Autonomous Network Optimization and Dynamic Channel Allocation for Cognitive Radio-Based Consumer IoT

Abstract

The heterogeneous environment of next-generation Consumer Internet of Things (CIoT) demands efficient resource utilization and reliable network services. On the contrary, the proliferation in the diverse nature of smart consumer IoT devices is causing spectrum scarcity and uneven utilization of available resources. Cognitive Radios (CRs) provide the most suitable solution for spectrum scarcity through dynamic spectrum access. To achieve spectral efficiency and provide consumer-centric network services we propose a novel Cognitive Radio based Autonomous Network Management framework called (CR-ANM). The framework combines the benefits of cognitive radios, Network Function Virtualization (NFV), and Software Defined Networking (SDN), to decouple the control plane from the data plane and is divided into two further operations called Dynamic Priority Determination (DPD) and Efficient Channel Allocation (ECA). DPD is responsible for determining the SU’s priority using a fuzzy logic-based decision controller. Whereas ECA optimizes the channel allocation process and allocates the best available channel to SU. Which increases the channel availability by 77% and reduces the service drop rate by 81.8%. Both schemes run as Virtual Utility Functions (VUFs) on dedicated virtual machines assigned by the SDN controller. This approach increases energy efficiency for low-power consumer IoT devices and improves network reliability.