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The main objective of this paper/project is to focus on an Agent based approach to flight control in ground/ runway. Here we are trying to provide a autonomous control on flight once it comes to runway. In all airport there is a particular structure for runway, Like main runways ,sub runways ,different tracks. In an International air port there will be a busy traffic. There will be more flights and other ground vehicles. By using this particular system we can move the flight from one location to another ,totally through wireless control. For a better autonomous control level of intelligence matters. A type 2 fuzzy interval controller can be applied to the autonomous vehicle in order to handle uncertainty in a better way. Ant colony optimization technique can be used for a optimized path planning in traffic environment with more number of flight. We are using a hybrid ant colony optimization to handle real time dynamic environment and path planning. Both Agent based and type-2 fuzzy together with Ant Colony Optimization technique is used to achieve another level of intelligence.

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Due to the exponential growth in the vehicle population it become very much challenging to control the pollution in the green city. As the vehicle waiting time in a traffic signal is diagonally proportional to the increase in pollution the traffic scheduling become essential. The IOT based wireless sensor based monitoring and traffic scheduling is an effective method but it always suffer from the energy efficiency of sensor network which ultimately aversively affect the environment of Green city. A fuzzy logic based traffic scheduling improves the system as the expert knowledge of a traffic police and environment specialist can be incorporated in the control algorithm. Genetic algorithm based or particle swarm optimization based approach will be able to improve the energy efficiency of the network as optimize the performance by considering multiple parameters which affect the environment.

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