scholarly journals IoT Based Intelligent Vehicle Parking Solution System

2018 ◽  
Vol 2 (4) ◽  
Author(s):  
Vedant Chauhan . ◽  
Shikhar Sharma . ◽  
Manju Khari .
Keyword(s):  
Real Time ◽  
Smart Cities ◽  
Cloud Layer ◽  
City Development ◽  
Time Data ◽  
Sensor Layer ◽  
Parking Spot ◽  
Parking System ◽  
On Chip ◽  
Layer Cloud

With rapid increase in population in urban cities, availability of parking space is real issue. This parking issue lead to traffic and encroachment of roads for parking. With implementation of smart cities is real time development, smart parking is integral part of this development. Intelligent parking system describe in this paper solve the parking issue and fits in the smart city development, this system is based on cloud-based parking system where user is able to get location of parking spot with helps sensors network and cloud computing. The user is updated with real time data of available parking spot near their destination, and they can choose the spot according to their convenience. The main components of the system are sensor layer, hardware layer, cloud layer and application layer. The sensor layer is controlled by Arduino board or other system on chip which manages the data collected by sensors, this data is sent to cloud through hardware layer cloud layer manages the data accordingly and data is sent to users’ application on the reception of request through application. This interconnection of all the layers is main aspect of IoT (Internet of Things). This system will help user to get the spot in hassle free and quick way.

Cloud-Based Big Data Analysis Tools and Techniques Towards Sustainable Smart City Services

2021 ◽  
pp. 63-90
Author(s):  
Suresh P. ◽  
Keerthika P. ◽  
Sathiyamoorthi V. ◽  
Logeswaran K. ◽  
Manjula Devi R. ◽  
...  
Keyword(s):  
Big Data ◽  
Real Time ◽  
Smart City ◽  
Traffic Management ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Streaming Data ◽  
City Development ◽  
Time Data ◽  
Public And Private

Cloud computing and big data analytics are the key parts of smart city development that can create reliable, secure, healthier, more informed communities while producing tremendous data to the public and private sectors. Since the various sectors of smart cities generate enormous amounts of streaming data from sensors and other devices, storing and analyzing this huge real-time data typically entail significant computing capacity. Most smart city solutions use a combination of core technologies such as computing, storage, databases, data warehouses, and advanced technologies such as analytics on big data, real-time streaming data, artificial intelligence, machine learning, and the internet of things (IoT). This chapter presents a theoretical and experimental perspective on the smart city services such as smart healthcare, water management, education, transportation and traffic management, and smart grid that are offered using big data management and cloud-based analytics services.

Microsense

2016 ◽  
Vol 7 (3) ◽  
pp. 38-55
Author(s):  
Srinivasa K.G. ◽  
Ganesh Hegde ◽  
Kushagra Mishra ◽  
Mohammad Nabeel Siddiqui ◽  
Abhishek Kumar ◽  
...  
Keyword(s):  
Real Time ◽  
Nodal Point ◽  
System On Chip ◽  
Streaming Data ◽  
Portable Devices ◽  
Time Data ◽  
Unified Framework ◽  
Real Time Data ◽  
Varied Degree ◽  
On Chip

With the advancement of portable devices and sensors, there has been a need to build a universal framework, which can serve as a nodal point to aggregate data from different kinds of devices and sensors. We propose a unified framework that will provide a robust set of guidelines for sensors with varied degree of complexities connected to common set of System-on-Chip (SoC). These will help to monitor, control and visualize real time data coming from different type of sensors connected to these SoCs. We have defined a set of APIs, which will help the sensors to register with the server. These APIs will be the standard to which the sensors will comply while streaming data when connected to the client platforms.

scholarly journals Semantic Traffic Sensor Data: The TRAFAIR Experience

2020 ◽  
Vol 10 (17) ◽  
pp. 5882
Author(s):  
Federico Desimoni ◽  
Sergio Ilarri ◽  
Laura Po ◽  
Federica Rollo ◽  
Raquel Trillo-Lado
Keyword(s):  
Air Quality ◽  
Real Time ◽  
Smart Cities ◽  
Low Cost ◽  
Semantic Annotation ◽  
Open Data ◽  
Transportation Systems ◽  
Sensor Data ◽  
Time Data ◽  
Future Evolution

Modern cities face pressing problems with transportation systems including, but not limited to, traffic congestion, safety, health, and pollution. To tackle them, public administrations have implemented roadside infrastructures such as cameras and sensors to collect data about environmental and traffic conditions. In the case of traffic sensor data not only the real-time data are essential, but also historical values need to be preserved and published. When real-time and historical data of smart cities become available, everyone can join an evidence-based debate on the city’s future evolution. The TRAFAIR (Understanding Traffic Flows to Improve Air Quality) project seeks to understand how traffic affects urban air quality. The project develops a platform to provide real-time and predicted values on air quality in several cities in Europe, encompassing tasks such as the deployment of low-cost air quality sensors, data collection and integration, modeling and prediction, the publication of open data, and the development of applications for end-users and public administrations. This paper explicitly focuses on the modeling and semantic annotation of traffic data. We present the tools and techniques used in the project and validate our strategies for data modeling and its semantic enrichment over two cities: Modena (Italy) and Zaragoza (Spain). An experimental evaluation shows that our approach to publish Linked Data is effective.

scholarly journals Android based real time advance security for parking system in IOT

2018 ◽  
Vol 7 (2.7) ◽  
pp. 197
Author(s):  
CH Venkata Sai Kasturi Babu ◽  
Manikanta Athuluri ◽  
N Venkatram
Keyword(s):  
Real Time ◽  
Road Safety ◽  
Smart Cities ◽  
Android Application ◽  
Network Connection ◽  
Parking System ◽  
Recent Trends ◽  
Parking Facilities ◽  
Made In

In recent trends traffic has become a major problem in most of the cities. There are limited car parking facilities and less road safety precautions observed. So, to address the traffic problems are being made in the field of IoT.    This paper will present the basic idea of cloud-based parking system. Cloud will store and process the data between device and mobile. In cloud, stored files will be obtained from anywhere through the network connection. By using the latest technology called IoT for smart cities, we can book the parking slot of a vehicle from anywhere. We need to take registration details for reserving the parking slot and for security purpose we are generating an OTP. By using the OTP, we can book our slot through an android application.

The Design of Real-Time and High-Speed Video Processing Platform for Aerial UAV Based on DM642

2014 ◽  
Vol 1061-1062 ◽  
pp. 1186-1189
Author(s):  
Ming Zhe Wei ◽  
Wan Wei Tang
Keyword(s):  
Real Time ◽  
Video Processing ◽  
High Speed ◽  
Rapid Development ◽  
Processing System ◽  
Video Signal ◽  
Good Effect ◽  
Time Data ◽  
Spatial Interference ◽  
On Chip

With the rapid development of aerial UAV (Unmanned Aerial Vehicle), the design of real-time data acquisition and transmission system for the video signal has a new applied field. It is different from traditional video acquisition and processing system, aerial video signal has the problems of screen jitter and spatial interference. The processing algorithm of aerial UAV airborne video signal is put forward in the paper, and the platform of high speed procession is constructed based on chip TMS320DM642, and get a good effect.

scholarly journals Smart Vehicular Parking System

2020 ◽  
Vol 8 (6) ◽  
pp. 4039-4043
Keyword(s):  
Image Processing ◽  
Internet Of Things ◽  
Real Time ◽  
Cloud Storage ◽  
Specific Aspect ◽  
Full Potential ◽  
Parking Space ◽  
Parking Spot ◽  
Parking System ◽  
Growing Population

In today’s world, ever growing population adds up increasing numbers of cars. Especially, in metro cities with limited spaces for parking, innovative measures are the need of the hour. Our paper focuses on this specific aspect of acute shortage of parking spaces. In this paper, we are proposing a smart vehicular parking model, that takes into account registered or non-registered users parking at the same time. Additionally, it also takes help of image processing to classify the available space based on the size of the vehicle, making it an optimized real-time parking prototype. Using RFID to authenticate each user, the model uses Internet of Things (IoT), cloud storage to makes it convenient for each and every individual to locate and remotely book a parking spot via a smartphone. It works to its full potential by securing cashless transaction via e-wallet. The efficiency of our prototype has been further established by improving parking area by 28 % under any given circumstance. Hence this novel idea excels in every aspect by providing an optimized parking space.

scholarly journals Virtual Channel aware Scheduling for Real Time Data-Flows on Network on-Chip

2019 ◽  
Vol 20 (3) ◽  
pp. 495-510
Author(s):  
Amine Meghabber ◽  
Lakhdar Loukil ◽  
Richard Olejnik ◽  
Abou El Hassan Benyamina ◽  
Abdelkader Aroui
Keyword(s):  
Real Time ◽  
Network On Chip ◽  
Time Constraints ◽  
Time Data ◽  
Virtual Channels ◽  
Software Designers ◽  
Data Flows ◽  
Real Time Data ◽  
Real Time Applications ◽  
On Chip

The increasing complexity of real-time applications presents a challenge to researchers and software designers. The tasks of these applications usually exchange large volume of data-flows and often need to satisfy real-time constraints. Although the Network on-Chip (NoC) paradigm offers an underlying communication infrastructure that gives more hardware resources, it is unable to safe tasks and data-flows deadlines. In recent works, preemptive wormhole switching with fixed priority has been introduced to meet real-time constraints of real-time applications. However, it suffers some bottleneck such as hardware requirement where none of these works takes account of the number of implemented virtual channels on the router. To alleviate this problem, we propose a novel scheduler for soft real-time data-flows application that takes into account the lack on resource in routers in term of Virtual channels. Experimental results obtained on a benchmark of synthetic and soft real applications have shown the efficiency of our approach in term of real-time constraints satisfaction for data-flow traffics and hardware requirements.

scholarly journals Mining Iot Data for Next Generation Smart Cities

2019 ◽  
Vol 8 (6) ◽  
pp. 259-262
Keyword(s):  
Real Time ◽  
Crucial Role ◽  
Data Analytics ◽  
Data Science ◽  
Smart Cities ◽  
Classification Algorithms ◽  
Time Data ◽  
New Era ◽  
Real Time Data ◽  
Iot Devices

Convergence of Cloud, IoT, Networking devices and Data science has ignited a new era of smart cities concept all around us. The backbone of any smart city is the underlying infrastructure involving thousands of IoT devices connected together to work in real time. Data Analytics can play a crucial role in gaining valuable insights into the volumes of data generated by these devices. The objective of this paper is to apply some most commonly used classification algorithms to a real time dataset and compare their performance on IoT data. The performance summary of the algorithms under test is also tabulated

scholarly journals Urban Planning and Smart City Decision Management Empowered by Real-Time Data Processing Using Big Data Analytics

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2994 ◽  
Author(s):  
Bhagya Silva ◽  
Murad Khan ◽  
Changsu Jung ◽  
Jihun Seo ◽  
Diyan Muhammad ◽  
...  
Keyword(s):  
Big Data ◽  
Data Processing ◽  
Real Time ◽  
Smart City ◽  
Data Analytics ◽  
Smart Cities ◽  
Big Data Analytics ◽  
Time Data ◽  
Real Time Data

The Internet of Things (IoT), inspired by the tremendous growth of connected heterogeneous devices, has pioneered the notion of smart city. Various components, i.e., smart transportation, smart community, smart healthcare, smart grid, etc. which are integrated within smart city architecture aims to enrich the quality of life (QoL) of urban citizens. However, real-time processing requirements and exponential data growth withhold smart city realization. Therefore, herein we propose a Big Data analytics (BDA)-embedded experimental architecture for smart cities. Two major aspects are served by the BDA-embedded smart city. Firstly, it facilitates exploitation of urban Big Data (UBD) in planning, designing, and maintaining smart cities. Secondly, it occupies BDA to manage and process voluminous UBD to enhance the quality of urban services. Three tiers of the proposed architecture are liable for data aggregation, real-time data management, and service provisioning. Moreover, offline and online data processing tasks are further expedited by integrating data normalizing and data filtering techniques to the proposed work. By analyzing authenticated datasets, we obtained the threshold values required for urban planning and city operation management. Performance metrics in terms of online and offline data processing for the proposed dual-node Hadoop cluster is obtained using aforementioned authentic datasets. Throughput and processing time analysis performed with regard to existing works guarantee the performance superiority of the proposed work. Hence, we can claim the applicability and reliability of implementing proposed BDA-embedded smart city architecture in the real world.

Microsense

2020 ◽  
pp. 170-189
Author(s):  
Srinivasa K.G. ◽  
Ganesh Hegde ◽  
Kushagra Mishra ◽  
Mohammad Nabeel Siddiqui ◽  
Abhishek Kumar ◽  
...  
Keyword(s):  
Real Time ◽  
Nodal Point ◽  
Aggregate Data ◽  
System On Chip ◽  
Streaming Data ◽  
Portable Devices ◽  
Time Data ◽  
Unified Framework ◽  
Real Time Data ◽  
On Chip

With the advancement of portable devices and sensors, there has been a need to build a universal framework, which can serve as a nodal point to aggregate data from different kinds of devices and sensors. We propose a unified framework that will provide a robust set of guidelines for sensors with varied degree of complexities connected to common set of System-on-Chip (SoC). These will help to monitor, control and visualize real time data coming from different type of sensors connected to these SoCs. We have defined a set of APIs, which will help the sensors to register with the server. These APIs will be the standard to which the sensors will comply while streaming data when connected to the client platforms.

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