## 4 Literature Review and Data Extraction
### Energy-Efficient Task Allocation
This study also covered issues of highly reliable communication networks, NOMA configurations, and earlier MEC-enabled IoV contributions. A cooperative task offloading solution using the base station in 5G heterogeneous networks was presented in [30]. This study considers both the waiting energy and delay consumption of the offloaded jobs and uses queuing theory to address them. The issue of choosing whether to offload a task or not is transformed into a task assignment probability problem. The MDs-centered minimization of delay and energy consumption was specified as the optimization objective. In addition, the study proposes a method that assigns probabilities to job assignments, resulting in combined optimization of mobile device consumption, while considering varying demands for delay and energy consumption. The proposed method uses a quasi-Newton interior point (TA-QNIP) algorithm to minimize consumption. Additionally, queuing theory is used to account for the waiting energy and delay consumption of offloaded jobs, and the offloading choice issue is converted to a task assignment probability problem. Additionally, the suggested algorithm's complexity was explored, and the convergence performance was confirmed. Previous studies like [43] suggested a creative approach to offload computations in IoT. The goal of this study was to reduce both the energy consumption and time delays commonly associated with standard edge computing offloading strategies. The authors put forth an enhanced method based on particle swarm optimization (PSO) that involves creating a model and optimization objective function focused on communication and personalized load models for multi-user computation tasks. By using a PSO algorithm to determine the optimal solutions for task offloading strategies, they succeed in decreasing the energy utilization during task allocation. According to their simulations, the suggested methodology works better than two other algorithms in terms of average time delay, energy usage across a variety of mobile devices, and data transmission speeds. The outcomes reveal their strategy's capability to diminish the task execution's lag while using less power; hence, it deals effectively with traditional edge computing problems involving high-energy usage or excessive latency duration. Furthermore, they also examine convergence performance while evaluating how crossover plus mutation rates contribute to their suggested technique. A novel UAV-assisted MEC system was presented by [PERSON] et al. [47]. The authors proposed an architecture for providing computing services to users with low latency requirements by maximizing the UAV's flight height and workload allocation ratio. They designed and developed a functional solution for the optimal UAV flying altitude and for the optimal job allocation ratio. The authors demonstrated the accuracy of the research findings, and it was discovered that the user's horizontal distance from the UAV affects the UAV's optimal flying altitude. Additionally, the impact of the transmit power, bandwidth, and CPU frequency on the optimal job allocation ratio was also examined. Previous research in [68] implemented a strategy for dealing with the issue of workload handling. More specifically, in this work, the workloads were assigned throughout the fog in an entirely autonomous manner by putting into use a monitor-analyze-plan-execute over a knowledge base (MAPE-K) control loop was defined in implementation problems. Using a control loop to break the problem down into a number of phases to find the best placements is provided by a central coordinating mechanism. The coordination mechanism was used for registering the on-device agents which carried out monitoring and program execution. In situations where coordination mechanisms were dispersed, device-specific agents were responsible for performing the phases of analysis and planning.