Ruppert's Delaunay Triangulation Refinement Scheme for Optimal RSUs Placement in Vehicle to Infrastructure Communication Network

Road Side Units(RSUs)are the essential component of vehicular communication for the objective of improving safety and mobility in the road transportation.RSUs are generally deployed at the roadside and more specifically at the intersections in order to collect traffic information from the vehicles and disseminate alarms and messages in emergency situations to the neighborhood vehicles cooperating with the network.However,the development of a predominant RSUs placement algorithm for ensuring competent communication in VANETs is a challenging issue due to the hindrance of obstacles like water bodies,trees and buildings.In this paper,Ruppert’s Delaunay Triangulation Refinement Scheme(RDTRS)for optimal RSUs placement is proposed for accurately estimating the optimal number of RSUs that has the possibility of enhancing the area of coverage during data communication.This RDTRS is proposed by considering the maximum number of factors such as global coverage,intersection popularity,vehicle density and obstacles present in the map for optimal RSUs placement,which is considered as the core improvement over the existing RSUs optimal placement strategies.It is contributed for deploying requisite RSUs with essential transmission range for maximal coverage in the convex map such that each position of the map could be effectively covered by at least one RSU in the presence of obstacles.The simulation experiments of the proposed RDTRS are conducted with complex road traffic environments.The results of this proposed RDTRS confirmed its predominance in reducing the end-to-end delay by 21.32%,packet loss by 9.38%with improved packet delivery rate of 10.68%,compared to the benchmarked schemes.

Rice yield estimation using remote sensing and simulation model

Remote sensing techniques have the potential to provide information on agricultural crops quantitatively , instantaneously and above all nondestructively over large areas . Crop simulation models describe the relationship between physiological processes in plants and environmental growing conditions. The integration between remote sensing data and crop growth simulation model is an important trend for yield estimation and prediction, since remote sensing can provide information on the actual status of the agricultural crop. In this study, a new model(Rice-SRS) was developed based mainly on ORYZA1 model and modified to accept remote sensing data as input from different sources. The model can accept three kinds of NDVI data: NOAA AVHRR(LAC)-NDVI,NOAA AVHRR(GAC)-NDVI and radiometric measurements-NDVI. The integration between NOAA AVHRR (LAC) data and simulation model as applied to Rice-SRS resulted in accurate estimates for rice yield in the Shaoxing area, reduced the estimating error to 1.027%,0.794% and (-0.787%) for early, single, and late season respectively. Utilizing NDVI data derived from NOAA AVHRR (GAC) as input in Rice-SRS can yield good estimation for rice yield with the average error (-7.43%). Testing the new model for radiometric measurements showed that the average estimation error for 10 varieties under early rice conditions was less than 1%.