Neuro-Fuzzy-Based Vertical Handoff Algorithm For Always Best Connectivity

Abstract

In the era of Internet of Everything (IOE), prime concern is to provide flawless connectivity among all connected devices and machines. Internet of Everything (IOE) encompasses not only machine-to-machine communication (M2M) but also people to machine (P2M) and people to people (P2P) communication through technology. But ensuring ‘Always Best Connectivity’ in diverse environments is a challenge. Providing seamless connectivity to provide a strong foundation to IOE is the requirement. Hence, in this paper, a smart handoff decision mechanism is proposed which works in two phases. This paper presents a Neuro-Fuzzy-based vertical handoff mechanism that ensures seamless connectivity by optimizing the network selection process. The proposed approach utilizes Neuro-fuzzy with Multi- Attribute Decision Making (MADM) techniques, specifically VIKOR and Fuzzy VIKOR, to initialize handoff and rank network alternatives. Three network types (WiFi-N1, LTE-N2, and WiFi-N3) are evaluated based on beneficial and non-beneficial parameters for different traffic classes: voice, video, and browsing. The simulation results demonstrate the effectiveness of the proposed approach by comparing handoff blocking probability, ping- pong effect probability, and corner effect probability with existing methods. The results validate the superiority of the Neuro-Fuzzy model in enhancing handoff accuracy and reducing inefficiencies.