## 4 Literature Review and Data Extraction
### Energy-Efficient Task Allocation  
[PERSON] et al. [11] covered a complete solution for wireless-powered MEC systems--which combine MEC with wireless power transfer (WPT). The suggested system was a single-user MEC system that employs energy beamforming for WPT toward the user and has a single multi-antenna energy transmitter. The user uses the captured energy to run computations locally and offloads some of them to a distant MEC server through an access point that is integrated with the server. This study proposes a method to optimize the allocation of transmission energy for WPT and the allocation of local computing and offloading tasks over a certain period while considering the energy and task causality constraints caused by channel fluctuations and dynamic task arrivals. This study provides an offline optimization that, under the assumption of perfect information, uses convex optimization techniques to produce an optimum result that is well structured. When channel and task state information are only causally known, online designs are also created for the combined allocation of energy and tasks. The suggested solutions outperform traditional myopic systems and benchmark schemes in terms of energy usage according to numerical data. An architecture for a secure MEC system was presented by [PERSON] et al. [19] where a multi-antenna access point (AP) contains an MEC server and offers to compute offloading services for several mobile devices. Each MD used a protocol for partial offloading to complete the computations in blocks. The authors provided secured offloading limits that state that each MD's offloading rate cannot exceed its attainable secrecy rate to the AP in order to prevent information loss during offloading to the eavesdropper. Following that, an optimization problem was created to reduce the system's total energy usage, with respect to the restrictions on secure offloading and computation latency. The distribution of computing jobs, offloading power, local CPU frequency, and timeslots were all cooperatively optimized. The initial optimization issue was nonconvex because of the safe offloading requirements. To find the iterative convex approximation of the issue, the authors used the difference of the convex approach. The answer was then derived in a semi-closed form using the Lagrange dual technique. The authors also provided a low-complexity algorithm to further minimize the computational cost. Finally, quantifiable results are provided about how well the suggested strategies performed. It was demonstrated that the suggested strategies greatly lower the MEC systems' energy usage. In particular, the existence of the eavesdropper had no effect on task offloading when the distance between the MDs and the eavesdropper reaches a specific distance, and the energy consumption of the computing jobs remains consistent. As result, a large separation between the mobile devices and the listener could lower the power needed for safe offloading. Recently, a framework for offloading the workloads to optimize energy consumption in the Internet of Vehicles (IoV) was presented by [PERSON] et al. [25]. This work suggested a network architecture supported by UAVs for offloading computationally demanding activities to roadside units (RSU) that incorporate MEC functions. A dual-reconfigurable intelligent surface (RIS) configuration that serves two neighboring network nodes was introduced. Each RIS unit functions as a data offloading aerial relay as well as an aerial-roadside unit (ARSU). To reduce the energy use of the cars and ARSU while taking into account transmit power limits, timeslot scheduling, and job allocation, an optimization technique was provided. In order to confirm the effectiveness of the improved dual-RIS-assisted wireless transmission model, the authors carried out extensive numerical computations. According to the article, RIS units can enhance end-to-end communication during data offloading.