## 7 Conclusions and Future Work  
This section describes the conclusions that have been obtained from this research. With respect to the techniques estimating the next access point (AP) being 92% average accuracy, while the next AP is correct, this implies a seamless handover without data lost. An exponential increase in the acquisition time of an IP address was also observed depending on the signal strength. Therefore, performing a jump with a power greater than \(-40\) db or 60% of quality of link is recommended. As future work, the implementation of this algorithm in a streaming data, or voice data call, to measure the quality of experience will be applied.  
**Author Contributions:** Investigation and formal analysis J.V.C.-B., L.A.S. and L.M.R.-C.; software and validation S.L.-S., F.G.-L. and A.D.C.-R.; writing--original draft, review & editing J.d.J.R. and J.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research received no external funding.  
**Acknowledgments:** Authors appreciate the infrastructure provided by Autonomous University of the State of Mexico (UAEMex), Metropolitan Autonomous University (UAM) and Autonomous University Chapingo (UACh), because this research could not be done without your valuable support. Authorsthankthe Instituto Politecnico Nacional, Secretaria de Investigacion y Posgrado, Comision de Operacion y Fomento de Actividades Academicas, and Consejo Nacional de Ciencia y Tecnologia for their help in this research.  
**Conflicts of Interest:** The authors declare no conflict of interest.  
\begin{table}
\begin{tabular}{p{42.7 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Approach** & **Title** & **Year** & **Classificator** & **Network** & **Features Input** & **Accuracy** \\ \hline \multirow{4}{*}{([PERSON], 2014)} & SVM-based spectrum mobility Prediction scheme in mobile cognitive radio networks & \multirow{4}{*}{2014} & \multirow{4}{*}{SVM} & Mobile cognitive radio networks & \multirow{4}{*}{Velocity, position} & \multirow{4}{*}{95\%} \\  & Modeling and analysis for vertical handoff based on the decision tree in a heterogeneous vehicle network & \multirow{4}{*}{2017} & \multirow{4}{*}{Decision tree (DT)} & \multirow{4}{*}{heterogeneous Network} & Received signal strength (RSS), Bit error rate (VER), Brooking rate (BR) & \\ \hline \multirow{4}{*}{([PERSON], 2019)} & Inter-frequency radio signal quality prediction for handover, evaluated in 3 GPP LTE. & \multirow{4}{*}{2019} & \multirow{4}{*}{Decision tree (DT)} & \multirow{4}{*}{LTE 3 GPP (DRT)} & \multirow{4}{*}{LTE 3 GPP (RSRQ)} & \multirow{4}{*}{90\%} \\  & Predicting strongest cell on secondary carrier using primary carrier data & \multirow{4}{*}{2018} & \multirow{4}{*}{Decision tree (DT)} & \multirow{4}{*}{LTE} & Reference signal received quality (RSRQ) & \\ \hline \multirow{4}{*}{**(Our Approach)**} & **Proactive cross-layer framework based on classification techniques for handover decision WLAN environments** & \multirow{4}{*}{**2022**} & \multirow{4}{*}{**KNN, SVM, DTL, NB y RL WLAN environments**} & \multirow{4}{*}{**WLAN**} & \multirow{4}{*}{**position, acceleration, velocity, Received signal strength (RSS)**} & \multirow{4}{*}{**96\%**} \\ \hline \hline \end{tabular}
\end{table}
Table 5: Comparison of results obtained versus handover decision algorithms.