A Quantum Twin Brain Storm Optimization for Fog Computing in 5G

Abstract

This paper presents a novel quantum-paired brainstorm-optimized content-driven network-based fog computing in 5G environment. This pesticide detection application driven resource allocator makes a decision based on the center quality parameters obtained instantaneously from the online calculation application network, which in turn depends on the instantaneous mobile speed interaction with the wireless edge service performance parameters. These parameters of the hit rate, packet loss, round trip time, radio signal strength, bandwidth, and data cost and vehicle speed are typically different from location to location. We use a quantum state brain storm optimization to guide the fundamental resource allocator for mode selection within 5G fog networks that are mixed with the normal and super speed optimization of the fog computing in 5G paradise. Simulations are conducted for the proof of the concept.