## 4 Literature Review and Data Extraction
### Dynamic Task Allocation  
Literature [13] suggested the idea of a \"resource pool\" made up of service vehicles that move together. They suggest a cooperative task scheduling strategy to reduce the task execution time, based on these idle resources that may be allocated in resource pools. They define the min-max issue with the allowable latency constraint for the job execution time optimization model. Additionally, they formulated problems that take node mobility into account. To find the optimum work allocation strategy and reduce the task execution time for the min-max issue, the authors adapted the max-min fairness algorithm and PSO algorithm, respectively, depending on whether to summon all service cars. The simulation results showed the success of the suggested strategies. Another effort made by [PERSON] et al. [20] applied edge computing in the healthcare system. The authors presented a critical healthcare task management (CHTM) paradigm for monitoring electrocardiograms (ECGs), in a fog-cloud computing topology. In more detail, this paper showed how fog computing might improve service delivery and network congestion, as well as the limits of cloud computing in terms of real-time healthcare applications. Fog computing does not have a distributed design, and its nodes are diverse and exclusive in how they share resources and space. In order to manage crucial activities, the suggested CHTM model intends to offer scheduling, resource sharing, interoperability, and dynamic task allocation. The model suggested a multi-agent system to completely control the network from the edge to the cloud, as well as a resource scheduling model for fog nodes. The outcomes of the simulation demonstrated how the suggested approach lowers the network utilization, reaction time, network latency, energy consumption, and instance cost. Finally, the authors discussed potential future study options as well as the comparisons of the suggested model with relevant work. Overall, the CHTM model has met its performance requirements and offers an effective resource scheduling plan for crucial healthcare activities between the fog node layer, edge layer, and cloud. [PERSON] et al. [23] provided a model for a multi-server task allocation method to optimize allocation over various connections. A solution for mobile augmented reality (MAR) and the ways in which mobile devices are limited by the processing and latency requirements of MAR apps was provided. The authors illustrated the durability of the system in situations of network instability using simulations and real-world trials. The authors also go over 802.11 ax and the forthcoming 5G technologies. A task dependency graph, optimization methods for related tasks, and a scheduling algorithm for work distribution over several wireless links to D2D, cloud, and edge servers were presented. The authors' contributions included conducting multiple simulations and implementing a real-world multipath, multi-server mapping application. A recently published paper by [PERSON] et al. [41] addressed the issue of dynamic task allocation and service migration (DTASM) in edge-cloud IoT systems in order to improve the task allocation strategy and reduce the load transferred to the cloud server while fulfilling migration, latency, and computing capacity restrictions. The work suggested a deep reinforcement learning (DRL)-based solution. A cloud server was used to administer the whole system, together with a number of RAN nodes and IoT users running various sorts of apps. Extensive simulations were used to test the suggested technique, and the results demonstrate that it outperforms other benchmarks' task allocation strategies. Investigating the DTASM problem in a heterogeneous edge-cloud IoT system that needs dynamic task allocation and offering a DRL-based solution to the problem of a large discrete action space are two contributions made by this study.