TY - JOUR
T1 - Proactive Cross-Layer Framework Based on Classification Techniques for Handover Decision on WLAN Environments
AU - Cervantes-Bazán, Josué Vicente
AU - Cuevas-Rasgado, Alma Delia
AU - Rojas-Cárdenas, Luis Martín
AU - Lazcano-Salas, Saúl
AU - García-Lamont, Farid
AU - Soriano, Luis Arturo
AU - Rubio, José de Jesús
AU - Pacheco, Jaime
N1 - Publisher Copyright:
© 2022 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2022/3/1
Y1 - 2022/3/1
N2 - In recent years, modern technology has been increasing, and this has grown a derivate in big challenges related to the network and application infrastructures. New devices have been providing more high functionalities to users than ever before; however, these devices depend on a high functionality of network in order to ensure a correct functioning ability over applications. This is essential for mobile networking systems to evolve in order to meet the future requirements of capacity, coverage, and data rate. In addition, when a network problem happens, it could be converted into somethingmore disastrous and difficult to solve. A crucial point is the network physical change and the difficulties, such as loss continuity of services and the decision to select the future network to be connected. In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments. The proposed framework considers a decision-making process based on five classifiers and the user’s position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy in predicting the connection of this future network. In this way, an early change of network is obtained without packet and time loss during the network change.
AB - In recent years, modern technology has been increasing, and this has grown a derivate in big challenges related to the network and application infrastructures. New devices have been providing more high functionalities to users than ever before; however, these devices depend on a high functionality of network in order to ensure a correct functioning ability over applications. This is essential for mobile networking systems to evolve in order to meet the future requirements of capacity, coverage, and data rate. In addition, when a network problem happens, it could be converted into somethingmore disastrous and difficult to solve. A crucial point is the network physical change and the difficulties, such as loss continuity of services and the decision to select the future network to be connected. In this article, a new framework is proposed to forecast a future network to be connected through a mobile node in WLAN environments. The proposed framework considers a decision-making process based on five classifiers and the user’s position and acceleration data in order to anticipate the network change, reaching up to 96.75% accuracy in predicting the connection of this future network. In this way, an early change of network is obtained without packet and time loss during the network change.
KW - Cross-layer
KW - Decision tree
KW - Handoff decision
KW - Handover
KW - K-nearest neighbors
KW - Logistic regression
KW - Naive bayes
KW - Support-vector machines
UR - http://www.scopus.com/inward/record.url?scp=85125406496&partnerID=8YFLogxK
U2 - 10.3390/electronics11050712
DO - 10.3390/electronics11050712
M3 - Artículo
AN - SCOPUS:85125406496
SN - 2079-9292
VL - 11
JO - Electronics (Switzerland)
JF - Electronics (Switzerland)
IS - 5
M1 - 712
ER -