## 9 Conclusions and Perspectives  
The paper proposes an adaptive beaconing strategy based on a fuzzy logic model to enhance the common geographical GPSR routing protocol, resulting in the \"AFB-GPSR\" protocol. This strategy mitigates uncertainty in information update lists and ensures more reliable data forwarding in high-mobility and rapid MANET topology changes. The fuzzy logic model dynamically generates a beaconing interval based on the variations of three key network parameters: (i) node speed (NS), (ii) one-hop neighbors' density (OHND) and (iii) link reliability (LR). The Mamdani fuzzy inference system is integrated in each mobile node to generate this new optimal beaconing interval time (OBIT) value, taking into account the network status, i.e., the neighbors' status. The performance of the AFB-GPSR routing protocol was assessed through experimental simulations using the OMNeT++ and INET framework in three different environmental scenarios by varying network size, node mobility and transmission power. The findings show that the AFB strategy can reduce the routing cost in terms of transmission delay and control the routing overhead; on the contrary, it increases the network reliability in terms of network throughput and packet delivery. The achieved enhancement of the packet-delivery ratio is up to 14% and 9% compared to the standard GPSR and AODV routing protocols, respectively, on average. The routing cost decreases by nearly 35%. Meanwhile, AODV registered a higher routing cost in terms of E2 ED than AFB-GPSR and the standard GPSR as well. As a result, the proposed AFB strategy can significantly enhance the geographic routing protocol, making a new AFB-GPSR routing protocol a state-of-the-art and effective protocol for ad hoc networks.  
In future work, we are planning to investigate further related aspects as follows: (1) To solve the accuracy of the proposed fuzzy-beaconing approach in this study, a compressed fuzzy logic system or hybrid approaches can be considered to jointly address the effect of extra multiple network parameters such as transmission power, interference, collision rate (contention window), and routing overhead in a MANET environment. (2) AI-based approaches are also suggested to design more efficient adaptive beaconing schemes related to the contention window at the MAC layer. Finally, (3) to prove its effectiveness, the AFB  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Year** & **Routing** & **Network’s Parameters** & **PDR\%** & **E2 ED** & **Throughput** \\ \hline Ref. [39] & 2015 & GPSR-FLDB & \begin{tabular}{c} Data Packet Size 512 B, \\ Node Speed 40 m/s \\ NS2.33 \\ \end{tabular} & \(\sim\)90\% & 600 ms & \(\longrightarrow\) \\ \hline Ref. [40] & 2017 & GPSR + Predict & \begin{tabular}{c} Data Packet Size 64 B, \\ Node Speed 20 m/s \\ NS2.33 \\ \end{tabular} & \(\sim\)80\% & 20 ms & 24 Kbps \\ \hline Ref. [47] & 2022 & LDAB-GPSR & \
\begin{tabular}{c} N52.33 \\ \end{tabular} & \(\sim\)90\% & 17 ms & 30 Kbps \\ \hline Proposed Protocol & 2023 & AFB-GPSR &
\begin{tabular}{c} Data Packet Size 512 B, \\ Node Speed 20 m/s \\ OMNeT++ \\ \end{tabular} & 80\% & 270 ms & 11 Kbps \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparative performance with recent protocols for a density of 50 nodes.  
strategy is eligible to be adopted in various network environments such as VANET or FANET according to the network characteristics.  
Conceptualization, G.A.A.-S.; Methodology, all authors; Data curation, R.I.A.-E.; Formal analysis, all authors; Funding acquisition, G.A.A.-S.; Investigation, all authors.; Project administration, G.A.A.-S.; Resources, G.A.-A.-S. and R.I.A.-E.; Software, R.I.A.-E.; Supervision, G.A.A.-S.; Validation, G.A.A.-S.; Visualization, G.A.A.-S.; Writing--original draft, all authors; Writing--review and editing, all authors. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The authors declare no conflict of interest.