## 1 Introduction  
Nowadays, the field of analysis using not only numerical data but also image, video and audio data [1, 2] is expanding rapidly due to the development of the IoT and the use of numerous smart/intelligent devices. As data types diversify, the amount of data and operations grow exponentially. In terms of hardware offloading the vast amounts of data and computing tasks into the cloud is efficient to work but it creates problems such as network delays, increased energy consumption and so on [3, 4, 5, 6, 7]. Much research has been done to address these issues [8, 9, 10, 11, 12]. Matters to consider within many papers are response time requirement, battery life constraint, bandwidth cost-saving as well as data safety and privacy [13, 14]. Recent researches have focused on saving energy and minimizing computation through data processing, traffic management, job placement and resource allocation. Ref. [15, 16] leverage the edge cloud to overcome the limitations of the limited computing capacity of mobile devices and to avoid high latency due to offloading operations. Ref. [17] uses fog computing to minimize data processing time and latency through data-intensive computing offload. Ref. [18] usestask-intensive offload to limit battery life to minimize average work time. Ref. [19] is also a study of edge computing with a focus on computational offloading. The edge computing performs some or all of the data and tasks at the edge node [20; 21; 22] without transferring all data generated at the end node to the cloud to optimize energy consumption. Processing bigdata and tasks at the edge node without going to the cloud can avoid long delays. As described above, many types of research on edge computing have proposed efficient strategies through offloading between devices, edge nodes, and the cloud. With the recent growth of intelligent and mobile devices combined with technologies such as IoT and Augmented Reality(AR), the focus has shifted to getting real-time responses with situational awareness. Edge computing makes processing faster than cloud computing when the field of real-time responses is increasing and requires immediate analysis [23]. However, the biggest problem with edge computing is the overload of edge nodes. Failure problems such as discharges of electricity can result in significant losses, which are more severe than the issue of delay. In this paper, the offloading based on edge computing divide main functions into tasks that require data storage and computation. The method of related works described above only considers data or task offloading to save energy and reduce latency. As data grows, the compute volume increases at the edge node, and tasks of offloaded to the cloud due to limited computing capacity. This solves the computational complexity but once again fails to respond in real-time due to latency and bandwidth issues. If you only consider one of the data and tasks, this problem will continue to repeat. Therefore, two functions result in congestion (overload problems due to centralized offload) as well as network and speed delays. These problems are especially important in areas where an immediate response is needed to detect nearby objects, collect real-time data, and speed up complex calculations [24; 25]. To identify abnormal situations and to take quick warnings and actions, a hybrid approach is needed that analyzes multiple volumes of sensor data in real-time [8]. However, if the edge node is overloaded, the performance of the function is delayed, which makes it difficult to guarantee safety. Existing methods are very inefficient for a fast way to deal with emergencies. For example, if a fire detected in a mountain or forest, it must be quickly suppressed to prevent forest fires, and in the event of a fire, the propagation path must track to avoid further loss. In recent years, the seriousness of wildfires is rapidly increasing. Currently, much of the damages caused by forest fires are extensive in many countries including Korea [26].