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the same IoT network. Generally speaking, the visions of the two paradigms overlap, aiming to make available more computational resources at the edge of the network. Hence, the most significant difference is given by the naming convention used to describe them. The aim of this chapter is to offer a detailed description of the two aforementioned paradigms, discussing their differences and similarities. Furthermore, we discuss their future challenges and argue if the different naming convention is still required.
The remainder of the chapter is structured as follows: Section 2.2 defines the edge computing paradigm by describing its architectural features. Next, Section 2.3 presents in detail the fog computing paradigm and describes two use cases by emphasizing the key features of this architecture. Section 2.4 describes several illustrative use cases for both edge and fog computing. Section 2.5 discusses the challenges that these paradigms must conquer to be fully adopted in our society. Finally, Section 2.6 presents our final remarks on the comparison between fog and edge computing.
2.2 Edge Computing
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.
2.2.1 Edge Computing Architecture
The details of edge computing architecture could be different in different contexts. For example, Chen et al. [14] propose an edge computing architecture for IoT-based manufacturing. The role of edge computing is analyzed with respect to edge equipment, network communication, information fusion, and cooperative mechanism with cloud computing. Zhang et al. [15] proposes the edge-based architecture and implementation for a smart home. The system consists of three distinct layers: the sensor layer, the edge layer, and the cloud layer.
Generally speaking, Wei Yu et al. divides the structure of edge computing into three aspects: the front-end, near-end, and far-end (see Figure 2.2). A detailed description is given below:
The front end consists of heterogeneous end devices (e.g. smartphones, sensors, actuators), which are deployed at the front end of the edge computing structure. This layer provides real-time responsiveness and local authority for the end-users. Nevertheless, the front-end environment provides more interaction by keeping the heaviest traffic and processing closest to the end-user devices. However, due to the limited resource capabilities provided by end devices, it is clear that not all requirements can be met by this layer. Thus, in such situations, the end devices must forward the resource requirements to the more powerful devices, such as fog node or cloud computing data centers.
The near end will support most of the traffic flows in the networks. This layer provides more powerful devices, which means that most of the data processing and storage will be migrated to the near-end environment. Additionally, many tasks like caching, device management, and privacy protection can be deployed at this layer. By increasing the distance between the source of data and its processing destination (e.g. fog node) it also increases the latency due to the round-trip journey. However, the latency is very low since the devices are one hop away from the source where the data is produced and consumed.Figure 2.2 An overview of edge computing architecture [16]. (See color plate section for the color representation of this figure)
The far end environment is cloud servers that are deployed farther away from the end devices. This layer provides devices with high processing capabilities and more data storage. Additionally, it can be configured to provide levels of performance, security, and control for both users and service providers. Nevertheless, the far-end layer enables any user to access unimaginable computing power where thousands of servers may be orchestrated to focus on the task, such as in [17, 18]. However, one must note that the transmission latency is increased in the networks by increasing the distance that data has to travel.
2.3 Fog Computing
Fog computing is a platform introduced by Cisco [10] with the purpose of extending the cloud capabilities closer to the edge of the network. Multiple definitions of fog computing are found in the literature; however, the most relevant is presented in [19]. According to