Routing protocols strategies for flying Ad-Hoc network (FANET): Review, taxonomy, and open research issues

A Flying Ad Hoc Network (FANET) is a self-organizing wireless network comprised of clusters of Unmanned Aerial Vehicles (UAVs) or drones that communicate while nearby. FANETs are increasingly used in a variety of applications, including smart ports, delivery of products, construction, monitoring of the environment and climate, and military surveillance. FANETs research is being driven by the potential for UAVs to be utilized in these regions. The purpose of this paper is to provide a comprehensive analysis of the most important FANET characteristics, mobility models, applications, and routing protocols. The present paper is an effort to provide a comprehensive description of the various routing techniques utilized by the most prevalent routing protocols in FANETs, including topology-based, position- based, hierarchical, swarm-based, and Delay Tolerant Networking (DTN) protocols. Reinforcement learning and deep reinforcement learning are both encompassed in a newly anticipated classification. In the meanwhile, this study primarily centres around the taxonomy for learning agents (single- agent, multi-agent) and learning models (model-based and free-model). In addition, the paper intends to shed light on identifying the applications of FANETs in various categories and identify research gaps and future opportunities in this field. In addition, it compares the results qualitatively to those of the previous surveys. Any future work on the FANET routing protocol could benefit from this paper as a reference and roadmap.