## 3 Intelligent Dynamic Offloading Algorithm
### Scenario of Computation Offloading for Wild Fire  
This section shows an example of a task-centric algorithm using virtual data related to the forest fire. Depending on the performance of the edge node, latency, and energy costs may vary. The example demonstrates task collaboration by further subdividing the five tasks that are broadly divided above. This paper deals with nine types, and the number of tasks varies depending on what you are expecting.  
Table 1 shows the nine tasks and the data used in each. The tasks are listed in the order of analysis and it is assumed to be a uniform the number of data in each task. Edge nodes commonly perform the tasks in Table 1.  
Figure 2 shows re-arranged in order based on an algorithm to find out data relations between tasks. As mentioned above, Task 1 is the most fundamental analysis, so it should be performed on its edge node. Therefore, the more the data is related to task 1, the higher the importance.  
There are a total of four tasks related to Task 1. Fire prediction':  
* Task 9. Fire predictions at landing locations
* Task 2. Calculate spread rate
* Task 7. Prediction of spread area  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Task** & **Data** \\ \hline Task 1. Fire prediction & temperature, humidity, fuel, mountain terrain, etc. \\ Task 2. Calculate spread rate & fuel, mountain terrain, weather, forest phyisogonomy etc. \\ Task 3. Forest fire intensity calculation & calculated diffusion rate \\ Task 4. Calculate flame height & calculated forest fire intensity \\ Task 5. Types of fire forest & temperature, humidity, forest phyisogonomy, wind speed etc. \\ Task 6. Calculate flame distance & wind speed, calculated diffusion rate \\ Task 7. Prediction of spread area & temperature, humidity, mountain terrain, forest phyisogonomy, wind speed etc. \\ Task 8. Calculate the location of the flame landing & wind speed, wind direction etc. \\ Task 9. Fire predictions at landing locations & temperature, humidity, fuel, mountain terrain, etc. \\ \hline \hline \end{tabular}
\end{table}
Table 1: Tasks for fire and spread range prediction, Part of the data used.  
Figure 2: Tasks re-arranged in order based on algorithm.  
Task 5. Types of fire forest  
The next criterion to look at is the relationship of between data; the more relationships between data, the lower the importance. Also, since the same data is assumed to have the same number of data, the smaller the data used, the lower the influence.  
The remaining tasks, listed in order of little relationship between data, are as follows:  
* Task 4. Calculate flame height
* Task 3. Forest fire intensity calculation
* Task 6. Calculate flame distance
* Task 8. Calculate the location of the flame
* Task 9. Fire predictions at landing locations  
Therefore, when an edge node is overloaded, it is delivered to the edge node, which cooperates in the order of Task 4, Task 3, Task 6, Task 8, Task 5, Task 7, Task 2 and Task 9.  
The difference between the proposed algorithm and the existing offloading method in this paper is as follows. The current offloading method offloads some tasks that consume less energy without considering the relationship between data and task. They simply looked at the energy efficiency of the device and offloaded it. However, this method is problematic when applied to forest fires. The importance of task changes depending on the situation and energy consumption also depends on the amount of data. If it merely thinks about energy efficiency and offloads the task, the execution of the important task can be delayed. For example, Task 1 should be monitored at all times to prevent forest fires. However, if a forest fire has already occurred, tasks that prevent spread are more important than Task 1. Task1 covers the most data and is fundamental analysis, so it is good always to do task1 under the edge node's self. However, the importance can vary depending on the situation.