# Research and Design of a Decentralized Edge-Computing-Assisted LoRa Gateway
## 2 Related Work
### Edge Computing Network  
To support creative IoT applications for edge devices and allow the success of the EC-assisted IoT paradigm, the academic community and industry have suggested a vast array of EC designs and technologies. In this category are cloudlets (small servers), vehicular (or portable) EC (VEC), and edge cloud. These technologies primarily aid in the deployment of applications in challenging environments with rapid temporal variation. In addition, there are mobile edge computing (MEC) and mobile cloud computing (MCC) technologies that enable the implementation of extensive computing applications on local IoT smart devices. This is achieved by offloading a significant percentage of programs to the devices themselves.  
Due to the diverse nature of the resources accessible to edge nodes, IoT applications confront several obstacles, including the need for flexibility, low latency, high bandwidth, error-handling capabilities, and capacity. Computing at the network's edge offers adaptive resources that allow distributed computing and safeguard data from errors typical of a centralized system. Some research, such as on cloudlets [15], femtoclouds [16], and edge computing, has emphasized the incorporation of mobile device resources. When a device detects edge support, it transfers the majority of its processing to a cloudlet rather than building task-specific components [17]. The concept of cluster computing in femtoclouds necessitates centralized management by expert controllers [18]. By distributing specialized servers to satisfy end users' needs in a particular place, edge computing makes this feasible. The mechanisms for clustering-based methods are detailed in [19; 20; 21].  
The previously mentioned studies have tended toward a centralized strategy for the structure and administration of different resources, such as operating systems and applications [22]. To meet deadlines on time, it is necessary to use a decentralized method. This decentralized strategy delivers resources at the network's edge [23].  
When an IoT application is operating on a collection of edge networks, it is crucial for the edge computing to be dependable and fault-tolerant [24]. Due to the vast range of edge devices, networks, and data processing methodologies, it is a significant challenge to create reliable network services and effective fault-tolerant solutions in edge computing networks. Refs. [25; 26; 27] have conducted research on methods for fault detection and correction in edge nodes.  
As we have seen, many researchers have attempted to deploy distributed computing methods in LoRa gateways and use various techniques to address the edge computing node failure problem, but these methods typically use centralized control for error handling, which often results in significant resource waste and communication delays [26]. In contrast to previous studies, the main significance of this study lies in leveraging LoRa gateways to share the distributed computing load, and in providing an efficient method for accessing and managing large-scale LoRa nodes as distributed edge nodes, which effectively reduces the burden on cloud computing processing and system energy consumption. As a result, this study provides a good edge computing approach for wireless IoT systems that utilize large-scale edge sensing LoRa nodes in applications such as smart forestry and agriculture.