Optimizing 6G Wireless Network Security for Effective Communication

1 Introduction

Optimizing a function helps solve challenging problems. Any optimization problem that can't be solved quickly utilizing deterministic approaches is hard. Metaheuristic optimization uses metaheuristics to discover optimum solutions. Meta and heuristic mean “higher level” and “solution” Using complicated approaches to address unsolvable problems. Single- or multi-objective difficulties may arise. Strong objective functions produce the finest outcomes. With one goal, where the particles converge is optimum. 6G internet wants one-microsecond latency. This is 1 millisecond quicker. 6G will improve imaging, presence, and location awareness. WSNs monitor and record environmental variables and other observables. Commercial and home communication using wireless sensor networks (WSN) (Ahmed et al., 2017).

The present work is related to the optimization of 6G wireless network security. Section 1 is introducing the optimization mechanism along with the Meta heuristic approach and nature-inspired approach. PSO, ACO, ABC, BFO, Cuckoo search and firefly algorithm are considered nature-inspired optimization approach. Then 6G wireless network has been illustrated considering its benefits and working. The requirement of a 6G network has been considered for edge computing and IoT. Finally, wireless sensor networks are presented as a hybrid system. Existing research on WSN, 6G networks, and network security is presented in Section 2 (Arora et al., 2019).

Many studies have been conducted on 6G wireless networks and wireless sensors (Kirubasri et al., 2021). Further, other optimization mechanisms have been shown in the literature. This section details the author's contributions to the study of wireless networks and optimization. Some of the studies examined network safety, while others focused on throughput and routing. After considering the contribution of existing research third section is presenting the problem statement. Here are issues related to research work such as the compatibility of optimization algorithms. Moreover, there is the issue of accuracy as well as error. Section 4 considers the process flow of the proposed work. Here the study of the existing optimization mechanism is considered and issues related to previous work have been presented. Then hybrid approach is built considering the objective function and integration of PSO-MVO. Finally, an evaluation of accuracy and performance is made (Anand et al., 2022). Section 5 is focused on the simulation of time consumption and accuracy in the case of the hybrid model. Section 6 is presenting the conclusion of the research work while section 7 presents the future scope of the research work (Behnaam Aazhang et al., 2019).