## 3 Classifications of Routing Protocols in UAV Networks
### Routing Based on Network Structure
#### 3.1.2 Proactive Routing Protocol  
Proactive routing protocols (PRPs) work by periodically updating routing tables, allowing fast access to routing paths. PRPs meet the application requirements of low latency and fast routing in complex and dynamic environments [15]. However, as the size and mobility of nodes in the network increase, most PRP schemes face certain challenges [32]. Some prominent proactive routing protocols today are variants developed from the Destination Sequence Distance Vector (DSDV) protocol and the Optimized Link State Routing (OLSR) protocol.  
In the DSDV routing protocol, each UAV maintains a routing table along with a sequence number to distinguish between old and new routes. The routing tables are updated periodically or when there is a change in the network. The routing table update message is sent to UAVs in the network. The authors in [38] have developed an Improved Sequencing Heuristic DSDV (ISH-DSDV) routing protocol. It is an improvement of DSDV for UAV networks based on the Nomadic mobility model. To avoid congestion, the authors propose a solution to allocate sequence numbers according to the target node. It helps optimize the data movement process. This protocol updates the routing table with three basic information: final destination, distance, and next node. This method is designed to use the most efficient information in the data transmission process between nodes. In addition, a legacy data entry method is also introduced to easily delete outdated entries. The simulation results show that the ISH-DSDV routing technique is a better choice for deployment in dense networks while improving the operational efficiency of the aircraft.  
OLSR is a proactive routing protocol that frequently shares topological data among network nodes. OLSR uses two types of control messages: \"Hello\" and \"Topology Control (TC)\". Each node in the network receives a \"Hello\" message that informs them about their link status and any neighbors within two hops. To maintain the database needed for packet routing, each node in the network broadcasts TC messages. By responding to events with TC messages and reducing the maximum periodic interval, OLSR is frequently optimized [39]. Some improved variants of the OLSR protocol include Directional Optimized Link State Routing (D-OLSR) [40], which is a routing protocol for UAVs with directional antennas. Mobility and load-aware OLSR (ML-OLSR) [41] was developed to mitigate the impact of high mobility and load imbalance on UAV network performance. The ML-OLSR protocol contains both a mobility-aware algorithm and a load-aware algorithm. P-OLSR is also a variant of the OLSR protocol, designed to predict changes in the expected level of  
Figure 10: DCR-based UAS deployment scenarios: e.g., fire suppression, natural disasters, or search and rescue.  
wireless communication quality across nodes in a UAV network based on the GPS data of each UAV.  
The core performance of the OLSR routing protocol is multipoint forwarding (MPR). However, the traditional MPR selection algorithm suffers from large redundancy. To solve this problem, the authors in [42] proposed an algorithm for the OLSR called Selective Routing OLSR, noted as SROLSR. It selects a new set of MPRs and makes some modifications to the MPR selection process to reduce the number of MPRs. The simulation results show that the improved SROLSR protocol has better performance than the standard version of the OLSR routing protocol in terms of network load, packet delivery rate, and end-to-end delay.  
The above studies highlight research efforts to improve proactive routing protocols for UAV networks, focusing on three important aspects: optimizing energy efficiency, enhancing the ability to adapt to the continuous changes of UAV networks, and improving the overall performance of the system.