Proactive Cross-Layer Framework Based on Classification Techniques for Handover Decision on WLAN Environments

• Published in: Electronics (Basel) 2022-03, Vol.11 (5), p.712
• Main Authors: Cervantes-Bazán, Josué Vicente, Cuevas-Rasgado, Alma Delia, Rojas-Cárdenas, Luis Martín, Lazcano-Salas, Saúl, García-Lamont, Farid, Soriano, Luis Arturo, Rubio, José de Jesús, Pacheco, Jaime
• Format: Article
• Language: English
• Subjects: Algorithms; Artificial intelligence; Decision making; Fuzzy logic; Local area networks; Neural networks; Proposals; Telecommunications systems
• ISSN: 2079-9292; 2079-9292
• DOI: 10.3390/electronics11050712

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.