With the development of the Internet of Things (IoT), the amount of data is growing and becoming more diverse. There are several problems when transferring data to the cloud, such as limitations on network bandwidth and latency. That has generated considerable interest in the study of edge computing, which processes and analyzes data near the network terminals where data is causing. The edge computing can extract insight data from a large number of data and provide fast essential services through simple analysis. The edge computing has a real-time advantage, but also has disadvantages, such as limited edge node capacity. The edge node for edge computing causes overload and delays in completing the task. In this paper, we proposes an efficient offloading model through collaboration between edge nodes for the prevention of overload and response to potential danger quickly in emergencies. In the proposed offloading model, the functions of edge computing are divided into data-centric and task-centric offloading. The offloading model can reduce the edge node overload based on a centralized, inefficient distribution and trade-off occurring in the edge node. That is the leading cause of edge node overload. So, this paper shows a collaborative offloading model in edge computing that guarantees real-time and prevention overload prevention based on data-centric offloading and task-centric offloading. Also, we present an intelligent offloading model based on several scenarios of forest fire ignition.

With the evolution of the Internet of Things (IoT), edge computing technology is using to process data rapidly increasing from various IoT devices efficiently. Edge computing offloading reduces data processing time and bandwidth usage by processing data in real-time on the device where the data is generating or on a nearby server. Previous studies have proposed offloading between IoT devices through local-edge collaboration from resource-constrained edge servers. However, they did not consider nearby edge servers in the same layer with computing resources. Consequently, quality of service (QoS) degrade due to restricted resources of edge computing and higher execution latency due to congestion. To handle offloaded tasks in a rapidly changing dynamic environment, finding an optimal target server is still challenging. Therefore, a new cooperative offloading method to control edge computing resources is needed to allocate limited resources between distributed edges efficiently. This paper suggests the LODO (linked-object dynamic offloading) algorithm that provides an ideal balance between edges by considering the ready state or running state. LODO algorithm carries out tasks in the list in the order of high correlation between data and tasks through linked objects. Furthermore, dynamic offloading considers the running status of all cooperative terminals and decides to schedule task distribution. That can decrease the average delayed time and average power consumption of terminals. In addition, the resource shortage problem can settle by reducing task processing using its distributions.

The accelerated development of AI technology has brought about revolutionary changes in various fields of society. Recently, it has been emphasized that Fairness, Accountability, Transparency, Ethics (Explainability) (FATE) should be considered to support the reliability and validity of AI-based decision-making. However, in the case of autonomous driving technology, which is directly related to human life and requires real-time adaptation and response to various changes and risks in the real world, environmental adaptability must be considered in a more comprehensive and converged manner. In order to derive definitive evidence for each object in a convergent autonomous driving environment, it is necessary to transparently collect and provide various types of road environment information for driving objects and driving assistance, and to construct driving technology that is adaptable to various situations by considering all uncertainties in the real-time changing driving environment.This allows unbiased and fair results based on flexible contextual understanding, even in situations that do not conform to rules and patterns, by considering the convergent interactions and dynamic situations of various objects that are possible in a real-time road environment. The transparent, environmentally adaptive, and fairness-based outcomes provide the basis for the decision-making process and support clear interpretation and explainability of decisions. All of these processes enable autonomous vehicles to draw reliable conclusions and take responsibility for their decisions in autonomous driving situations. Therefore, this paper proposes an Adaptability, Fairness, Transparency, Explainability and Accountability (AFTEA) Framework to build a stable and reliable autonomous driving environment in dynamic situations. This paper explains the definition, role, and necessity of AFTEA in artificial intelligence technology and highlights its value when applied and integrated into autonomous driving technology. The AFTEA framework with environmental adaptability will support the establishment of a sustainable autonomous driving environment in dynamic environments, and aims to provide a direction for establishing a stable and reliable AI system that adapts to various real-world scenarios.

Autonomous vehicles make decisions and controls based on various object recognition results. The driving environment is characterized by the coexistence of a multitude of objects of varying shapes and sizes. Therefore, the ability to accurately recognise fine-grained objects is essential for accurate object recognition in a variety of changing situations. For object detection, the autonomous vehicle performs bounding box and segmentation to provide the detected object information. However, bounding box and segmentation based object detection has difficulties in identifying objects with complex shapes, small or distant objects, and it is hard to distinguish and detect objects with similar colors to the background or similar colors and textures to surrounding objects. This has limitations for reliable object identification in autonomous driving environments containing a variety of objects, which is a challenge for clear criteria-based object avoidance and collision protection. To overcome these limitations, this paper proposes Edge-Adaptive Depth Estimation(EADE). EADE, the combination of edge extraction and depth estimation, enables detailed edge extraction and partial distance estimation of objects even in environments where object shape and size, surrounding objects, and backgrounds make it difficult to recognise distinct objects, which allows for reliable autonomous decision-making and control based on detailed object collision and avoidance criteria. To validate EADE, experiments were conducted with real-world driving environment image data. The results of EADE demonstrate that detailed object recognition is possible with clear edge recognition and estimation of object distance, even for complex shaped objects such as trees with branches in multiple directions, distant objects, and objects that are difficult to distinguish from the background such as curbs.