## 2 Related Work
To enhance energy efficiency with various mobility speeds of nodes, ref. [42] presents a Disaster Scenario-Optimized Link State Routing (DS-OLSR) protocol with Message Prioritisation (DS-OLSRMP). The DS-OLSRMP is a novel approach to enhance the energy efficiency and communication reliability of disasters in terms of recovery scenarios of low-battery devices using an OLSR protocol. The study introduces the ALERT message, a new type of message designed to prioritize communication based on the battery level of devices, by grouping node priority into four stages, namely Low, Medium, High, and Critical. This proposed solution effectively prioritizes status notifications and message delivery, ensuring that low-battery, energy-consuming devices are not overburdened and can continue to function during critical operations. This strategy includes modifying the multipoint relay (MPR) mechanism to prevent the selection of MPR nodes with low battery, thereby extending their operational lifespan and ensuring timely message delivery. The paper emphasizes the importance of balancing communication efficiency with energy conservation, particularly in the context of disaster response where communication networks must remain operational under challenging conditions. Simulation results demonstrate the effectiveness of the DS-OLSRMP protocol in maintaining high packet delivery rates and reducing the likelihood of message repetition. This contributes to a more resilient and sustainable communication infrastructure for disaster scenarios. The study also evaluates the scalability of the proposed protocol under varying network sizes and node speeds, highlighting its robustness and suitability for large-scale mobile ad hoc networks (MANETs). [PERSON] and [PERSON] [43] introduce an innovative protocol to address the energy consumption and routing challenges in Mobile Ad Hoc Networks (MANETs). This is carried out by integrating a chaotic bat swarm with the deep Q-learning Network into the CBS-OLSR protocol, which leverages the MPR technology of OLSR, thereby enhancing it with deep Q-learning to optimize MPR set selection. This results in significantly reducing energy usage while increasing network lifespan. The protocol significantly reduces end-to-end delay and improves packet delivery ratio, addressing the critical issue of controlling packet overhead in existing active routing protocols that often lead to link failures, reduced network bandwidth, and lower Quality of Service (QoS). After the CBS-OLSR model has been integrated with the improved deep Q-learning algorithm, the study demonstrates a substantial enhancement in network performance metrics, including throughput, network lifetime, overhead, and energy consumption, as compared to existing methods. The CBS optimization mitigates the issue of premature convergence by incorporating chaotic functions, which diversify the bat swarm and reduce complexity. Through simulations, the proposed CBS-OLSR protocol shows significant improvements over traditional protocols, offering a more efficient, robust, and sustainable solution for MANETs. [PERSON] et al. [44] proposed an optimized approach for device-to-device (D2D) communication to enhance the effectiveness of relay and routing techniques, particularly in Off-Load Traffic scenarios. The paper focuses on addressing the frequent power failures and disconnections in multipoint relay (MPR) nodes within the OLSRv2 protocol, which often results from the selection of unsuitable MPR nodes. To mitigate this issue, a Linear Programming (LP) technique is employed to determine the optimal weight distribution among four key parameters: battery level, mobility speed, node degree, and connection to the base station. These parameters are crucial in maintaining robust D2D connectivity, and the proposed model ensures that each parameter contributes fairly to the relay selection process.