## 5 Discussion  
We will summarize every related research using two perspectives: scheduling to minimize the delay of offloading and scheduling to guarantee the deadline satisfaction of services in an IoT environment. In [14], the authors developed a task-scheduling policy based on network size and user node mobility to minimize delay in a fog network environment. They considered both a stationary network scenario with a fixed average delay and a non-stationary scenario with frequent changes in average delay based on the rapid movement of users. On the other hand, the authors of [15] proposed an optimization problem solution to minimize the delay when multiple users offload tasks to multiple edge cloud nodes performing complex IoT event processing, such as speech and face recognition. The author of [16] researched a voluntary-node-based task scheduling technique that autonomously determines which resources to allocate to user nodes according to their current resource allowance. This method can effectively increase idle resource utilization. In [17], the authors developed online algorithms for dynamic service caching at the edge computing environment. This technique quickly handled the load of unknown patterns and minimized the cost of operating the edge cloud server, including the cost of forwarding requests to the central cloud server and downloading the new service. The author of [18] proposed a dispatching method that determines which job is offloaded from the user device, and a scheduling method that determines which task is processed first on the edge and central cloud servers to minimize the average response time. In particular, latency sensitivity was assigned a larger weight to give a task higher priority. However, these studies focused only on reducing the offloading delay or the total response time of a system, and the problem of satisfying the deadlines of each task was not considered. In this paper, we considered both satisfying deadlines and minimizing the delay in IoT environments. None of the studies mentioned above considered the deadline satisfaction objectives in the context of task scheduling. In [19], the authors proposed a multi-decision mobile computation offloading scheme that can minimize energy consumption while guaranteeing deadline satisfaction. The authors of [20] investigated a resource provisioning problem that can optimize the cost of a system while ensuring the deadline satisfaction of IoT service. The authors of [21] designed a deadline-aware rate allocation scheme that guarantees the deadline satisfaction of services by assigning higher priority to IoT services with deadline constraints when allocating network bandwidth at the data center. Various other studies have considered deadline satisfaction, but our goal was to solve multi-purpose problems, such as  
Figure 9: Performance comparison of processing time according to the different application types. energy and cost minimization or fair resource allocation, while satisfying deadlines. Based on our goal of minimizing both latencies during the offloading and task execution time, as well as satisfying task deadlines, we have developed a method that guarantees faster task processing performance than previous methods. In addition, the authors of [22] developed a resource allocation strategy for performing user request approval, scheduling, and placement of service virtual machines to maximize user QoS (quality of service). In particular, they used the remaining deadline information to schedule edge server selection and used the LRU (Least Recently Used) policy, assuming that urgent services were not necessarily popular services. However, during the experiment, they ignored the size and transmission delay of the data. In the MEC environment, data size and transmission delay are more critical than propagation delays of links and queuing delays of user requests. In this paper, we divided the network into WLAN, MAN, and WAN by type, and we calculated the delays for each scenario.