## 3 OLSR-Based Protocols
### Standard OLSR  
OLSR is a proactive routing protocol that regularly shares topological data between network nodes. The objective of OLSR is to operate independently from other network protocols. In addition, OLSR does not depend on any calculations performed at the layer below it (i.e., the connection layer). OLSR is utilized in ad-hoc network families, such as MANETs [20], VANETs [21], and most recently FANETs [22].  
A subset of each node's network neighbors is selected to function as multipoint relays (MPRs). Only MPRs are responsible for relaying control packets intended for dissemination throughout the whole network in OLSR. MPRs reduce the number of transmissions that are needed to send control messages, which enables them to create a reliable and effective solution. In addition, MPRs are responsible for transmitting connection state information inside the UAV network. This information is applied in route computation to generate routes between two network nodes, commencing at a source node and ending at a destination node [23; 24].  
Two types of control messages are used in OLSR. These types of messages are \"Hello\" and \"Topology Control (TC)\" messages. Every node in the network receives \"Hello\" messages informing them of their link condition and any neighbors within two hops. To keep an essential database for packet routing, each node in the network broadcasts TC messages. By responding to events with TC messages and decreasing the maximum periodic time interval, OLSR is routinely optimized [19].  
Figure 2 shows the formats of the \"Hello\" and \"Topology Control (TC)\" messages.  
\begin{table}
\begin{tabular}{c c c c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**Simulation Scenario**} & \multicolumn{4}{c}{**Performance Metrics**} \\ \hline
**Reference/Year** & **Routing** & **SAR** & & & & & & & & & \\
**Protocols** & **Station** & **Mobility** & **Density** & **Allowed** & **PDR/Drop** & **Latency/Delay** & **Throughput** & **Energy** & **Consumption** \\ \hline Ref. [12] & ACO/DSDV, & & & & & & & & & & \\
2018 & DS, OLSR, & x & & & & & & & & \\
2018 & AO/DVD & & & & & & & & & \\ Ref. [13] & ACO/O, OISR, HWMP & x & & & & & & & & \\
2015 & & & & & & & & & & \\ Ref. [14] & OLSR, AO/DSDR, & x & & & & & & & & \\
2020 & TO/D, GRP & & & & & & & & & \\ Ref. [15] & AO/D, DSR, GRP, & x & & & & & & & & \\
2020 & OLSR & & & & & & & & & \\ Ref. [16] & DSR, AOD/GSD, OIS, E-OLSR & x & & & & & & & & \\
2022 & & & & & & & & & & \\ Ref. [17] & & & & & & & & & & \\
2022 & & CLEA-AODV & & x & & & & & & & \\ Ref. [18] & ZZBDSR, OLSR, AODV & & & & & & & & & \\
2021 & & & & & & & & & & \\ OUR WORK & OLSR, D-OLSR, ML-OLSR, & & & & & & & & & \\
2023 & & P-OLSR & & & & & & & & \\ \hline \hline \end{tabular}
\end{table}
Table 1: A comparison of related works with this study.  
Trajectory-aided OLSR (TOLSR) [25] is a recent routing protocol that utilizes the trajectory of UAVs as a known factor to improve optimum link-state routing. In TOLSR, Q-learning is used to determine the optimal network path. The authors [25] presented a packet-forwarding mechanism to alleviate a typical problem encountered by UAVs, namely, deteriorating image quality. The simulation results [25] showed its significant advantages over OLSR and greedy perimeter state routing (GPSR) in a sparsely distributed environment. The PDR was improved by more than 30%, and the end-to-end latency was reduced by more than 40 s. MATLAB software was used to implement many SAR simulation situations.