## 4 Implement and Discussion of Intelligent Dynamic Offloading Model
### Proposed Model Processing Overview  
Section 4.3 shows the flow of collaboration when wildfires occur in certain areas and overloads occur. Based on the proposed model, this is a flowchart to collaborate when an edge node is overloaded in a specific area. Figure 5 shows the process of the intelligent offloading model for forest fire prediction and prevention scenarios. The cooperative process in case of overload is as follows:  
1. Sensing data from sensors of EN_1. 2. If overload prediction of edge node#1, the result of analysis by the master node. 3. Find the problem by separating it into data and task. 4. Cooperative request to master node. 5. If problem of data intensive offloading, then use the 'Data centric algorithm'
6. If problem of task intensive offloading, then use the 'Tata centric algorithm'
7. MN checks the possibilities of collaborative predictions by data and tasks from edge node#n. Figure 5: Dynamic offloading model processing overview. If the output is Yes, master node check the IP address of edge node#n. Send IP of EN_n to edge node#1. Data or task transfer to EN_n. If problem of data offloading, then use the 'Data centric algorithm'  
If problem of task offloading, then use the 'Tata centric algorithm'  
Finally, send all results to action node and copy master node to second master node. We describe the process by applying scenarios to the cooperative processes listed from 1 to 8. The scenario is when the temperature rises rapidly, and the wind direction changes frequently. First, EN_1 collects and processes data in real-time. EN_1 collects and processes data in real-time through sensors. Task1 starts to predict the forest fire when an event that rapidly increases in temperature occurs. The assumption is that the initial fire suppression failed. The following tasks performed to predict the diffusion range and speed through data such as wind direction and wind speed. EN_1 rapidly increases the workload. The master node monitors all edge nodes in real-time and predicts the overloaded if the energy of EN_1 increases and exceeds the threshold. EN_1 is the overload, and the EN_1 requests cooperation from the Master Node to find the cause of the overload first. The master node divides into data and tasks to find edge node problems. The two algorithms decide the part to collaborate based on the causes of data collection and processing and task analysis. For example, in the case of a task offload problem, use the 'Task-centric algorithm'. The algorithm rearranges the parts to collaborate as in the example in Section 3.4. The fire prediction task is the most frequently performed task as sensing data is collected. Most fundamentally, to extinguish forest fire through fire prediction will prevent the problem of spreading. The next most important task is to predict the flow of forest fires by using the wind direction, wind speed and temperature in real-time. Therefore, the two tasks are used most frequently and handle the most data, so make sure to analyze them at their edge nodes so that there is no delay. Also, the task of predicting the type of wildfire is the most challenging situation to predict the spread of wildfire. The embers blow in the wind and create a new forest fire. The fireball attached to the upper part of the pine heats the surrounding air, rises above the air. If that meets the strong winds, these embers can fly for 1-2 km. There is the possibility of a considerable risk of fire spreading in a short time and becoming hard to control. To extinguish, it is urgent to analyze the landing area and how quickly it spreads. Therefore, tasks are important in order of forest fire prediction, diffusion prediction, and forest fire type prediction. The importance may vary depending on the situation. Currently, the temperature is rising and the wind speed is rising, so there is already a forest fire. In this situation, the spread prediction and forest fire type prediction becomes more important than the forest fire prediction task in EN1.