Dynamic Decision Making for Connected Vehicles in IoT System

Abstract

Intelligent Transportation System (ITS) which focuses on dynamic decision making for IoT data processes using distributed and decentralized systems has attracted much attention in recent years. In this paper, we propose a novel approach for enabling computational intelligence into dynamic decision-making for connected vehicles to provide various applications of smart cities. The proposed approach is based on the ant colony optimization (ACO) approach, which is able to improve the performance of intelligent transportation systems. In addition, the simulated results are compared with previous works in the literature.