# Sustainable Power Consumption for Variance-Based Integration Model in Cellular 6G-IoT System
## 2 Literature Review
### Related Works
Thus, when communication bottlenecks occur, an increase in bandwidth can undoubtedly improve throughput performance. In order to achieve the shortest total execution time in the partitioning approach, most subtasks are allocated to local devices for execution. In this case, as the CCR value increases, this may result in an increase in the total execution time, while the throughput decreases. [PERSON] et al. [33] suggested the MPTO-MT algorithm to address the task-offloading problem at different periods. Specifically, the algorithm first determines whether the task can be offloaded in time at a certain period according to the number of candidates offloading nodes of the task in this period. Secondly, the algorithm selects the offloading node and the task-transmission method. In order to select the appropriate offloading node and transmission method, we introduce the service utility and propose three heuristics of selecting the offloading node. Finally, the algorithm updates the resource information of nodes at the next period based on the utilization and release of node resources at the period, and the service time of the nodes. In order to balance the overall service time and transmission costs, the MPTO-MT algorithm uses WiFi as the transmission method for some tasks. However, the MPTO-MT algorithm has a higher task-completion rate than the RtDS algorithm, which reflects that the MPTO-MT algorithm may have more tasks that are not completed in time. [PERSON] et al. [34] suggested the D2-QDPO3 method, a novel offloading method based on learning for IoT edge systems. The Markov Decision Process (MDP) and Deep Learning (DL) are used in this work to solve the dynamic online/offline IoT-edge offloading scenario. The D2-QDPO3 method employs the IoT object to develop an appropriate privacy-aware offloading strategy at a much quicker speed by utilizing the additional information about the EH process. With the use of edge computing, the D2-QDPO3 technique significantly improves high processing and is handled effectively compared to conventional online DL algorithms. One likely explanation is that the computing jobs becomes harder as the number of cells rises, which puts a greater burden on the controllers. Additionally, the agent wants to offload duties rather than carry them out locally to conserve energy. This increases D2-QDPO3's operational time. [PERSON] et al. [35] suggested a novel low-complexity joint resource algorithm named Joint Energy-Efficient Resource Allocation (JEERA) to maximize the energy efficiency of the radio sub-system. The main computational task was distributed into various subproblems, which were solved optimally by exploiting Lagrangian decomposition and the Kuhn-Munkres (KM) algorithm to enhance system performance and energy efficiency in dynamic large-scale IoT systems. The JEERA algorithm fully utilizes the available resource allocation at a sufficiently low threshold and therefore avoids multi-user interference. The JEERA algorithm achieves better energy-efficiency performance than the baseline algorithm. However, the complexity of the proposed JEERA algorithm used in the second and third iterations is efficiently evaluated and it was demonstrated that the proposed algorithm has polynomial complexity. [PERSON] et al. [36] suggested a novel Non-Orthogonal Multiple-Access-Enabled Mobile Edge Computing (NOMA-enabled MEC) in 6G Communications. We summarized the significant aspects and technologies used by the many publications featured in the survey and gave a comprehensive overview of NOMA-enabled MEC. It achieves superior spectral efficiency by serving multiple users simultaneously with the same frequency resource and mitigating interference through SIC. It increases the number of simultaneously served users, thus supporting massive connectivity. NOMA-based MEC technologies have a lot of benefits, yet they also have many shortcomings, such as short battery life, high energy consumption, unfair workload offloading and resource allocation, restrictive latency constraints and security issues.