## 2 Related Work
Additionally, the authors proposed the implementation of a testbed-enabled improved protocol to further evaluate its efficiency, particularly in real-time, multi-hop network scenarios. [PERSON] et al. (2018) work on the intrinsic variability and complexity of multi-access edge computing networks by proposing a policy called the Policy Proximal Optimization (PPO). The proposed policy is based on a device-to-device (D2D)-assisted computation offloading and resource allocation scheme. In addressing the challenge of optimizing the utilization of idle computational resources in multi-access edge computing, the researchers implemented ad hoc wireless technology to authenticate network environment mirroring. They tackled the inherent randomness, dynamics, and complexity by modifying the training features of neural networks to generate a deep Q-network on the user's equipment, aiming to reduce the computation of energy consumption, task execution time, transmission delay, and task count. To achieve this, they formulated a Markov chain decision process designed to minimize energy consumption while reducing time loss, ultimately deriving an optimal offloading strategy. The proposed model focused on achieving these objectives effectively. They did not incorporate an aspect for preserving privacy in the uploading processes of the D2D network. [PERSON] and [PERSON] (2018) propose and evaluate two modifications to the MPR selection strategy within the OLSR protocol, addressing the limitations of the standard algorithm, which focuses on local optimality but neglects global network efficiency. The proposed modifications shift the focus from local properties to global optimization, aiming to reduce the overall number of MPR nodes in the network. The first modification minimizes the number of MPRs selected by a node, which in turn reduces the global MPR count and protocol overhead without introducing any additional transmission or computational complexity. This makes it particularly suitable for mobile networks. The second modification, while introducing minimal overhead, significantly enhances route stability within the OLSR framework. Simulations demonstrate that these heuristics can reduce the global number of MPRs by 8-15% and improve routing stability across various network topologies and mobility scenarios. The results indicate that these simple modifications effectively achieve better global network properties, paving the way for future research into more sophisticated heuristics that could further optimize the logical topology created by OLSR. [PERSON] et al. (2018) addresses the challenge of maintaining Quality of Service (QoS) in Unmanned Aerial Vehicle Ad Hoc Networks (UANETs), where rapid changes in network topology due to UAV movement often lead to increased endwise delay, reduced throughput, and higher packet loss rates. To tackle these issues, the authors propose a Deep Q-Network algorithm which dynamically optimized the link state routing protocol. Their protocol adaptively sends hello messages and topological messages in intervals according to the speed and position of neighboring in real-time, which improves the routing update capability of nodes. By using DQN, the protocol dynamically adjusts the TC message flooding interval, enabling timely routing table updates that ensure correct data packet transmission even as the network topology rapidly changes. The study demonstrates that DQN-OLSR outperforms the traditional protocols such as AODV, GRP, and standard OLSR in terms of end-to-end delay, packet loss rate, and throughput, especially in random waypoint and random walk mobility models. Furthermore, DQN-OLSR shows strong robustness, maintaining low QoS performance degradation even when positioning errors are introduced. The paper suggests that this approach is particularly effective in environments that showcase quick changes in the network topology. Future work will focus on incorporating additional factors like the link load rate of the MAC layer to further enhance the routing strategy and QoS performance.