## 7 Conclusions  
In conclusion, the proposed E-SA-MMAC and Max-E-SA-MMAC protocols achieve greater throughput than the other MMAC protocols at the expense of fairness while the  
Figure 11: Quantile–quantile plot of (**a**) throughput, (**b**) JFI, and (**c**) channel access delay.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Component** & **Sum of Squares** & **\% Variation** &
\begin{tabular}{c} **Degree of** \\ **Freedom** \\ \end{tabular} & **Mean Square** \\ \hline \(y\) & 904,502.3 & & 60 & \\ \(\hat{y}\)... & 550,405.2 & & 1 & \\ \(y-\hat{y}\)... & 354,097 & 100\% & 59 & \\
**Main Effects** & 302,778.4 & 85.50\% & 9 & 33,642 \\ No. of Stations & 197,759.6 & 55.85\% & 3 & \\ MMAC Protocols & 4207.245 & 1.19\% & 4 & \\ No. of Channels & 100,811.6 & 28.47\% & 2 & \\
**First Order** & 50,000.7 & 14.10\% & 26 & 1923.1 \\
**Interactions** & 860,6436 & & 12 & \\ Stations’Photonics & 47,618.91 & & 6 & \\ Protocols’Channels & 1521.139 & & 8 & \\
**Second Order** & 1317.925 & 0.40\% & 24 & 54.9 \\
**Interactions** & 1317.925 & & 24 & \\ Stats’Proto’Channels & 1317.925 & & 24 & \\ \hline \hline \end{tabular}
\end{table}
Table 8: ANOVA for Channel Access Delay.  
number of channels is low. Max-E-SA-MMAC adapts to the \(CW_{max}\) mode, which provides greater throughput and equal fairness to the previous works. A large number of channels shows a negative effect on the throughput of the protocol due to the congestion at the control channel. Thus, we extended the protocol to adapt to the changing wireless ad hoc network conditions based on the number of channels by using the control channel for data transmission at a low availability of channels and the BiMMAC approach in other situations. The results of this adaptive MMAC demonstrate that this approach achieves the best of both worlds in dynamically changing environments.  
Further, we learned from the analysis of variation that protocols have a much smaller effect on the variation of throughput compared to the number of stations and the total number of channels. The number of channels has the greatest effect on the variation of throughput by a large margin. The ANOVA table for throughput also highlights the effect of the interaction between the number of channels and the number of stations on the variation of throughput. Lastly, MMAC protocols explain the majority of the variation in fairness, while the number of stations contributes to the majority of the channel access delay.  
Future WorkFor future work, the contention window should be adaptable, depending on the network sizes, and considering the mobility of nodes. Moreover, an optimization approach can be used to optimize the value of \(CW_{max}\) used in Max-E-SA-MMAC. Security of the protocol can be tackled by the use of the lightweight cryptographic algorithms at the MAC layer. Furthermore, the simulation environment and subsequent parameters should be adjusted to the modern 1 Gbps bit rate of the channel so further tests could be performed to confirm whether the results stay consistent for this simulation scenario. In addition, this study can be extended to consider the size of the collision domain to measure the fairness among nodes in the network.  
Conceptualization, A.B., S.M.M., A.A., T.R.S. and A.M.; methodology, A.B., S.M.M., A.A., T.R.S. and A.M.; software, A.B., S.M.M. and A.A.; writing--original draft, A.B., S.M.M. and A.A.; writing--review and editing, A.B., S.M.M., A.A., T.R.S. and A.M. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The authors would like to thank the Department of Computer Engineering and the Interdisciplinary Research Center of Smart Mobility and Logistics, King Fahd University of Petroleum and Minerals, for their support of this research.  
The authors declare no conflict of interest.