Parking slots have become a widespread problem in urban development. In this context, the growth of vehicles inside the university's campus is rapidly outpacing the available parking spots for students and staff as well. This issue can be mitigated by the introduction of parking management for the smart campus which targets to assist individually match drivers to vacant parking slots, saving time, enhance parking space utilization, decrease management costs, and alleviate traffic congestion. This paper develops an IoT Raspberry Pi-based parking management system (IoT-PiPMS) to help staff/students to easily find available parking spots with real-time vision and GPS coordinates, all by means of a smartphone application. Our system composes of Raspberry Pi 4 B+ (RPi) embedded computer, Pi camera module, GPS sensor, and ultrasonic sensors. In the IoT-PiPMS, RPi 4 B+ is used to gather and process data input from the sensors/camera, and the data is uploaded via Wi-Fi to the Blynk IoT server. Ultrasonic sensors and LEDs are exploited to detect the occupancy of the parking spots with the support of the Pi camera to ensure data accuracy. Besides, the GPS module is installed in the system to guide drivers to locate parking areas through the Blynk App. that discovers parking spaces availability over the Internet. The system prototype is fabricated and tested practically to prove its functionality and applicability. According to the results, the IoT-PiPMS can effectively monitor the occupancy of outdoor parking spaces in the smart campus environment, and its potency in terms of updating the data to the IoT server in real-time is also validated..

An IoT Raspberry Pi-based parking management system for smart campus

In present years, the Internet of Things (IoT) has been one of the most sought research areas and has been an integral part of everyday life. Every household or society is nowadays dependent on some kind of smart things like smart TV, smart refrigerators, smart lighting, smart security systems, etc. Smart parking is one of the important areas for research and development in this field. In this paper, we have tried to propose a method that will be simple and efficient for the user. The objective is to study the available smart parking system, propose a complete smart parking system, implement the propose system, and analyze and compare the results with another smart parking systems. Our proposed system helps to manage the indoor parking system automatically starting from detection of vehicle, vehicle license plate detection, and recognition using Convolutional Neural Network (CNN), and then, license plate is matched with the registered license plate which is saved in a parking database while doing registration to park the vehicle for allocating automatic parking slot. The slot allocation process is scheduled through the FCFS algorithm, slot verification of individual parking users is done by license plate matching while parking the vehicle at slot, multilevel parking is designed for parking any type of vehicle, and e-ticket email has been sent using SMTP protocol. All parking statuses are being monitored in real time at the cloud server using ThinkSpeak. Finally, elapsed time result and analysis for complete parking has been measured and compared with another similar parking system.

A Smart Indoor Parking System

This research work has developed an artificial intelligence (AI) enabled smart city IoT system using Edge Computing. To take the smart decision and data processing purpose, this research work has deployed AI algorithms. The novelty in this work is, incorporation of Edge computing in IoT system, which enhances the performance of the IoT system by reducing the load on the cloud. Wi-Fi protocol is used in the network level for data transmission. Raspberry–pi is used to design edge server. The main motivation of this project is to prevent from unexcepted pollution levels in air, water, etc., that causes harmful to the health and also to the nature. So, in this smart city application includes City air management, managing the traffic and transportation, utilization of power effectively, water pollution monitoring. And this monitoring is done by different sensors like camera, gas sensor, water quality sensors, other monitoring sensors. We gather physiological data from the sensors environment and stores it in the database and analyze it to take smart decision and indicates it on the webpage and intimate that information to the through e-mail via SMTP.

Artificial Intelligence Enabled Smart City IoT System using Edge Computing

Our proposed system has seven main contributions, i.e., Smart street lights, Smart home, Bio-metric door and home security system, Intelligent traffic lights management and road security system, Private and smart parking, Intelligent accident management system and Smart information display/ notice board system. Our prototypes / products employ Arduino UNO board, Node MCU, Ultrasonic sensor, Fingerprint module, Servo motors, GSM, GPS, LEDs, Flame Sensor, Bluetooth and Wi-Fi module etc. We are very confident that our proposed systems are efficient, reliable, and cost-effective and can be easily tested and implemented on a large scale under real conditions.

Design and Implementation of User-Friendly and Low-Cost Multiple-Application System for Smart City Using Microcontrollers.

Powering the Smart Parking System with Photovoltaic Solar Panels at Campus of “Džemal Bijedić” University of Mostar

The internet of things plays a vital role in interconnection and automation of various physical devices, vehicles, home appliances and other things. With the help of software, various sensors, actuators, these objects connect and exchange data. This automation of devices enhances a person’s standard of life and way of living, which is a need of future. A similar need is discussed in this paper. In this project a smart parking feature is discussed which enables a user of find a parking location and a free slot in that parking space inside a city. This project focuses on reducing time wasted on finding parking space nearby and on going through the filled parking slots. This in turn reduces the fuel consumption and standard of living.

Smart Parking System using Cloud based Computation and Raspberry Pi

Location Based Social Networks (LBSNs) like Twitter, Foursquare or Instagram are a very good source of extracting human generated data in the form of check-ins, location and social relationships among users. Location data bridges the gap between the physical and digital worlds and enables a deeper understanding of users preference and behaviour. There are many underlying patterns in this type of dataset of human mobility which are utilised for applications like recommender systems. The current approaches involve extracting data from user-item rating, GPS trajectories, or other forms of data, whereas we focus on an integrated model considering factors like user's interest, social influence, time and proximity. There is metadata associated with this dataset which tells us about the whereabouts of the user, with emphasis on types of places. This paper proposes an integrated location recommendation model that considers users' interests, their friends influences, time and seasonality factors, and users' willingness to visit distant locations. We integrate all these parameters to generate a ranked list of locations which will be recommended to the user. Experiments are performed on a real-world dataset which show that our proposed model is effective in the stated conditions.

Akash Gupta

Papers

Smart Parking System using Cloud based Computation and Raspberry Pi

Influence-Time-Proximity Driven Locations Recommendation Model: An Integrated Approach