## 2 Background and Related Works
Accordingly, the neighbor node that reveals the optimal Q-value for a given destination is selected as the next-hop forwarder. Their simulation results demonstrate a significant improvement in routing performance compared to the AODV routing protocol. QGeo [24] proposed a distributed machine-learning-based geographic routing scheme for ad hoc networks to reduce overhead in a high-mobility scenario. The proposed routing scheme considers a model-free reinforcement learning algorithm in which the global network information is not required to make a routing decision. Instead, nodes make geographic routing decisions distributively relying on the exchange of local location information by periodic Hello messages that include location, current Q-value, and link condition. QGeo routing scheme introduces a local maximum avoiding algorithm in the reward function in which the forwarder node gets a negative reward value to prevent detours. Recently, fuzzy logic became popular in the design of routing protocols. Routing schemes that utilized fuzzy logic for Vehicular ad hoc networks (VANETs) have been proposed [25; 26; 27]. [PERSON] et al. [25] proposed a routing protocol (FL-DGR) which utilizes fuzzy logic to combine and find a trade-off between different routing metrics intelligently without deriving a mathematical model. In FL-DGR, the communication between nodes takes place through a unicast transmission in which the forwarder node selects one next-hop candidate only using fuzzy logic evaluation. FL-DGR demonstrates improvement in network connectivity and routing performance in relatively dense urban environments compared to some relevant VANETs routing protocols. [PERSON] et al. [26] proposed a vehicle-to-roadside routing scheme. Their scheme utilizes distributed clustering, where a coalitional game model stimulates the vehicles joining a cluster. At the same time, they utilized a fuzzy logic algorithm to select stable clusters heads where the authors evaluated multiple metrics of mobile vehicles. Their evaluation uses the performance of multi-hop routes between nodes and roadside assistants. The scheme relies on reinforcement learning. With the popularity of global positioning systems (GPS), the geographic location information of the neighbors has been considered as a metric to select and sort the candidate set nodes [28; 29]. Accordingly, depending on the Distance Progress (DP), the forwarder node computes the Euclidean distance between potential candidate nodes and the given destination; the closest node to the geographic location of the destination has higher priority to become the potential next-hop forwarder. Utilizing the DP metric, source nodes can estimate the distance from each potential candidate node to reach the destination. Expected Distance Progress (EDP) [19] improved the performance of DP by including the link delivery probability between the current custodian node and the candidate set nodes. Selecting the closest nodes to the destination is not always the optimal choice, especially when the link delivery probability ratio is low. In such a case, multiple re-transmissions of the same packet will be attempted until it is received by one of the candidate nodes. When the DP metric fails to select reliable candidate nodes, EDP will overcome the DP shortcoming by selecting reliable candidate nodes by taking into consideration the same delivery probability, and that will reduce the number of re-transmissions of a packet significantly. While multiple OR protocols have been proposed, most of these solutions consider a homogeneous network consisting of similar wireless nodes equipped with the same wireless capabilities, moving at the same speeds. In this paper, however, we consider OR in a hybrid scenario, where we leverage, detect, and select nodes equipped with high transmission range wireless antenna, from which they are allowed to construct eventual shortcut paths towards the destination.