Research on the Application of Key Technologies in the Construction of Smart Cities Based on Smart Transportation

2020 ◽  
Author(s):  
Lei Tong
Keyword(s):  
Smart Cities ◽  
Smart Transportation ◽  
Key Technologies

Energy-Saving Methods for Urban Travel and Public Transportation in Smart Cities

2022 ◽  
Author(s):  
Shan Li ◽  
Ying Gao ◽  
Tao Ba ◽  
Wei Zhao
Keyword(s):  
Energy Saving ◽  
Public Transportation ◽  
Traffic Control ◽  
Smart Cities ◽  
Learning Technologies ◽  
General Strategy ◽  
Smart Transportation ◽  
Urban Travel ◽  
Emissions Mitigation ◽  
The Impact

In many countries, energy-saving and emissions mitigation for urban travel and public transportation are important for smart city developments. It is essential to understand the impact of smart transportation (ST) in public transportation in the context of energy savings in smart cities. The general strategy and significant ideas in developing ST for smart cities, focusing on deep learning technologies, simulation experiments, and simultaneous formulation, are in progress. This study hence presents simultaneous transportation monitoring and management frameworks (STMF ). STMF has the potential to be extended to the next generation of smart transportation infrastructure. The proposed framework consists of community signal and community traffic, ST platforms and applications, agent-based traffic control, and transportation expertise augmentation. Experimental outcomes exhibit better quality metrics of the proposed STMF technique in energy saving and emissions mitigation for urban travel and public transportation than other conventional approaches. The deployed system improves the accuracy, consistency, and F-1 measure by 27.50%, 28.81%, and 31.12%. It minimizes the error rate by 75.35%.

Efficient Cyber Security Framework for Smart Cities

2019 ◽  
pp. 130-157 ◽  
Author(s):  
Amtul Waheed ◽  
Jana Shafi
Keyword(s):  
Cyber Security ◽  
Smart City ◽  
Smart Cities ◽  
Cost Effective ◽  
Cyber Attacks ◽  
Security And Privacy ◽  
Sensitive Information ◽  
Security Framework ◽  
Smart Transportation ◽  
Security Issues

Smart cities are established on some smart components such as smart governances, smart economy, science and technology, smart politics, smart transportation, and smart life. Each and every smart object is interconnected through the internet, challenging the security and privacy of citizen's sensitive information. A secure framework for smart cities is the only solution for better and smart living. This can be achieved through IoT infrastructure and cloud computing. The combination of IoT and Cloud also increases the storage capacity and computational power and make services pervasive, cost-effective, and accessed from anywhere and any device. This chapter will discuss security issues and challenges of smart city along with cyber security framework and architecture of smart cities for smart infrastructures and smart applications. It also presents a general study about security mechanism for smart city applications and security protection methodology using IOT service to stand against cyber-attacks.

Comparing Machine Learning Models for the Predictions of Speed in Smart Transportation Systems

2022 ◽  
pp. 34-46
Author(s):  
Amtul Waheed ◽  
Jana Shafi ◽  
Saritha V.
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
Smart Cities ◽  
Learning Algorithms ◽  
High Capacity ◽  
Transportation Systems ◽  
Machine Learning Algorithms ◽  
Smart Transportation ◽  
Data Propagation ◽  
Machine Learning Models

In today's world of advanced technologies in IoT and ITS in smart cities scenarios, there are many different projections such as improved data propagation in smart roads and cooperative transportation networks, autonomous and continuously connected vehicles, and low latency applications in high capacity environments and heterogeneous connectivity and speed. This chapter presents the performance of the speed of vehicles on roadways employing machine learning methods. Input variable for each learning algorithm is the density that is measured as vehicle per mile and volume that is measured as vehicle per hour. And the result shows that the output variable is the speed that is measured as miles per hour represent the performance of each algorithm. The performance of machine learning algorithms is calculated by comparing the result of predictions made by different machine learning algorithms with true speed using the histogram. A result recommends that speed is varying according to the histogram.

scholarly journals A Survey of Context-Aware Messaging-Addressing for Sustainable Internet of Things (IoT)

2020 ◽  
Vol 12 (10) ◽  
pp. 4105
Author(s):  
Alaa Omran Almagrabi ◽  
Yasser D. Al-Otaibi
Keyword(s):  
Internet Of Things ◽  
Smart Cities ◽  
Contextual Information ◽  
Spatial Models ◽  
Context Aware ◽  
Engineering Technology ◽  
Smart Transportation ◽  
Context Aware Computing ◽  
Iot Applications ◽  
Smart Healthcare

Nowadays, communication engineering technology is merging with the Internet of Things (IoT), which consists of numerous connected devices (referred to as things) around the world. Many researchers have shown significant growth of sensor deployments for multiple smart engineering technologies, such as smart-healthcare, smart-industries, smart-cities, and smart-transportation, etc. In such intelligent engineering technologies, sensors continuously generate a bunch of messages in the network. To enhance the value of the data in the messages, we must know the actuality of the data embedded inside the messages. For this purpose, the contextual information of the data creates a vital challenge. Recently, context-aware computing has emerged to be fruitful in dealing with sensor information. In the ubiquitous computing domain, location is commonly considered one of the most essential sources of context. However, whenever users or applications are concerned with objects, and their site or spatial relationships, location models or spatial models are necessary to form a model of the environment. This paper investigates the area of context-aware messaging and addressing services in diverse IoT applications. The paper examines the notion of context and the use of context within the data exchanged by the sensors in an IoT application for messaging and addressing purposes. Based on the importance and need for context of the information, we identify three critical categories of new IoT applications for context-aware messaging and addressing services: emergency applications, applications for guiding and reminding, and social networking applications. For this purpose, a representative range of systems is reviewed according to the application type, the technology being used, their architecture, the context information, and the services they provide. This survey assists the work of defining an approach for context-aware messaging services domain by discovering the area of context-aware messaging.

scholarly journals GNSS-Free Outdoor Localization Techniques for Resource-Constrained IoT Architectures: A Literature Review

2021 ◽  
Vol 11 (22) ◽  
pp. 10793
Author(s):  
Azin Moradbeikie ◽  
Ahmad Keshavarz ◽  
Habib Rostami ◽  
Sara Paiva ◽  
Sérgio Ivan Lopes
Keyword(s):  
Large Scale ◽  
Smart Cities ◽  
Low Cost ◽  
Security And Privacy ◽  
Smart Manufacturing ◽  
Localization Accuracy ◽  
Smart Transportation ◽  
Smart Healthcare ◽  
Definition Of ◽  
Outdoor Localization

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.

Fruitful Synergy Model of Artificial Intelligence and Internet of Thing for Smart Transportation System

2020 ◽  
Vol 4 (1) ◽  
pp. 43-57 ◽  
Author(s):  
Vikram Puri ◽  
Chung Van Le ◽  
Raghvendra Kumar ◽  
Sandeep Singh Jagdev
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Urban Transportation ◽  
Fall Detection ◽  
Transportation System ◽  
Transportation Systems ◽  
Support Vector ◽  
Smart Transportation ◽  
Artificial Neural Network Ann

In urban transportation systems, bicycle sharing systems are majorly deployed in major cities of both developed and developing countries. The recent boom of bicycle sharing system along with its upgraded technology have opened new opportunities towards urban transportation system. With the enlargement of intelligent transportation systems (ITS's), smart bicycle sharing schemes are more popular to smart cities as a green transportation mode. In this article, the Internet of Things (IoT) and artificial intelligence-based monitoring devices have been proposed for the bicycles. This system contains a harmful exhaust gas sensor, wireless module, and a GPS receiver and camera that are capable to send data with time and date stamping. In addition, sensor also integrated on the bicycle for the fall detection. An artificial neural network (ANN) and support vector machine (SVM) applied to the data collected at central server is designed to analyze the root mean square error (RMSE), and coefficient of correlation (R2). Result shows that ANN performance is better when compared to SVM.

scholarly journals MOBDA: Microservice-Oriented Big Data Architecture for Smart City Transport Systems

2020 ◽  
Vol 4 (3) ◽  
pp. 17 ◽  
Author(s):  
Suriya Priya R. Asaithambi ◽  
Ramanathan Venkatraman ◽  
Sitalakshmi Venkatraman
Keyword(s):  
Big Data ◽  
Intelligent Transportation Systems ◽  
Consumer Satisfaction ◽  
Smart Cities ◽  
Transportation Systems ◽  
Transport Systems ◽  
Hybrid Architecture ◽  
Smart Transportation ◽  
Transport Services ◽  
Data Architecture

Highly populated cities depend highly on intelligent transportation systems (ITSs) for reliable and efficient resource utilization and traffic management. Current transportation systems struggle to meet different stakeholder expectations while trying their best to optimize resources in providing various transport services. This paper proposes a Microservice-Oriented Big Data Architecture (MOBDA) incorporating data processing techniques, such as predictive modelling for achieving smart transportation and analytics microservices required towards smart cities of the future. We postulate key transportation metrics applied on various sources of transportation data to serve this objective. A novel hybrid architecture is proposed to combine stream processing and batch processing of big data for a smart computation of microservice-oriented transportation metrics that can serve the different needs of stakeholders. Development of such an architecture for smart transportation and analytics will improve the predictability of transport supply for transport providers and transport authority as well as enhance consumer satisfaction during peak periods.

scholarly journals Enhancing the Reliability of Cellular Internet of Things through Agreement

2020 ◽  
Vol 10 (21) ◽  
pp. 7699
Author(s):  
Shin-Hung Pan ◽  
Shu-Ching Wang
Keyword(s):  
Internet Of Things ◽  
Large Scale ◽  
Fault Tolerant ◽  
Smart Cities ◽  
Smart Devices ◽  
The Internet ◽  
Smart Transportation ◽  
Fifth Generation ◽  
Smart Healthcare ◽  
Wide Range

Because the Internet of Things (IoT) can provide a global service network through various smart devices, the IoT has been widely used in smart transportation, smart cities, smart healthcare, and factory automation through the Internet connection. With the large-scale establishment and 5G (fifth generation) wireless networks, the cellular Internet of Things (CIoT) will continue to be developed and applied to a wide range of applications. In order to provide a reliable application of CIoT, a safe and reliable network topology MECIoT is proposed in this study. To improve the reliability and fault-tolerant capability of the network proposed, the problem of reaching agreement should be revisited. Therefore, the applications in the system can still be performed correctly even if some processing units (PUs) in the system have failed. In this study, a new protocol is proposed to allow all normal PUs in MECIoT to reach an agreement with the minimum amount of data exchanges required and the maximum number of failed PUs allowed in MECIoT. In the end, the optimality of the protocol has been proven by mathematical method.

scholarly journals Smart Cities and Data Analytics for Intelligent Transportation Systems: An Analytical Model for Scheduling Phases and Traffic Lights at Signalized Intersections

2021 ◽  
Vol 11 (15) ◽  
pp. 6816
Author(s):  
Fatih Gunes ◽  
Selim Bayrakli ◽  
Abdul Halim Zaim
Keyword(s):  
Traffic Control ◽  
Intelligent Transportation Systems ◽  
Smart Cities ◽  
Transportation Systems ◽  
Signalized Intersections ◽  
Effective Control ◽  
Main Role ◽  
Smart Transportation ◽  
Traffic Lights ◽  
Service Rates

This paper is intended to improve the performance of signalized intersections, one of the most important systems of traffic control explained within the scope of smart transportation systems. These structures, which have the main role in ensuring the order and flow of traffic, are alternative systems depending on the different methods and techniques used. Determining the operation principles of these systems requires an extremely careful and planned study, considering their important role. Performance outputs obtained from the queue analyses made in previous studies formed the input of this study. The most important techniques are used in the effective control of intersections, such as signal timing: in particular, the use of effective green time and order of the transitions between phases. In this research, a traffic-sensitive signalized intersection control system was designed with the suggested methods against these two problems. The sample intersections were selected from three cities with the highest population density as the case study area. In the analysis of the performance of the connection arms of the selected intersections, flow intensity data were taken into consideration, as well as the arrival and service rates. Based on this, the outputs of the two proposed models regarding the use of phase transitions and effective green durations were compared with two other adaptive control systems and their positive results were shared. The results showed that signalized intersections, operating with a well-planned and correctly chosen technique, better regulate density and queuing.

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