Navigation system using ZigBee wireless sensor network for parking

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.

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.

In China, finding a vacant parking space in a congested area or a large parking lot is always time consuming and frustrating. To minimize hassle and inconvenience to the automatic drivers, this paper proposed a novel smart parking system which consists of many parking sensor nodes and one parking gateway in one large parking lot. Parking sensor node is used as vehicle occupancy detector and parking gateway is used as parking lot manager. All parking sensor nodes compose wireless sensor networks with Zigbee transmission protocol. With the GPRS/3G transmission function of parking gateway, large parking lot can provide friendly parking information to the running vehicles and subscribed users. In a word, the proposed novel smart parking system is provided with innovative and practicability.

Due to the large volumes of modern vehicles in metropolises, finding a vacant parking space has become an irritating and frustrating problem for drivers, particularly in a congested area, such as sport centers, shopping malls, and downtown [1]. The extra traffic cruising for parking spaces brings serious social problems, including traffic congestion, vehicle accident, fuel waste, and air pollution [2]. Although Google Maps and portable navigators assist drivers to discover parking garages in destinations, drivers are then faced with a problem that no vacant parking space are available [3].

With the continuous acceleration of China’s modernization process, the number of private cars has increased sharply, and the traffic flow of various parking lots is increasing. The parking management of motor vehicles has become a problem of great concern to the society. In view of the shortage of parking spaces, the long time of manual fee collection, low efficiency and the inability to quickly find the reserved parking spaces in urban parking lots in China. At the same time, in large shopping malls, there is a common phenomenon that the parking range is single or the parking space is difficult to find, which makes the vehicles stay in the parking lot in a short time and cause congestion. For parking lot managers, it is difficult to timely regulate the parking lot through the existing traffic flow information of the parking lot, and it is impossible to determine the number of vacant parking spaces in the parking lot in real time. At the same time, The existing management system has problems such as low efficiency, poor scalability and less service content, which is difficult to solve in the face of emerging problems. Therefore, based on the above problems, this project designs an urban intelligent parking cloud platform based on wechat applet, which can distinguish users and administrators, select spare parking spaces, route navigation and time payment of parking fees for parking users, and the parking lot managers can understand the spare parking spaces and vehicle payment of the parking lot they manage, At the same time, it can detect the traffic flow through the traffic flow in a period of time and give early warning to prevent the accumulation of vehicles in the parking lot. Users and administrators can obtain more convenient and real-time information only through mobile phones, which solves the difficult problem of parking management in intelligent parking lot.

Smart parking management aims to leverage advanced sensing and monitoring technologies to optimize the usage of parking spots within large car parks and ease parking for drivers by directing them to available parking spots, thereby reducing unnecessary stress and delay that further worsen the traffic conditions. The idea of smart parking development began to make parking easier, faster, with less of a drain on resources which are slowly disappearing. The aim of the Smart Parking Management System (SPMS) proposed in this paper is to assist drivers in alleviating several issues linked to parking. Our SPMS provides a sensing platform supported by a mobile app to enable real-time interaction of drivers with the car park infrastructure in a way that minimizes drivers' search time for an empty parking spot within large multi-storey car parks and facilitates the localization of their parked cars. To validate the efficiency of our SPMS, a small scale test-bed was developed as a proof of concept and tested under five different scenarios. The obtained results highlight its effectiveness and call for its potential integration in real world car parks.

We discuss the problem of predicting the number of available parking spaces in a parking lot. The parking lot is modeled by a continuous-time Markov chain, following Caliskan, Barthels, Scheuermann, and Mauve. The parking lot regularly communicates the number of occupied spaces, capacity, arrival and parking rate through a vehicular ad-hoc network. The navigation system in the vehicle has to compute from this data the probability of an available parking space upon arrival. We derive a structural result that considerably simplifies the computation of the transition probabilities in the navigation system of the vehicle.

Finding available parking spaces made easy

One of the major urban concerns is the lack of parking places. The most appropriate indicator for assessing the spatial efficiency of the car parks is the unit surface. The largest space-saving resources to improve spatial efficiency belong to the large car parks in the distinct situations: for employees of a large company or for hypermarket customers. As a result, a mathematical model for access to the parking place by means of a single steering manoeuvre has been constructed, having the angle of inclination of the parking spaces as variable and the 95% percentiles corresponding to the overall current dimensions of the cars as parameters. In the case of customer parking, it was considered that it is necessary for the customers to have access to the trunk to pick up the purchased items from the shopping cart, so a specific arrangement of the vehicles on the parking places is required. The results confirmed the superiority, from the point of view of spatial efficiency, of the angled car parks as compared to the rectangular car parks. For the car parks angled at 45° - 60° it has been demonstrated that increases of more than 10% over rectangular parking spaces are achieved.

The parking space is one of the crucial problems in the urban city. Most buildings have parking facilities for residents and visitors. The common parking patterns are parallel parking, perpendicular parking and angular parking. Parallel parking is a common design for street side parking whereas perpendicular parking and angular parking are a common design for garages. Angular parking is easy to park than other types. A typical parking space for a personal car is 3 meters in width and 6 meters in length. The parking space have limitation but the number of car increasing along with the economic growth [7]. So the parking space should accommodate the maximum number of car. Normally, the parking space configuration is designed by architects or construction engineers because it's required technical knowledge to calculate a number of car and parking pattern. In parking area, there must be an alleyway for cars to enter and exits. This study is to design and implement a calculation model for number of car and parking pattern in the specified parking area. In the calculation, the dimension and the specific condition of parking area is require. The model calculate the maximum number of car and also calculate the parking pattern for the parking area. This model is use to support non-technical user during the parking area design and can be as engine to develop mobile application for general user in the future.

In today's world, time is the most valuable asset and this paper focuses on developing a smart parking system for smart cities. Its main objective is to assist users to find a vacant location of car parking in a shorter time. The system displays occupied and vacant parking spots on LCD, making it convenient for the users to park. This parking system is based on sensors and cloud server integration. The working principle is divided into two phases. In the first phase, the ultrasonic sensor is used to detect vacant or occupied car parking space and display it on the screen using cloud technology. In the second phase, the improper parking of the car is detected by IR sensors. It alerts the driver with a buzzer if the car is not parked in the parking boundary. Moving further, some important features of this parking system are also discussed followed by, components required and their description. Towards the end of the paper, this parking system is compared with other alternative smart parking systems, the comparisons prove the correctness of our system over others.

A quest of vacant parking space in the public area can indirectly lead to traffic congestion which can be troublesome for drivers in terms of time efficiency. This study is expected to assist drivers to get the available parking slots information in a real-time manner and support the parking control systems by constantly updating the information of vacant parking slots positions in public areas. A vision-based parking slots recognition method is proposed to identify occupied areas by vehicle which is divided into two main parts: setup configuration and object detection. Canny Edge and Hough Line Transform are used to achieve line detection for parallel parking slot marking; contour extraction and bounding rectangular are then applied for an initial parameter to form a reference area as a region of interest (ROI). Moreover, Structural Similarity Index Measurement (SSIM) exploits the reference image and target image to identify whether the area is empty or occupied by vehicle depending on structure comparison. Experimental result shows, from 50 sample images of parking slots attained by surveillance camera, the detection accuracy of 92% and precision of 89% are obtained using selected features with tuning SSIM threshold level of 0.4.

SummaryIn big cities, the drivers face a major problem in identifying an optimal vacant parking space for their vehicles as the number of vehicles has rapidly grown over the recent years. This objective of determining the available parking space results in wastage of time, air pollution, and traffic congestion. Smart parking systems facilitate the driver to book parking slots and attain real‐time parking information. However, the existing smart parking solutions necessitate the disclosure of sensitive information like desired destination from the vehicle drivers. Moreover, the current smart parking solutions are highly centralized, making them vulnerable for launching privacy breaches by service providers and resulting in the issues of single‐point failure. In this paper, ring signature and improved multi‐transaction mode consortium blockchain‐based private information retrieval (RS‐IMTMCB‐PIR) scheme is proposed for privacy preserving in smart parking system. This RS‐IMTMCB‐PIR scheme initially utilizes an improved multi‐transaction mode consortium blockchain constructed by different number of parking lot owners in order to achieve maximized parking offers based on the factor of availability, transparency, and security. It incorporates an improved private information retrieval approach for protecting the location privacy of drivers and secretly retrieving the offers of parking from the improved multi‐transaction mode consortium blockchain. It further utilizes the benefits of ring signature for permitting the drivers in a potential anonymous authentication process that aids in available parking slot reservation. The simulation results of this proposed RS‐IMTMCB‐PIR scheme portrayed a predominant performance in ensuring maximized degree of drivers' privacy with reduced computation and communication overheads.

This paper aims to present an intelligent system for parking space detection based on image processing technique. The proposed system captures and processes the rounded image drawn at parking lot and produces the information of the empty car parking spaces. In this work, a camera is used as a sensor to take photos to show the occupancy of car parks. The reason why a camera is used is because with an image it can detect the presence of many cars at once. Also, the camera can be easily moved to detect different car parking lots. By having this image, the particular car parks vacant can be known and then the processed information was used to guide a driver to an available car park rather than wasting time to find one. The proposed system has been developed in both software and hardware platform. An automatic parking system is used to make the whole process of parking cars more efficient and less complex for both drivers and administrators.

Finding a vacant place to park cars in the rush hour is time-consuming and even may be frustrating for the drivers. In some studies, vehicles are equipped with communicative tools called On Board Units (OBUs), which come along with roadside devices known as Roadside Units (RSUs), which allow the drivers to communicate each other and trace a vacant parking place easily. Previous systems work with connection of sensors all over the road and parking space and may result in spending much time to find a parking place, occupancy of the empty parking place until the car gets to the desired location, requirement for an additional hardware, wired network communication, and security issues. In this paper, we propose an exponential smoothing and multi-objective decision-making by using cloud-based methods to find the best parking place, taking into consideration of the park-cost for the driver. The proposed system uses the cellular base stations to eliminate the cost of the RSUs and the sensors. The results of our simulations via NS-2 network simulator confirm the efficiency of the proposed model.