## 4 Literature Review and Data Extraction
### Distributed Task Allocation  
Applying edge computing in high-altitude balloons (HABs), as per [PERSON] et al. [18], take into account a HAB network that supports MEC and allows users to periodically request computing tasks with different data volumes. The HABs must dynamically decide on the best job allocation, service sequence, and user association to provide their users with computing services. The objective of this combination of job allocation, service sequence, and user association was to reduce the weighted total of the time and energy used by all users. The authors propose a federated learning (FL) approach that uses support vector machines (SVMs) to predict user association in advance, addressing the optimization challenge in task allocation. By using this approach, the heterogeneous autonomous base stations (HABSs) can collaboratively train an SVM model that can accurately predict the best user association without needing to transmit large amounts of task data or previous user association results. The original, nonconvex issue is split into two sub-optimization problems--the task allocation optimization problem and the service sequence optimization problem--which are then addressed repeatedly based on the expected user association. Specifically, the authors developed a closed-form formula for the ideal service sequence given the job allocation vector. This optimization problem may be converted into a piecewise linear problem that can be addressed using linear programming if the best service sequence is known. Previous research showed [22] the occurrence of a multi-device game that offloads processing for several users. Computing efficiency may be significantly increased by using the various available intelligent devices that industrial vehicles can assign calculation assignments to perform the operation in parallel. Moreover, to determine the optimal offloading strategy, they formulated a sequential game of multi-user computation offloading, treating multiple industrial vehicles as multi-devices for handling multiple targets. They took into account that the system cost is primarily made up from the price of rental IDs, energy consumption, and execution time. To improve the chances of establishing wireless connections between industrial vehicles (IVs) and unmanned aerial vehicles (UAVs), a dynamic scheduling technique based on the density of small partitions was introduced. The current vehicle density in the partition determines the UAV's residence time, which was scheduled by the software-defined network controller. They developed an algorithm for minimum incremental task allocation (MITA), a job distribution mechanism for industrial vehicles to distribute some of the computational duties among several IDs. MITA was determined as the best multi-target task allocation strategy and reduces the system cost associated with task execution. In order to reduce the user response time, balancing the load, and maximizing server resource usage, [PERSON] et al. [24] developed a task offloading technique based on a multi-agent approach. Additionally, the authors addressed different problems that centralized scheduling would create and also presented the benefits of centralized training and dispersed operation. The authors used an experimental approach to confirm the viability and efficacy of their distributed task offloading algorithm based on multi-agent and load balancing (DTOMALB) method and the analysis of the simulation results. This paper [29] addressed the problem in MEC of indivisible task allocation with heterogeneous resources (TAHRC). In a scenario with diverse resources, the authors provided an accurate mathematical method for indivisible task allocation. Furthermore, an effective task allocation that takes into account various types of resources, as well as binary computation offloading, was presented. The authors defined TAHRC as an integerprogramming problem. The TAHRC was divided into two subproblems: the resource allocation and offloading choice.