Optimal Dropbox Deployment Algorithm for Data Dissemination in Vehicular Networks - 2018

PROJECT TITLE :

Optimal Dropbox Deployment Algorithm for Data Dissemination in Vehicular Networks - 2018

ABSTRACT:

For vehicular networks, dropboxes are very helpful for aiding the data dissemination, as they can greatly increase the contact probabilities between vehicles and cut back the data delivery delay. But, because of the pricey deployment of dropboxes, it's impractical to deploy dropboxes in an exceedingly dense manner. In this Project, we tend to investigate the way to deploy the dropboxes optimally by considering the tradeoff between the delivery delay and the value of dropbox deployment. This is often a terribly difficult issue due to the problem of correct delay estimation and also the complexity of solving the optimization downside. To address this issue, we 1st provide a theoretical framework to estimate the delivery delay accurately. Then, based mostly on the idea of dimension enlargement and dynamic programming, we have a tendency to style a novel optimal dropbox deployment algorithm (ODDA) to obtain the optimal deployment strategy. We tend to prove that ODDA contains a fast convergence speed, that is less than ? (? <; n) iterations for convergence. We have a tendency to also prove that the computational complexity of ODDA is O(nkm logm), i.e., ODDA incorporates a polynomial computational complexity for a given m, the quantity of dropboxes for deployment. Performance analysis by simulation demonstrates the superior performance of the proposed methods compared with the benchmark methods.

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