# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 1 Introduction  
Recent advancements in smart device technology and wireless communication have encouraged the proliferation of IoT with ubiquitous computing and sensing abilities to connect millions of objects to the Internet [1, 2, 3]. The IoT is becoming a crucial component of the future Internet and is receiving a lot of interest from both academics and businesses owing to its enormous potential to provide consumer services in all facets of contemporary life [4, 5, 6]. Enterprise technology supported by 6G will change how businesses handle information, communicate, make decisions and train staff. This new technology will lead to fascinating and novel use cases, significant social changes, and new difficulties. Through fully automated and intelligent remote managing systems, the IoT facilitates easy interactions and automated management among heterogeneous devices without the need for human intervention. This has the power to transform industries and benefit society significantly [7, 8, 9]. Mobile applications from the first to the fifth generation have already been suggested and commercially implemented as an enabler for enabling IoT networks and apps [10, 11, 12]. However, 5G cannot fully fulfill the evolving technological requirements, such as independent, highly dynamic, ultra-wide scale and fully intellectualservices, due to the unknown number of connected devices and the quick growth of IoT networks [13; 14; 15]. Additionally, while 5G networks have the ability to support a variety of IoE-based services, they are unable to fully satisfy the needs of emerging smart applications. To overcome the significant shortcomings in the current 5G networks, there is a growing demand for imagining the 6G wireless communication technologies. Additionally, the inclusion of AI in 6G will offer solutions for extremely difficult issues pertaining to network optimization. Additionally, researchers are looking into new technologies like THz and quantum communications to enhance the future 6G networks. The potential of 5G wireless systems is anticipated to be exceeded by the rapid rise of automatic IoT networks [16; 17; 18]. Additionally, new IoT applications and services like flying cars and remote robotic surgery require improvements to the present 5G systems to enhance the quality of IoT delivering services and business operations [19; 20; 21]. Research on 6G wireless networks and the associated technical advances has lately received significant attention from both academia and industry, helping the advancement of the IoT and beyond [22; 23; 24]. Because of its improved features over the prior network decades, like ultra-low latency communication, ultrahigh throughput, satellite-based client services and massive and automatic networks, 6G is anticipated to offer a completely new service quality and improve users' experiences in current IoT systems [25; 26; 27]. In the areas of IoT data detection, device connectivity, wireless connectivity and 6G network, these levels of capability will be exceptional and will hasten the appliances and deployment of a 6G-oriented IoT network. Due to the enormous potential of 6G IoT, a lot of work has been put into this potential field of study [9] concerning clustering techniques [6] and optimization strategy [4] to enhance the overall network features, including coverage, capacity and mobility managing; however, the models need more improvisation towards the improvement of 6G networks as a major downlink traffic overload will result. The 6G networks are designed to improve performance by offering ultra-low latency (ms) and peak data speeds of roughly 1 Tbps [28; 29]. Additionally, by using THz frequencies and spatial multiplexing, the 6G networks are designed to increase bandwidth by 1000 times over the 5G networks. Through the efficient implementation of underwater and satellite communication networks, the 6G networks would also offer worldwide coverage. Furthermore, the IoT device observes the subchannel in an opportunistic way while being allocated to one RRU.