An Application of CHNN for FANETs Routing Optimization
DOI:
https://doi.org/10.13052/jwe1540-9589.195613Keywords:
flying ad hoc network, routing algorithm, neural network, Hopfiled neural networkAbstract
Routing algorithm has a decisive influence on routing quality, and routing quality has a direct impact on network performance. For FANETs, the highly dynamically changing topology poses a challenge to the design of routing algorithms. The paper studies the characteristics of FANETs, and uses a CHNN to search for FANETs routing to form CHNNR. Using NS3 as a simulation tool, a highly dynamic simulation scheme in the background of the network topology of the air flight platform was designed, making the simulation scene closer to the dynamic performance of the FANETs highly dynamic mobile node. By comparing parameters such as network delay, normalized network throughput, routing load and data transmission success rate, the performance of CHNNR and passive routing algorithms is analyzed and compared. The simulation results show that the comprehensive performance of CHNNR is better than other passive routing algorithms, and it is more suitable for FANETs networks where nodes move at a high speed and the network topology changes frequently, and lay the foundation for the next research.
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