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In the past couple of years, the cloud computing paradigm was at the center of the Internet of Things' (IoT) ever-growing network, where companies can move their control and computing capabilities, and store collected data in a medium with almost unlimited resources [1]. It was and continues to be the best solution to deploy demanding computational applications with the main focus on processing vast amounts of data. Data are generated from geo-distributed IoT devices, such as sensors, smartphones, laptops, and vehicles, just to name a few. However, today this paradigm is facing growing challenges in meeting the demanding constraints of new IoT applications.

With the rapid adoption of IoT devices, new use cases have emerged to improve our daily lives. Some of these new use cases are the smart city, smart home, smart grid, and smart manufacturing with the power of changing industries (i.e. healthcare, oil and gas, automotive, etc.) by improving the working environment and optimizing workflow. Since most of the use cases consist of multiple applications that require fast response time (i.e. real-time or near real-time) and improved privacy, most of the time the cloud fails to fulfill these requirements (i.e. network congestion and ensuring privacy).

To overcome these shortcomings, researchers have proposed two new paradigms, fog computing and edge computing, to enable more computational resources (i.e. storage, networking, and processing) closer to the edge of the network. Fog computing (FC) extends cloud capabilities closer to the end devices, such that a cloud-to-things continuum is obtained that decreases latency and network congestion while enforcing privacy by processing the data near the user [2]. On the same note, the edge computing vision is to migrate some computational resources from the cloud to the heterogeneous devices placed at the edge of the network [3].

Embracing the vision of these paradigms and focusing on the deployment of multiple applications in close proximity of users, researchers have suggested new fog/edge devices. Among these devices, the most notable are mini servers, such as cloudlets [4], portable edge computers [5], and edge-cloud [6], which enable an application to work in harsh environments; mobile edge computing (MEC) [7] and mobile cloud computing [8] improve user experience and enable higher computational applications to be deployed on smartphones by offloading parts of the application on the device locally.

Many surveys are found in the literature that describe each paradigm in detail and its challenges [3, 9, 10]. However, there is no paper that compares the two; most of the time the terms fog and edge are both used to describe 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.