scholarly journals Deploying Wireless Sensor Devices in Intelligent Transportation System Applications

10.5772/25993 ◽  
2012 ◽  
Author(s):  
Kirusnapillai Selvarajah ◽  
Budiman Arief ◽  
Alan Tully ◽  
Phil Blythe
Keyword(s):  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Wireless Sensor ◽  
Sensor Devices

scholarly journals Internet of Things and Intelligent Transportation System

2021 ◽  
Vol 2066 (1) ◽  
pp. 012066
Author(s):  
Dandan Zhang ◽  
Panjing Tan
Keyword(s):  
Internet Of Things ◽  
Intelligent Transportation System ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Route Optimization ◽  
Wireless Sensor ◽  
The Internet ◽  
Dijkstra Algorithm ◽  
Design Scheme ◽  
The Internet Of Things

Abstract As the core of the new monitoring system, the Internet of Things realizes the integration of wireless sensor networks and traditional communication networks, and provides a platform for remote management and monitoring of the underlying equipment. The intelligent transportation system framework built on this basis combines intelligent transportation technology and the organic combination of vehicle management technologies is conducive to the safety, speed and reliability of vehicle transportation, and plays an important role in further reducing transportation costs. Based on this, this article launched the research on the Internet of Things and intelligent transportation systems. This article first summarizes the concepts of the Internet of Things, intelligent transportation and wireless sensor network technology and the current research status at home and abroad. By analyzing and comparing the performance and characteristics of various communication methods, embedded core microprocessors and embedded operating systems, this paper proposes an overall design scheme of the Internet of Things transportation system based on embedded technology. This paper analyzes the path planning problems in the application of the transportation system, combining the shortest path algorithm simulation results and the actual characteristics of the transportation network, and proposes a simulation data fitting method based on two network parameters and Bellman-Ford, Dijkstra, and Floyd algorithms. The route optimization scheme, and the above-mentioned design scheme was implemented in the transportation system, and the scheme verification was carried out. Finally, this article describes in detail the overall debugging process and operating results of the transportation system, thereby fully verifying the feasibility and correctness of the design and implementation methods of the intelligent transportation system based on the Internet of Things. The research results show that when the INF-PROPORTION is small, the Dijkstra algorithm is better than the Bellman-Ford algorithm. When INF-PROPORTION=0.3, the two algorithms T overlap. Since then, the advantages of the Bellman-Ford algorithm gradually appear, but it is approaching in INF-PROPORTION. At 1 o’clock, the Dijkstra algorithm has a sharp decrease, which is again smaller than the Bellman-Ford algorithm. The second loop condition in the main loop of Dijkstra’s algorithm cannot be satisfied, resulting in a decrease in T.

Application Research of Wireless Sensor Network in Intelligent Transportation System

2010 ◽  
Vol 108-111 ◽  
pp. 1170-1175 ◽  
Author(s):  
Xue Dong Du ◽  
Jiang Tao Ji ◽  
Da Peng Yan
Keyword(s):  
Wireless Sensor Network ◽  
Sensor Network ◽  
Traffic Congestion ◽  
Mobile Agents ◽  
Intelligent Transportation System ◽  
Dynamic Network ◽  
Intelligent Transportation ◽  
Transportation System ◽  
Urban Traffic ◽  
Wireless Sensor

The research of using wireless sensor network to settle the problem of urban traffic has become a hotspot in the fields of intelligent transportation system. In this paper, a wireless sensor network model based on mobile agents is proposed. Since mobile agents have characteristics of autonomous collaboration and asynchronous interaction, this model owns the abilities of adapting to the dynamic network changes, reducing the network load and enhancing the data fusion. In the application of the model, the travelers can be guided to choose the right road line through calculation of the predicated value of traffic congestion, so the condition of the traffic congestion can be remitted in some way.

Sign in / Sign up

Export Citation Format

Share Document