## 2 Related Work  
In wireless devices, a routing protocol operating at the network layer facilitates the transmission of data packets among interconnected nodes [31; 32]. In 5G/B5G technology, the implementation of D2D communication requires efficient routing and relay selection to enhance Quality of Service (QoS) performance [24; 32]. [PERSON] [33] implemented OLSR [15] in the NS-3 simulator by porting OLSR implementation codes from NS2 [34; 35]. His implementation added support for MID messages. According to [30], most MANET practitioners believe the loss is caused by MPRs, which tend to function nicely in highly dense environments but create these shortcomings in sparse networks. Others think this is due to the excessive flooding of TC messages in the network [30]. [PERSON] et al. [36] proposed an improvement of how an OLSR selects an optimal MPR and called it M-OLSR. The improvement incorporates a system whose weight considers density metrics, mobility, and energy. The improvement was dependent on the k-means clustering-enabled mobility metric proposed by [PERSON] et al. [37]. Their method improved the OLSR algorithm by addressing factors such as adaptability to network conditions, node stability, energy efficiency, predefined thresholds, and expert analysis. This method enhances the versatility of the protocol in optimizing MPR selection across various network scenarios. The dynamic adjustment of the mobility metric within the protocol contributes to the stable and efficient selection of MPR nodes, as well as improving the Quality of Service (QoS) in dynamic network environments. The optimal configuration of this metric which accounts for direction, historical movements, and speed has already demonstrated significant improvements. Future efforts will focus on proposing additional algorithms to further refine the OLSR protocol, reflecting researchers' ongoing dedication to advancing efficient and reliable routing protocols. [PERSON] and [PERSON] [38] proposed a novel algorithm, the Hybrid ACO-PSO Meta-Heuristic (HAPM), which combines Ant Colony Optimization (ACO), Particle Swarm Optimization (PSO), and a dynamic queue mechanism to enhance Quality of Service (QoS) constraints and reduce data dropping. The HAPM algorithm demonstrated superior performance in key metrics such as throughput, packet delivery ratio (PDR), hop count (Hc), routing overhead, and end-to-end delay, when compared to standalone PSO, ACO, and other hybrid algorithms like the enhanced AODV. Notably, the HAPM algorithm achievedsignificant reductions in hop count of 83%, 87%, and 90% compared to the enhanced AODV, PSO, and ACO, respectively. Despite these improvements, the algorithm does not increase time complexity, making it a viable solution for complex network environments. The study also highlighted the growing focus on swarm-based multicast routing protocols, with the HAPM algorithm offering a promising approach for solving multicast routing problems in large networks. [PERSON] and [PERSON] (2018) propose an enhanced approach to reduce control packet overhead in dense networks by applying game theory to the optimized OLSR protocol. The method utilizes two policies, \"willingness_always\" and \"willingness_never,\", to decrease the MPR ratio by excluding nodes with less influence from being nominated as MPRs. Implemented in Riverbed Modeler 18.0, the improved protocol was compared to the original OLSR and demonstrated superior performance across most metrics, with the exception of a negligible increase in end-to-end delay. The proposed algorithm dynamically adjusts a node's willingness to become an MPR based on its influence on neighboring nodes, using indexed HELLO messages to prevent duplication. Although the study did not account for the battery power of nodes--an important consideration given OLSR's high power consumption--future work aims to address this by incorporating battery life and Quality of Service (QoS) parameters, particularly for multimedia applications.