Chapman Kolmogorov and Deep Recurrent Network Based Seamless Mobility for IOT Data Transmission in Wireless Network

Abstract

Next-generation wireless networks will be designed via all-IP-based network framework that ensures seamless mobility and universal access to Internet via wireless networks. Also over the past few years, wireless network has gained its popularity due to its exorbitant volume and reasonable access cost. Despite its familiarity owing to the constraint in WLAN coverage, handover between nodes may cause elevated amount of handover failures. Seamless mobility is an empirical framework that seizes prevailing circumstances of what user or node is doing, with the objective that the users inclined services are said to be optimized. To develop a smart decision-making mechanism for seamless mobility and reducing energy consumption, deep learning-enabled reconfigurable wireless network solutions are required. In this work, a new method called, Chapman Kolmogorov and Deep Recurrent Network-based (CK-DRN) IoT data transmission in wireless network is introduced to validate handoffs in pragmatic frameworks. First, with the raw data obtained from IoT device network logs, Chapman Kolmogorov Poisson Taylor Optimum Beacon-based Route Discovery algorithm is designed with the objective of ensuring seamless mobility in an energy efficient manner. Second, Deep Recurrent Network-based Data Transmission is proposed that with the aid of concurrent utilization of the numerous paths initiates numerous subflow associations covering disjoint routes and transmits via the subflows, therefore ensuring greater amount of throughput. Simulation of CK-DRN achieves Direction of Arrival (DOA) and ID oriented Socket Layer (IoSL) in terms of delay, energy saving, and throughput.