# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 2 Literature Review
### Related Works  
A framework combining ECC was created in 2020 by [PERSON] et al. [1]. First, Wireless Power Transfer (WPT) in the downlink is used by the base station (BS) to simultaneously charge a large number of IoT devices. IoT devices use the energy they have captured to carry out processing and communique in uplink at a similar frequency. They proposed a hybrid BF design method for the BS and IoT to enhance the overall efficiency of Energy, Computation and Communication (ECC). At last, the results of the simulation confirmed the success of the suggested algorithm in 6G IoT. Energy, communication and computation models have been integrated into a framework, where the error model remains linear. The IoT applications are accommodated with nonlinear data and transmission rate and so a linear error model cannot determine the precise data-communication framework. To enhance precise data communication and the respective energy consumption, variance of information has to be incorporated into the model. A process-oriented approach to developing communications and Mobile-Edge-Computing (MEC) networks in a time-division way was put forward by [PERSON] et al. [2] in 2022. In this approach, a non-convex latency-reduction issue is developed and large-scale Channel State Information (CSI) is employed to differentiate the complicated propagation surroundings at a reasonable cost. The estimated problem is then given, and it can be divided into smaller problems. Then, sequentially, these problems are resolved. The simulation outcomes proved the suggested process-oriented scheme's advantage over competing methods. The researchers adopted successive convex optimization to handle the latency issues in mobile-edge-computing environments. The successive convex optimization uses an updating model based on weighted error, which does not consider a nonlinear environment when the mobile users are increasing exponentially. [PERSON] et al. [3] detected malicious activity on Software-Defined-Network (SDN) switches in 2021 that resulted in network disruption. The suggested method relies on signatures and can identify malfunctioning switches that discard and switch packets because of their malicious intent rather than a broken link. Every assault has a signature, and these attacks may be recognized by their distinct behaviors and predetermined signatures. The demonstration of anomaly detection and an assessment of the network's performance were carried out. The experimental findings highlight the recognition method that can identify assaults, accomplish a high detection rate with a low FPR and outline potential future work, as well as the efficacy of the suggested work. Even though anomaly detection in 6G IoT has been effectively handled, communication and computation overheads are considered to a limited extent. The usage anomaly detection increases the communication-link establishments and computational burden, though it is unavoidable. Based on the distribution efficiency of existing agricultural-goods logistics distribution methods, [PERSON] and [PERSON] [4] improved and optimized the logistics mode in 2021. Through the use of Genetic Algorithms (GAs) and MATLAB, the logistics cost and client satisfaction are evaluated and assessed under the logistics mode. GA computation shows that the logistics of agricultural goods with the best common distribution method have a big impact on things like oil consumption and damage costs. Similar to [3], the researchers adopted the 6G IoT for e-commerce applications for which vehicle routing was carried out with the aid of data-mining algorithms. This is because of the efficient communication-link establishments between the users. However, the genetic algorithm was adopted for selecting the user path, which requires advanced optimization algorithms to handle the large scale of users. Since the number of users is increasing, the usage of small-scale algorithms remains a bottleneck in handling the large solution space.