Evolutionary algorithm based network coding for optimization of intelligent vehicular ad hoc network - Lee, Chun Hoe (2017)
This project aims to improve the throughput, energy consumption and overhead of vehicular ad hoe network (VANET) by optimising the network coding (NC) using Genetic Algorithm and Particle Swarm Optimisation (GA-PSO). VANET shows a promising technology as it could enhance the traffic efficiency and promote traffic safety on the road systems. The conventional store-and-forward transmission protocol used in the intermediate node(s) simply stores the received packet and then send at a later time to the destination. However, the rapid changing in VANET topology has made the conventional store-and-forward approach inefficient to meet the throughput and reliability demand posed by VANET. Hence, NC is proposed to perform additional functions on the packet in the source or intermediate node(s). The results showed that the NC used in wireless network outperforms the conventional store-and-forward in terms of throughput and energy consumption. However, the chances to perform NC in wireless network is highly unlikely if the packet is not transmit to the potential NC node. Therefore, GA based network routing (GANeR) is embedded into network to search for shortest path from the source to the destination, and PSO based coding aware routing (CAR) is also proposed to further converge the solutions obtained from GANeR. It showed that the developed GA-PSO in this work provides a better route with coding opportunities and reduces energy consumption in the network. The total energy consumed by GA-PSO is 7.39% fewer than the store-and-forward approach and 4. 77% fewer than NC in wireless network transmission and forwarding structure (COPE).