# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 2 Literature Review
### Related Works
A unique hierarchical 6G-IoT system with UAVs in the air and Intelligent Reflecting Surfaces (IRSs) was created in 2021 by [PERSON] et al. [5]. The system uses free-ride BackCom to convey data. IRS boosts the energy via beam forming, increasing BackCom's range and effectiveness. Based on the results of our simulations, it is clear that our method outperforms conventional approaches in terms of overall system performance. The key focus of this research remains on the precise transmission and reception of data, while energy efficiency has also been considered. Since the adopted methodology is for UAV networks, link establishments and computational complexity play key roles. This necessitates joint performance consideration against the existing proposal. [PERSON] and [PERSON] (2021) [6] aimed to incorporate sophisticated technology in the field of drama. A data-mining clustering technique is used to assess theatre language using the IOT AI. Through the use of relevant data-mining techniques and their analysis, the traditional K-means algorithm and the enhanced K-means algorithm were compared. The texts were examined using the Latent Dirichlet Allocation (LDA) topic-clustering model. The findings imply that the enhanced algorithm was more trustworthy and effective in data processing and clustering, which demonstrates the enhancement of the enhanced K-means algorithm. Unlike [3; 4], the usage of 6G IoT for drama language art worked on data-handling issues to reduce the computation overhead. However, energy and communication overheads have not been considered in the clustering model. Moreover, K-means clustering has a drawback in its dependency on initial centroids. The relevant feedback technique was merged with the conventional image-data-mining technique depending upon the KNN algorithm and was then optimized by [PERSON] and [PERSON] [7] in 2021. This would further enhance the conventional systems and increase the intellect of image-data-mining technology. For extracting image shape features, the negative-feedback K-Nearest-Neighbor (K-NN) method is the most accurate one. Thus, the best accuracy for image-feature extraction reached 99.3% when utilizing the improved KNN method. The usage of the KNN algorithm in connection with 6G IoT incurs computational complexity because the unknown input to KNN has to be processed with the knowledge library at every instant. Since the degree of unknown input increases with the number of users, the computing complexity shall increase further. To examine the use of smart technology in the 6G-IoT communique environment, [PERSON] [8] developed a human-behavior-identification technique for wearable devices in 2021. Data-mining technology is deployed to evaluate and process the IoT information, and trials are used to develop the classifier model. Second, an ANN is used to create a model for recognizing human behavior, and the BP technique was used to learn the parameters of the ANN to enhance behavior identification. The classifier and the ANN-based model for recognizing human behavior are then evaluated. The methodology established a robust application of 6G IoT under wearable technology, but the energy constraints, communication and computation overhead remain unaddressed. When wearable technology hasbecome a limelight application of 6G IoT, the trade-off between energy, communication and computation overheads needs to be addressed extensively. [PERSON] et al. [32] suggested an application-centric framework and built a finer-grained offloading scheme based on application partitioning. Here, we depicted application partitioning via directed acyclic graphs (DAGs) and used vehicles and MEC servers as offload nodes. In addition, the offloading scheme took the total execution time and throughput of the application as optimization goals. As bandwidth increases, the partitioning approach can transfer some subtasks to other devices for execution in order to reduce computational overhead.