Performance of Cognitive-LPWAN: Hungarian Spectrum Optimization and Sharing for Hybrid Low Power Wide Area Networks

Artificial Intelligence (AI) is one of the promising cognitive computing technology applied in the various
field of health care, automobile industry and memory application. The proposed work focuses on the
technologies involved in the wireless communication like WIreless Fidelity (Wi-Fi) with low power
technologies likes low power wide area network (LPWA) with the combined action of cognitive radio
network for resource sharing and data sharing designed as the model. The spectrum optimization and
sharing scheme uses Hungarian Algorithm. This algorithm provides the spectrum sharing optimization
along with the QoS guarantee (SSO-QG). The demand for the channels and bit rate manages the
requirement for the heterogeneous Internet of Things (IoT) devices. The proposed work concentrates
on the delay that exists between the communication devices and the energy consumption. The design
of the hybrid network concentrates on the AI-enabled method with Low Power wide Area (LPWA)
network. The proposed network model reduces the traffic control. The network is tested with the
samples input of video, signal and normal web browsing data. The performance metrics analysis
involves the bit error rate (BER), Primary user activity (PU activity) and data rate for the channel
users. The designed frame work is implemented using the MATLAB software and the results are
simulated.