The development and high growth of the Internet of Things (IoT) have improved quality of life and strengthened different areas in society. Many cities worldwide are looking forward to becoming smart. One of the most popular use cases in smart cities is the implementation of smart parking solutions, as they allow people to optimize time, reduce fuel consumption, and carbon dioxide emissions. Smart parking solutions have a defined architecture with particular components (sensors, communication protocols, and software solutions). Although there are only three components that compose a smart parking solution, it is important to mention that each component has many types that can be used in the deployment of these solutions. This paper identifies the most used types of every component and highlights usage trends in the established analysis period. It provides a complementary perspective and represents a very useful source of information. The scientific community could use this information to decide regarding the selection of types of components to implement a smart parking solution. For this purpose, herein we review several works related to smart parking solutions deployment. To achieve this goal, a semi-cyclic adaptation of the action research methodology combined with a systematic review is used to select papers related to the subject of study. The most relevant papers were reviewed to identify subcategories for each component; these classifications are presented in tables to mark the relevance of each paper accordingly. Trends of usage in terms of sensors, protocols and software solutions are analyzed and discussed in every section. In addition to the trends of usage, this paper determines a guide of complementary features from the type of components that should be considered when implementing a smart parking solution.

Smart Parking: A Literature Review from the Technological Perspective

With the increasing availability of affordable open-source embedded hardware platforms, the development of low-cost programmable devices for uncountable tasks has accelerated in recent years. In this sense, the large development community that is being created around popular platforms is also contributing to the construction of Internet of Things applications, which can ultimately support the maturation of the smart-cities era. Popular platforms such as Raspberry Pi, BeagleBoard and Arduino come as single-board open-source platforms that have enough computational power for different types of smart-city applications, while keeping affordable prices and encompassing many programming libraries and useful hardware extensions. As a result, smart-city solutions based on such platforms are becoming common and the surveying of recent research in this area can support a better understanding of this scenario, as presented in this article. Moreover, discussions about the continuous developments in these platforms can also indicate promising perspectives when using these boards as key elements to build smart cities.

Open-Source Electronics Platforms as Enabling Technologies for Smart Cities: Recent Developments and Perspectives

Smart parking systems: comprehensive review based on various aspects.

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

This study develops an ultrasonic sensor system that can be used to determine the number, type, and speed of car vehicles passing a point over some specified time period. The proposed system consists of two ultrasonic sensors and a microcontroller equipped with data logging shield. The data were recorded and then analyzed using a computer program written in Java. Actual experiments that have been conducted indicates that this system can give a correct number of vehicles in a given interval of time.

https://iopscience.iop.org/article/10.1088/1742-6596/1204/1/012017/pdf

An Ultrasonic Sensor System for Vehicle Detection Application

Nowadays in an urban area, looking for available parking spots, is not only a waste of time and energy but also it causes more traffic and air pollution. Therefore finding available parking spots is becoming more challenging on daily life. This paper proposes a novel solution by providing motorists with real-time information about the availability of spots in parking spaces through a smartphone application. Ultrasonic sensors and raspberry pi were used at the gates of parking zones to detect cars and calculate the number of available parking spots. Client web server was used to accommodate multiple users in an android platform. Drivers can see available parking spots in real time on a map, they also can get navigate to nearest available parking spots using the mobile application. The parking system is designed for different types of parking zone (such as open space car parking and multilevel car parking); the cost of deploying the system is inexpensive due to the low demand for Raspberry pi single board computer as a local server and ultrasonic sensors for each entrance and exit gates compared to those that require a sensor for each parking spot.

Smart urban parking detection system

Farming1 is an excellent niche for automation and robotic innovations. The manual farming process deals with many repetitive and tedious tasks. It makes it perfect for applying automation through agricultural robot concepts, AGRIBOTS, which can lead to more effective and efficient farming. The AGRIBOTS can cope with various agrarian tasks such as seeding, watering, harvesting, and monitoring. One of the challenges in autonomous farming is identifying the accurate harvest time. This study addressed the challenge for carrot farms by developing an optimized Fast Library for Approximate Nearest Neighbors (FLANN) feature matching algorithm with a randomized KD-tree index and a Scale Invariant Feature Transform (SIFT) algorithm to classify carrots and weeds images. This paper proposes an autonomous, innovative farming approach for small to medium-sized enterprises (SME) with a robotic arm and a claw that can move in the x-axis and y-axis on a gantry crane to precisely automating the seeding, weeding, and harvesting processes. In addition, a web application was developed for farmers to monitor crops conditions such as humidity, temperature, and soil moisture in real-time. They can also observe the whole automated process of planting with the growing period of the corps on the calendar. Finally, a prototype was built and tested successfully to evaluate the overall performance of the proposed AGRIBOT. Over one hundred samples were used to train the classification model that reached the Percentage of Correct Classification (PCC) of 77% and the precision of 88%. 

Autonomous smart farming system using FLANN-based feature matcher with robotic arm

Agriculture plays a critical role in the economy of many countries. Pesticide and herbicide solutions make the farm field disinfect the pest, rats, and other living organisms that destroy the goods. Doing all these processes manually takes lots of time and requires several workers to work in the fields. Moreover, close contact with pesticide and herbicide solutions can disturb many organs' functionality in the human body and cause nerve damage and chronic diseases for farmers. This paper proposes an Unmanned Aerial Vehicle (UAV) with an adjustable angle of the sprayer to lift the heavy load (up to five kilograms) for pesticides/herbicides. The UAV decreases the farmers' contact with toxic substances and increases the farms' productivity. It also has a built-in GPS and Surveillance camera for the farmers to monitor the farm precisely. With a unique foldable design, farmers can carry it around easily. A UAV with four propellers (Quadcopter) has been designed and successfully tested during this project. Based on the testing results, the spraying time with the UAV has improved by about 83.4% compared with the manual process. The farmers can safely and quickly use the UAV to disinfect the farm field with the proposed solution. 

Nastaran Reza Nazar Zadeh

Papers

Smart urban parking detection system

Autonomous smart farming system using FLANN-based feature matcher with robotic arm

Pesticides/herbicides unmanned aerial vehicle with adjustable sprayer and surveillance camera