## 4 Literature Review and Data Extraction
### QoS Task Allocation  
A recent article [26], provided a summary of the techniques, advantages, and limitations of the G-PATA approach. This methodology is unique in that it simultaneously optimizes conflicting objectives, namely the reduction in energy consumption at the rational edge and the minimization of processing time at the server, while also fulfilling specific privacy requirements. The authors discussed the challenge of task allocation in edge computing systems that take into account privacy concerns for delay-sensitive applications related to social sensing. They also formally outlined the problem's goals after presenting the task model, underlying assumptions, and privacy model they utilized.  
Furthermore, [PERSON] et al. [32] presented edge computing into mobile crowd sensing (MCS) and suggested a three-tier architecture, in which they presented ideas for task distribution and user data input. The edge servers transmitted the job requirements to users during task allocation so that exact personal information is no longer required. Edge servers enable participants to contribute high-quality altered data without compromising their privacy.  
The authors outline the challenge of sensing cost reduction while maintaining the privacy and provide the data sensing mechanism with user privacy preserved (DS-UPP) method as a solution. In DS-UPP, they created a compressive sensing-based method to reduce the quantity of required sensing data and an LDP-based algorithm to safeguard the privacy of the participants. They also examined the DS-UPP that satisfies e-differential privacy. Taking into account the limitations posed by the privacy budget and task recovery error, the authors established both the minimum and maximum number of participants required to mathematically complete the task, along with the expected amount of data that each participant should contribute. Additionally, through the use of a simulator, they conducted in-depth simulations to assess the effectiveness of the DS-UPP. After applying the PrivKV algorithm for comparison, according to the experimental findings, DS-UPP may, on average, cut the cost of sensing by over 90% while still maintaining privacy and data quality standards.  
A method to lower the overall latency on an MEC platform by providing a work allocation mechanism was proposed by [PERSON] et al. [48]. Three different server types were available on this platform: a dedicated cloud server, a MEC server, and a shared MEC server. The authors initially calculated the processing time and transmission delay for different types of servers to determine the time taken for a task to be submitted and a response to be received. They used queuing theory to determine the transmission latency for the shared MEC server, with the bottleneck node being modeled as an M/M/1 queueing model. To minimize the overall latency for all tasks, they formulated an optimization problem for task allocation. Tasks may be properly distributed across the MEC servers and cloud servers by resolving this optimization challenge. Even with the use of a meta-heuristic approach like the genetic algorithm, the computation time is still exceedingly long. As a result, the authors also suggested a heuristic technique to find the roughly ideal answer more quickly. A core algorithm and three supporting algorithms make up this heuristic algorithm's four components. Tasks are split into two groups in this method, and task distribution is carried out for each group. The examination of the performance of their suggested heuristic algorithm in comparison to the results provided by the genetic algorithm and other techniques was provided. The task allocation methods are relatively simple and can be easily implemented on an MEC platform. However, there is a risk of falling into a local minimum when using the suggested heuristic approach. To address this issue, the authors utilized the random search approach (ARSET) and heuristic random optimization (HRO) to avoid getting trapped in local minimums.