## 6 Conclusions  
In this paper, we propose FQ-AGO, a novel Fuzzy Logic Q-learning based Asymmetric Link Aware and Geographic Opportunistic Routing scheme. FQ-AGO handles asymmetric links that frequently arise in heterogeneous mobile ad hoc networks. FQ-AGO uses a distributed multi-hop ad hoc wireless communication without relying on any network infrastructure units. FQ-AGO utilizes a fuzzy logic approach to evaluate each direct wireless link of its neighbors and employs the Q-learning algorithm to select the most stable and reliable wireless communication link. Following the opportunistic paradigm, the proposed routing scheme novelties are presented concerning the candidate selection algorithm, where the fuzzy logic model captures the following performance metrics: one-hop throughput, expected distance progress, link quality to the potential candidate, and transmission power. FQ-AGO utilizes fuzzy logic to find the device heterogeneity inherent in MANETs, and exploit the presence of asymmetric links in the candidate set to increase the communication reachability substantially. The candidate selection algorithm improves network connectivity and routing performance by leveraging the presence of asymmetric links in the routing paths and their costs, which minimizes the average distance that each data packet must travel before reaching the final destination. The awareness of the presence of asymmetric links and leveraging asymmetric links opportunistically are the main factors in providing a scalable large-scale network, with the absence of network topology information.  
We studied the performance of FQ-AGO via simulation while comparing it with the existing MANETs routing protocols DSDV, AODV, and GOR. For precise simulation analysis, we considered various network metrics to compare the performance of the routing protocols. We performed ns-3 simulation and considered the following performance metrics: hop-count, the available throughput, end-to-end latency, and packet delivery ratio for performance comparison. Simulation results demonstrate that FQ-AGO substantially outperforms the other protocols. FQ-AGO achieves up to 15%, 50%, and 45% higher throughput compared to DSDV, AODV, and GOR, respectively, while reducing by 50% end-to-end latency and the average number of hop-count. The simulation results show that FQ-AGO improves network connectivity and routing performances, particularly in the case of increasing the network size and higher nodes mobility. However, the performance of relevant routing protocols failed to scale and cope whenever the network size increased, or network topology changed.  
Figure 13: Hop-count vs. number of nodes.  
**Author Contributions:** Conceptualization, A.A. and H.H.; methodology, A.A.; software, A.A.; validation, A.A., H.H. and A.-H.A.B.; formal analysis, A.A.; investigation, A.A.; resources, A.A., H.H. and A.-H.A.B.; data curation, A.A.; writing--original draft preparation, A.A.; writing--review and editing, A.-H.A.B.; visualization, A.A.; supervision, A.-H.A.B. and H.H.H.; and project administration, A.-H.A.B. and H.H. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** The authors would like to acknowledge the support from the Klipsch School of ECE.  
**Conflicts of Interest:** The authors declare no conflict of interest.