## 4 System Design and Goals
### Modification to Message Intervals and New Message Variables
#### 4.4.5 DRI Function
Subsequently, these parameter values are utilized by the proposed routing protocol to identify optimal routing information and relay nodes to the target destination. A Constant Bit Rate (CBR) traffic model has been utilized for this study because it generates traffic at a constant rate, with packets sent at regular intervals and with a transmission rate of 5 packets/second, given as P\({}_{3}\). The total seconds spent transmitting data with modified algorithms (P\({}_{1}\)) and the total seconds spent receiving data with modified algorithms (P\({}_{2}\)) were used. These transmission rates have been used throughout the four performance metrics considered in this paper. Seven simulations were conducted for each scenario with a network density of 10, 30, 50, and 100 nodes, and the statistical average was computed. Each simulation lasted for 1800 s, as given in Table 1. We provide a detailed analysis of the proposed routing protocol, focusing on four performance evaluation metrics: routing overhead, energy consumption, packet delivery, and end-to-end delay. The simulation models highlighted in this paper are essential tools for studying complex systems. These models provide a virtual environment, enabling controlled experiments and observations without the limitations of physical implementation. Our carefully designed simulation models aim to replicate real-world scenarios, contributing to a deeper understanding of dynamic interactions and intricate behaviors. ## 7 Result Discussion

This section presents the analysis of our simulation results and compares them with the OLSR, OLSRv2, and other improved OLSR results in the related literature, such as in [42]. The simulation parameters and their values are presented in Table 1 Section 5.2 The simulations were conducted in a static and mobile environment using network scenarios with 10, 30, 50, and 100 nodes. The results show that our improved OLSR outperforms the existing OLSR, OLSRv2, and other improved routing protocols in optimizing energy consumption, routing overhead, packet delivery ratio, and end-to-end delay. The simulations for energy consumption in static scenarios and scenarios with various mobility speeds were carried out by implementing the modified variables and their values into the simulation environment. The simulation results for the static scenario, as shown in Figure 14, indicate that our proposed routing protocol outperforms OLSR and OLSRv2 in energy consumption across static scenarios with 10, 30, 50, and 100 nodes. This superiority is attributed to reduced overhearing, improved link management, and optimized information base management. The proposed protocol achieves this by implementing efficient neighbor discovery, minimizing irrelevant message processing, and ensuring that only active and reliable links are used. Programmable listening and base chore periods further enhance efficiency by managing information bases effectively. For scenarios with various mobility speeds, our analysis highlights that our proposed protocol effectively considers the mobility speed of nodes in the selection of the MPR, resulting in efficient energy consumption as compared to the OLSR and OLSRv2, as shown in Figure 15. By dynamically adjusting MPR selection based on the node's energy and mobility, our protocol optimizes routing decisions, reducing unnecessary overhead and conserving energy. This adaptive approach ensures efficient communication even in dynamic environments, enhancing network performance and longevity. In general, the energy consumption of the proposed routing protocol is quite impressive, taking into account the huge packet delivery in all scenarios and, therefore, demonstrating robustness and energy efficiency as compared to the energy consumed by nodes in related research such as the work of [42]. The simulation for routing overhead was also conducted for both static scenarios and scenarios with various mobility speeds for scenarios with 10, 30, 50, and 100 nodes.