## 4 Topology-Based Routing Protocols in FANETs
### Proactive Routing Protocol  
Proactive routing protocols maintain a routing table that contains all of the network's routing information. These routing tables are updated and shared between nodes periodically, and tables must be updated when the topology changes. The primary benefit of proactive routing protocols is that they always have the most up-to-date information. Routing messages must be transmitted between all communication nodes to maintain the routing tables. As a result, proactive routing algorithms precompute pathways to all network destinations in order for nodes to begin data delivery immediately, dramatically lowering delivery delay. Grid Position No Centre Shortest path (GPNC-SP) is an unmanned aerial system shortest path routing algorithm. It replaces the original Euclidean distance with the logical grid distance to lessen the sensitivity of fast-moving nodes. By utilizing a perception and updating algorithm, this algorithm automatically computes and maintains the adjacency connection and topological structure, and utilizes the Dijkstra algorithm to find the shortest routing path. Additionally, a regional reconstruction technique (RSS) is used to optimize the routing path dynamically. Simultaneously, two metrics are employed to establish the optional scope of logical grid width, namely, the percentage of the effective communication area and the sensitivity to logical grid size. MATLAB was used to validate the protocol against the DSDV and DREAM routing protocols [89]. OLSR-ETX is a new implementation of the optimized link-state routing (OLSR) protocol that can adapt to quick dynamic topology changes and prevent communication interruptions. The crucially important concept is to leverage GPS information to determine when the link on the path should be expiring and choose the best relay node based on remaining energy. The NS-3 simulator performs the improved multimetric ETX (expected transmission count) simulation in OLSR. In terms of packet transmission, end-to-end delay, and overhead, the improved OLSR with expected transmission count (OLSR-ETX) outperforms the traditional OLSR [90]. TOLSR is a novel protocol that utilizes the trajectory of unmanned aerial vehicles as a known factor to improve optimum link-state routing (OLSR). To determine the optimal route for the system, Q-learning is used in this protocol. Additionally, a packet-forwarding system is detailed to tackle a typical issue encountered by UAVs: declining image quality. The simulation findings demonstrate considerable gains over OLSR and GPSR in a sparsely distributed environment, with the packet delivery ratio increasing by over 30% and the end-to-end delay decreasing by over 40 s. Several search and rescue simulation scenarios were implemented using MATLAB software [91]. The ML-OLSR protocol is a modified OLSR protocol that incorporates FANETs mobility and load awareness. In order to carry out the computations, two algorithms were installed in the protocol that used the GPS information of nodes. The first mobility-aware algorithm utilizes a statistical estimation of communication channels to calculate the stability degree of nodes. Thus, each surrounding node was assigned a weight, and then a reachability degree for the node was calculated. The second load-aware algorithm was able to identify nodes in the network by looking at the queued packets that were present at a node's interfaces and how much interference its surrounding links caused. QualNet's simulator was used to model and simulate the ML-OLSR protocol. In terms of packet delivery ratio and end-to-end delay, the ML-OLSR protocol performed better [92]. P-OLSR is an OLSR protocol for FANETss that is predictive in nature. It facilitates effective routing in highly dynamic environments. The P-OLSR protocol makes routing decisions dynamically by utilizing the nodes' GPS information. To optimize routing decisions, the protocol calculates the expected transmission count (ETX) by comparing the speeds of two flying nodes.