas, etc.”, we proposed a general-purpose model that builds GIS—based spatial information. We expect that local governments can reduce time and cost of introducing the park and green space management system, and the central government will be able to collect data and statistics on national park and green space, and establish data-based policies.

Parking space detection is a key technology in the parking lot management system. When performing parking space detection using magnetic sensors, there may be interference from the adjacent parking space, which will result in false detection. However there still will be false detection when only cameras are used to detect the parking spaces because the performance of the cameras is inclined to be affected by the environment. Considering that the magnetic sensors are only sensitive to ferromagnetic objects and insensitive to the environment effect and images often contain a wealth of information, we propose a parking space detection algorithm combining the information from magnetic sensors and images. The algorithm performs primitive detection using magnetic sensors. If the sensor reports that there is a vehicle nearby, the algorithm analyzes the image information of the parking space gathered by the camera, extracts the image features of the parking space and classifies the features to confirm whether the parking space is occupied. The experimental results show that our algorithm can make more precise decisions for parking space detection with much lower complexity and less computational efforts. Furthermore our algorithm can effectively reduce the burden of image or video processing of the system.

Finding a vacant parking space in outdoor parking lots is a daily concern of most vehicle drivers during rush hours, especially in the urban context. In this paper, an outdoor parking space vacancy detection system is proposed, using mobile devices to improve parking space searching experience for vehicle drivers by providing them with the location and occupancy information of parking spaces. The system uses state-of-the-art image recognition algorithm, namely Convolutional Neural Network with a Raspberry Pi to identify vacant parking spaces from a parking lot image retrieved in real time via an IP camera. A university parking lot has been chosen as the test bed to deploy the proposed system for real time parking space vacancy detection. An Android smartphone application called Driver App is developed to enable ubiquitous visualization of real time outdoor parking spaces occupancy information for vehicle drivers. Evaluation outcomes based on the responses to System Usability Scale (SUS) questionnaire revealed high usability of the Driver App as a tool that provides smart parking service to assist vehicle drivers in searching for a vacant parking space.

In many domains, organizations must model personnel and corresponding data access privileges as fine-grained hierarchical access control models. One class of such models, Role-based Access Control (RBAC) models, has been widely accepted and deployed. However, RBAC models are often used without involving cryptographic keys nor considering confidentiality/privacy at the data level. How to design, implement and dynamically modify such a hierarchy, ensure user and data privacy and distribute and manage necessary cryptographic keys are issues of the utmost importance. One elegant solution for cryptography-based hierarchical access control combines the collusion-resistant and privacy-preserving Access Control Polynomial (ACP) and Atallah's Dynamic and Efficient Extended Key Management scheme. Such a model involves cryptographic keys used to encrypt data, can address confidentiality/privacy at the data level and can efficiently support dynamic changes to the RBAC access hierarchy. In this paper, we discuss several implementation challenges and propose solutions when deploying such a system including: data encryption and decryption, key storage and key distribution. Furthermore, we provide analysis of the efficiency and scalability of the resulting system.

In today's urban landscapes, parking congestion has become a pervasive challenge, leading to wasted time, increased emissions, and driver frustration. The ever-growing urban population, coupled with the increasing number of vehicles, has led to a severe shortage of parking spaces in metropolitan areas. This scarcity has not only made finding a parking spot a time-consuming and frustrating experience but has also contributed to traffic congestion and environmental pollution. Traditional parking management systems, which often rely on manual ticketing or physical barriers, are becoming inadequate in addressing these challenges. To address this issue, innovative solutions that combine technology and automation are on the rise. Traditional parking management systems often rely on physical infrastructure and human intervention, resulting in inefficiencies and limited scalability. In contrast, the integration of deep learning techniques and advanced computer vision technology into parking management opens up new possibilities for a smarter, more efficient, and user-friendly experience. This system leverages two key components: facial recognition technology to identify vehicle occupants and automatic recognition of license plate numbers for vehicle identification. By seamlessly integrating these technologies, the system not only facilitates effortless parking but also enhances security and optimizes parking space utilization.we will explore the fundamental components, benefits, and potential impact of the Face and Number Plate-Based Smart Parking System. Experimental results shows that improved efficiency in smart parking system using face and number plate verification system.

A blockchain-enabled private parking space allocation with improved parking space utilization

Urban complex has become an essential part of our daily life. However, the management efficiency and parking experience of its parking lot have problems such as uneven utilization rate, excessive parking time and difficult parking. In order to solve these problems, we propose a novel parking management system, which improves the accuracy of short-term prediction of free parking spaces through our proposed method. And then it combines the probability distribution model to recommend the optimal parking area for drivers. In particular, based on the collected data of the parking lot in Suzhou Center and the open data of the social platform, we have established this parking management system in urban complex. The experimental results show that our parking management system not only solves the problem of drivers to blindly search for parking spaces, but also improves the management efficiency of the entire parking lot and facilitate the overall coordination of the managers.

This paper discusses a smartphone-based parking reservation system that manages a finite number of curbside parking spaces located at various places in a downtown area. Parking reservation schemes are designed to minimize the total social cost of parking, which is assumed to be a weighted sum of the cruising times for drivers to travel from their current locations to allocated parking spaces and the walking times from parking places to final destinations. With the assumption of perfect information on cruising and walking times, a simple reservation scheme to achieve an optimum allocation of parking spaces is presented. However, although the locations of drivers can be retrieved from their smart-phones, it is shown that drivers have an incentive to misreport their final destinations for their own benefit, which compromises the system benefit. Thus, the Vickrey–Clark–Groves mechanism is applied to determine the allocation of parking spaces and parking fees to minimize the total social cost while ensuring that all drivers report their final destinations truthfully. Last, a revenue redistribution scheme to reduce drivers' financial burden further and increase public acceptance of the reservation system is discussed.

In the modern world with about a billion vehicles present around the globe, and being in a densely populated country like India creates the need for an efficient parking system and with world advancing towards automation, there is also a need for an automated parking system. Mainly the problem of parking arises in big cities and this causes problems like traffic congestion, driver frustration & air pollution, etc. The issue mainly arises from not knowing whether the parking spaces is available at any given point of time. Even if known, many vehicles may be competing for limited parking spaces causing severe traffic congestions. Even there are problems that can be caused with regards to the parking management. It may be difficult for the parking management to organise the parking of the vehicles in an orderly manner. Thus we are trying to resolve the problems stated by developing an RFID based smart parking system that monitors the availability of idle parking slots and guides the vehicle to the nearest such slot by sending a message to the mobile number registered in the vehicle’s RFID. The proposed system uses infrared transmitter-receiver pairs that remotely communicate the status of parking occupancy to the Arduino UNO microcontroller and displays the vacant slots on the display at the entrance of the parking so that the user gets to know the availability /unavailability of parking space prior to his/her entry into the parking place. Implementation involves minimal human interaction and provides a seamless parking experience thereby reducing a lot of time wasted by the user in parking his/her vehicle. Key Words: Parking System, RFID, Arduino UNO

Pothole detection and mitigation systems are integral to enhancing road safety and optimizing vehicle performance. By leveraging Convolutional Neural Networks (CNN) and Internet of Things (IoT) technologies, these systems enable real-time hazard identification, such as potholes, and facilitate dynamic vehicle adjustments to mitigate potential damage. This study analyzed over 1,000 pothole images, with the CNN model achieving an accuracy rate of 96% to 99%. Edge detection techniques, including Sobel filters, were utilized to assess key pothole attributes such as diameter, depth, and edge sharpness. For potholes with diameters exceeding 50 cm and edge sharpness above 85%, the vehicle's suspension damping was automatically increased by 40%, minimizing the impact on the vehicle's chassis. Additionally, the system dynamically reduced vehicle speed by 10–20 km/h for severe potholes, based on real-time analysis by the Electronic Control Unit (ECU). The ECU also communicated with the Anti-lock Braking System (ABS) to apply braking force when sharp-edged potholes were detected. In scenarios where rear vehicles maintained a safe distance of 50 meters, the braking system was activated, reducing the risk of tire damage and collisions. Through IoT integration, real-time data was stored in the cloud, enabling predictive maintenance and improving repair planning efficiency by 30%. This approach not only enhances passenger safety but also reduces vehicle wear and tear, while improving road infrastructure management efficiency. The combination of CNN and IoT-based solutions marks a significant advancement in automotive safety systems.

Managing parking space in city areas is a big issue that impacts traffic, air cleanliness, and how easy it is for people to find a place to park. In this research, the authors are proposing a new approach to overcome this problem using quantum computing techniques, specifically quantum image processing and quantum machine learning, to detect vacant parking spaces from live video feeds. This new method uses the strong points of quantum algorithms for spotting patterns and objects. The methodology involves pre-processing parking lot video data and employing quantum image processing techniques like quantum wavelet transforms and quantum pattern recognition algorithms to identify vacant spaces. Moreover, the authors look into quantum machine learning models, such as quantum neural networks and quantum support vector machines, to make the detection of open spots more accurate. They test how well these quantum methods work by using a big mix of parking lot cases and comparing them with traditional machine learning methods like convolutional neural networks. The findings show that bringing quantum computing into parking space management systems can work well and has possible upsides, shining a light on where quantum algorithms do better and pointing out what needs more work in future studies. In this project, the authors dived into how quantum computing can be used in seeing things through computers and managing city infrastructure.

– With the rapid increase in urban populations and the corresponding rise in the number of vehicles, efficient parking management has become a critical challenge. Traditional parking systems are not equipped to handle the demands of modern cities, leading to traffic congestion, inefficient space utilization, and a lack of infrastructure for electric vehicle (EV) charging stations. The Smart Parking Management System using IoT for both EVs and Non-EVs aims to address these challenges by integrating Internet of Things (IoT) technology, data analytics, and real-time monitoring. The proposed system utilizes ultrasonic sensors, ESP8266 microcontrollers to detect and manage parking space occupancy while providing real-time updates to users. Additionally, the system includes provisions for managing EV charging stations efficiently. By optimizing space utilization and minimizing the time spent searching for parking spots, this system enhances user convenience, reduces congestion, and contributes to a more sustainable urban environment. . Keywords: Smart Parking, Internet of Things (IoT), Electric Vehicles (EV), Non-Electric Vehicles (Non-EV), Ultrasonic Sensors, ESP32, Urban Mobility, Real-time Monitoring

With the increasing population in urban areas, finding a vacant parking space has always been the biggest problem for drivers, which causes a series of issues such as energy waste and environmental pollution. Most outdoor parking lots cannot provide details of vacant parking spaces due to limited sensor technology in outdoor environments (i.e., poor resistance to external interference due to heat, light or signal noise). Although some studies are based on images captured by cameras, the existing vacancy detection methods using fixed-position surveillance cameras are not flexible and accurate enough for wide use. We propose an Inclined Bounding Box (IBB) method for detecting vacant parking spaces using aerial images. The IBB method takes advantage of the feature-description ability of deep Convolutional Neural Network (CNN) to identify the status of parking spaces and develops an inclined bounding boxes calibration algorithm on the top layer of CNN architecture. The proposed IBB method can automatically identify parking spaces, locate vacant parking spaces with any direction and detect multiple parking spaces simultaneously. The experimental results show that the proposed IBB method is more accurate than the three counterpart methods.