Generation of Optimal Coverage and Resource Allocation Policies from a Vanet Infrastructure Using Machine Learning Algorithms

Project Details


General objective Propose an algorithmic model of coverage and allocation of resources from the VANET infrastructure on urban planning scenarios in order to cover the majority of users in dynamic, predictive and scalable environments using Machine Learning algorithms. Justification The present work aims to explain the importance of coverage and the optimal allocation of resources in VANETs to investigate the behavior of the communications network, based on the generation of a determined set of algorithms that satisfy the need for network dimensioning under optimal environments. and also allow the analysis of its performance, determining its efficiency and achieving the referred indicators. The theoretical study based on qualified material from several scientific journals leads us to its theoretical, methodological and practical justification. The execution through simulations with Machine Learning algorithms and also using specialized tools for this purpose to obtain verification tests, with their respective results. The study, tests and development of the definitions, standards, protocols, as well as unified technologies in VANETs give greater benefits to the system.
Effective start/end date11/06/2011/06/22


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