Читать книгу Fog Computing - Группа авторов - Страница 69

As we explore new IoT applications and use cases, the consideration of proximity between edge nodes and the end-users is becoming increasingly obvious. The physical distance between the edge and the user affects highly end-to-end latency, privacy, network, and availability. Recently, this leads to a new paradigm allowing computation to be performed in close proximity of user and IoT devices (i.e. sensors and actuators). Edge computing [11] is a new paradigm aiming to provide storage and computing resources and act as an additional layer, composed of edge devices, between the end-user IoT device and the cloud layer. In edge computing, we define “edge” as any computing and network resources along the path between the initial source of data and destination storage of data (fog nodes, cloud data centers).

Figure 2.1 Edge computing solution using an IoT and edge devices [12].

Edge computing is ever stronger when converging with IoT, offering novel techniques for IoT systems. Multiple definitions of edge computing are found in the literature; however, the most relevant is presented in [3]. Authors define edge computing as a paradigm that enables technologies allowing computation to be performed at the edge of the network, on downstream data on behalf of cloud services and upstream data on behalf of IoT services. The proposed paradigm is a relatively new concept and due to the same nature, the term “edge computing” in literature may refer to all other architectures, such as MEC, cloudlet computing (CC), or fog computing (FC). However, we vision edge computing as a bridge between IoT things and the nearest physical edge device that aims to facilitate the deployment of the new emerging IoT applications in users' devices, such as mobile devices (see Figure 2.1).

Authors [12] in Figure 2.1, present the main idea of the edge computing paradigm by adding another device in the form of an edge device. Such a device can be referred to as a personal computer, laptop, tablet, smartphone, or another device capable of locally processing the data generated by IoT devices. Furthermore, depending on device capabilities, it may offer different functionalities, such as the capability of storing data for a limited time. In addition, an edge device can react to emergency events by communicating with the IoT devices and can aid other devices like cloudlet, MEC server, and cloud data center, by preprocessing and filtering the raw data generated by the sensors. In such scenarios, the edge computing paradigm offers processing near to the source of data and reduces the amount of transmitted data. Instead of transmitting data to the cloud or fog node, the edge device, as the nearest device to the source of the data, will do computation and response to the user device without moving data to the fog or cloud.

Edge computing is considered a key enabler for scenarios where centralized cloud-based platforms are considered impractical. Processing data near to the logical extremes of a network – at the edge of the network – reduces significantly the latency and bandwidth cost. By shortening the distance that data has to travel, this paradigm could address concerns also in energy consumption, security, and privacy [13]. However, the rapid adoption of IoT devices, resulting in millions of interconnected devices, are challenges that Edge Computing must overcome.