No abstract

In the recent times due to the increase of vehicular nodes in a vehicular communication network, there is a need of developing efficient systems in order to optimize the vehicular traffic congestion issues in urban areas. The current research trends shows that most of the conventional studies focused on developing fuzzy inference systems based vehicular traffic congestion model which has gained lots of attention on detecting and minimizing the congestion levels.We have proposed a new approach towards detection and controlling of traffic congestion in VANET. The proposed system utilizes the communication channels very efficiently and irrespective of any kind of overload. This proposed system aims to introduce a novel framework for identifying traffic jam on Vehicular Ad-hoc Networks. In order to detect and minimize the level of congestion our approach will use a fuzzy logic based approach to notify the drivers about available routes during the traffic congestion. An experimental prototype will be set up to enable the graphical simulation.

Internet of Vehicles (IoV) is an evolution of vehicular adhoc network with concepts of internet of things (IOT). Each vehicle in IOV is an intelligent object with various capabilities like sensors, computation, storage, control etc. Vehicles can connect to any other entity in the network using various services like DSRC, C2C-CC etc. Ensuring QoS for vehicle to everything (V2X) communication is a major challenge in IoV. This work applies an integration of hybrid metaheuristics guided routing and service differentiated flow control to ensure QoS in Internet of Vehicles. Clustering based network topology is adopted with clustering based on hybrid metaheuristics integrating particle swarm optimization with firefly algorithm. Over the established clusters routing decision is done using swarm intelligence. Packet flows in the network are service differentiated and flow control is done at cluster heads to reduce the congestion in the network. High congestion in routes is mitigated with back up path satisfying the QoS constraints. Due to optimization in clustering, routing and data forwarding process, the proposed solution is able to achieve higher QoS. Through simulation analysis, the proposed solution is able to achieve 2% higher packet delivery ratio and 9.67% lower end to end packet latency compared to existing works.