Routing Algorithms for Flying Ad-hoc Networks (FANETs)

University
California State University
Course
CS 3590 | Data Communications and Networking
Pages

1

Academic year

2023
Author

Jithin Jacob Issac
Views

0

p {margin: 0; padding: 0;} .ft00{font-size:21px;font-family:NimbusSansBold;color:#000000;} .ft01{font-size:18px;font-family:NimbusSansBold;color:#000000;} .ft02{font-size:15px;font-family:OpenSymbol;color:#000000;} .ft03{font-size:18px;font-family:NimbusSans;color:#000000;} .ft04{font-size:18px;line-height:23px;font-family:NimbusSans;color:#000000;} ROUTING ALGORITHMS FOR FLYING AD-HOC NETWORKS (FANETS) Introduction  FANETs consist of unmanned aerial vehicles communicating with groundstations  Node mobility and changing topology make routing challenging  Nature-inspired algorithms (NIAs) promising for FANET routing Existing Algorithms  Ant colony optimization (ACO) - mimics ant colony behavior  Firefly algorithm (FA) - based on flashing light attraction of fireflies  Genetic algorithm (GA) - uses natural selection and genetics Modified Algorithms  Modified FA (MFA) - adds clustering of nodes  Modified GA (MGA) - uses location-based mobile coverage Simulation Setup  Algorithms: ACO, MFA, MGA  Simulator: ns-3.26  Parameters: packet delivery ratio, delay, overhead, throughput Results  MFA has best performance on all metrics  ACO second best after MFA  MFA and ACO show most promise for FANET routing Future Work  Enhance ACO and other NIAs for better FANET performance  Develop new bio-inspired routing algorithms In summary, modified firefly algorithm performs the best for routing in flying ad-hocnetworks based on simulations. Further improvements to swarm intelligencealgorithms could enable more efficient FANET communication.