Wireless Sensor Networks for Intelligent Transportation Systems

2009 ◽  
Author(s):  
Mirko Franceschinis ◽  
Luca Gioanola ◽  
Massimiliano Messere ◽  
Riccardo Tomasi ◽  
Maurizio A. Spirito ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Wireless Sensor

scholarly journals A Hybrid Mobile Node Localization Algorithm Based on Adaptive MCB-PSO Approach in Wireless Sensor Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Hua Wu ◽  
Ju Liu ◽  
Zheng Dong ◽  
Yang Liu
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Three Dimensional ◽  
Mobile Node ◽  
Global Optimum ◽  
Wireless Sensor ◽  
Localization Algorithm ◽  
Mobile Nodes ◽  
Node Localization ◽  
Localization Accuracy

In this paper, a hybrid adaptive MCB-PSO node localization algorithm is proposed for three-dimensional mobile wireless sensor networks (MWSNs), which considers the random mobility of both anchor and unknown nodes. An improved particle swarm optimization (PSO) approach is presented with Monte Carlo localization boxed (MCB) to locate mobile nodes. It solves the particle degeneracy problem that appeared in traditional MCB. In the proposed algorithm, a random waypoint model is incorporated to describe random movements of anchor and unknown nodes based on different time units. An adaptive anchor selection operator is designed to improve the performance of standard PSO for each particle based on time units and generations, to maintain the searching ability in the last few time units and particle generations. The objective function of standard PSO is then reformed to make it obtain a better rate of convergence and more accurate cost value for the global optimum position. Furthermore, the moving scope of each particle is constrained in a specified space to improve the searching efficiency as well as to save calculation time. Experiments are made in MATLAB software, and it is compared with DV-Hop, Centroid, MCL, and MCB. Three evaluation indexes are introduced, namely, normalized average localization error, average localization time, and localization rate. The simulation results show that the proposed algorithm works well in every situation with the highest localization accuracy, least time consumptions, and highest localization rates.

Using wireless sensor networks to support intelligent transportation systems

2010 ◽  
Vol 8 (5) ◽  
pp. 462-473 ◽  
Author(s):  
David Tacconi ◽  
Daniele Miorandi ◽  
Iacopo Carreras ◽  
Francesco Chiti ◽  
Romano Fantacci
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Intelligent Transportation Systems ◽  
Intelligent Transportation ◽  
Transportation Systems ◽  
Wireless Sensor
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