## 4 Literature Review and Data Extraction
### Distributed Task Allocation
Finally, the authors compared their deployment technique with several other randomly dispersed deployment strategies to demonstrate its advantage. A more systematic analysis was presented by [PERSON] et al. [34] which studied a hierarchical fog-computing CRAN network with three levels of computational services, namely the MEC server (MEC-L) located in radio units, the MEC server (MEC-H) located in distribution units, and cloud computing located in central units. The authors focused on optimizing the received beamforming vectors, task distribution, processing speed, and transmission bandwidth. More specifically, they aimed to reduce the overall latency of the computing tasks, by lowering the integer constraints. In addition, they defined a mixed-integer nonlinear problem (MINLP) and transformed it into a convex problem before applying the Lagrange dual method to solve it. Furthermore, this work [35] offered a working prototype of their multi-layered architecture, which combines fog computing and cloud computing layers to perform latency sensitive analysis. The major objective of this research was to improve the real-time data analysis system based on IoT devices by using this multi-layer fog computing platform. Their study is based on the OpenFog Consortium policy, which offers good results as well as monitoring and data analysis features. Through case studies, this work demonstrated how their suggested prototype architecture performed better in terms of delay time, network utilization, and energy consumption than the cloud-only environment. Previous research made by [PERSON] et al. [36] presented a study on multi-user multi-MEC server scenarios of the MEC environment. In this paper, to balance delay and energy consumption, a penalty mechanism was used, aiming to mimic real-world scenarios. The authors characterized the offloading choice in order to minimize the delay under the restrictions of energy consumption. Furthermore, a detailed examination of the glowworm swarm optimization (GSO)'s computing methodology was provided. Finally, the simulation tests were carried out, and the findings demonstrated that their approach has a higher performance of energy savings and delay reduction in the MEC environment compared to the alternative compute offloading technique by up to 25%. Recently, researchers [65] have also focused on utilizing MEC to effectively address the processing demands of new IoT applications while assuring the security of the sent data. To reduce the overall time delay in computing the executions of intelligent terminals, this research proposes an optimization problem that considers the allocation of computing tasks and the security performance requirements of intelligent devices within the edge computing topology. The authors recommended resource management and genetic algorithms to identify the optimal solutions to the problem. This study also takes security issues into account by assessing the likelihood of a level of security breach by representing the security level of computing data. Furthermore, the authors enhanced IoT applications' efficiency and security when leveraging MEC technologies. In order to confirm the efficiency of the suggested algorithms, the researchers compared them to heuristic algorithms using data testing and simulations. The research offers information on how MEC might improve the productivity and security of IoT applications. [PERSON] et al. [66] proposed a thorough optimization model that was built for the UAV environment. The model aimed to optimize the costs of migration, operation, and QoS, while accounting for the energy constraints and location sensitivity of the nodes. An online method was suggested for this convex optimization task. The proposed method was thoroughly analyzed in terms of competitiveness, and it was shown that it has a parameterized competitive ratio. The results of the simulation indicate that the suggested approach may save 20% of the overall cost compared to previous approaches and has dependable robustness independent of the user and UAV node counts.