A new collision avoidance method for a surveillance algorithm in FANETs

Abstract

In recent years, Flying Ad hoc Network (FANET) has become one of the most popular technologies, thanks to its wide range of applications. One significant application area of FANET networks is surveillance using drones or Unmanned Aerial Vehicles (UAVs), where many factors, including resource consumption, determine the performance of this mission. This has led researchers to implement new mobility models in FANETs, which is the main challenge in ensuring optimal energy consumption. Additionally, the airspace has seen a predominant presence of UAVs, inspiring researchers and developers in both military and civilian fields. UAVs perform tasks professionally, but the mission field is not free from obstacles, both fixed and moving. In the event of a collision, the mission will fail. This situation requires methods and algorithms that allow UAVs to make decisions on how to avoid colliding with these obstacles. In this dissertation, we will gain a better understanding of drone networks and study the different existing mobility models and how they work. We will propose a new monitoring method to ensure optimal energy consumption and a new collision avoidance method to enhance mission success. To test and validate these methods, we will use the NS-3 simulator to compare our proposed methods with existing mobility models and collision avoidance techniques in terms of energy consumption.