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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 them, fog computing is a geographically distributed computing architecture connected to multiple heterogeneous devices at the edge of the network, but at the same time not exclusively seamlessly backed by cloud services. Hence, we envision fog as a bridge between the cloud and the edge of the network that aims to facilitate the deployment of the newly emerging IoT applications (see Figure 2.3).

A fog device is a highly virtualized IoT node that provides computing, storage, and network services between edge devices and cloud [2]. Such characteristics are found in the cloud as well; thus, a fog device can be characterized as a mini-cloud which utilizes its own resources in combination with data collected from the edge devices and the vast computational resources that cloud offers.

Figure 2.3 Fog computing a bridge between cloud and edge [20].

By migrating computational resources closer to the end devices, fog offers the possibility to customers to develop and deploy new latency-sensitive applications directly on devices like routers, switches, small data centers, and access points. Depending on the application, this can impose stringent requirements, which refers to fast response time and predictable latency (i.e. smart connected vehicles, augmented reality), location awareness (e.g., sensor networks to monitor the environment) and large-scale distributed systems (smart traffic light, smart grids). As a result, the cloud cannot fulfill all of these demands by itself.

To fill the technological gap in the current state of the art where the cloud computing paradigm is at the center, the fog collaborates with the cloud to form a more scalable and stable system across all edge devices suitable for IoT applications. From this union, the developer benefits the most since he can decide where is the most beneficial, fog or cloud, to deploy a function of his application. For example, taking advantage of the fog node capabilities, we can process and filter streams of collected data coming from heterogeneous devices, located in different areas, taking real-time decisions and lowering the communication network to the cloud. Thus, fog computing introduces effective ways of overcoming many limitations that cloud is facing [1]. These limitations are:

1 Latency constraints. The fog shares the same fundamental characteristics as the cloud in being able to perform different computational tasks closer to the end-user, making it ideal for latency-sensitive applications for which their requirements are too stringent for deployment in the cloud.

2 Network bandwidth constraints. Since the fog offers the possibility of performing data processing tasks closer to the edge of the network, lowering in the process the amount of raw data sent to the cloud, it is the perfect device to apply data analytics to obtain fast responses and send to the cloud for storage purposes only filtered data.

3 Resource constrained devices. Fog computing can perform computational tasks for constrained edge devices like smartphones and sensors. By offloading parts of the application from such constrained devices to nearby fog nodes, the energy consumption and life-cycle cost are decreased.

4 Increased availability. Another important aspect of fog computing represents the possibility of operating autonomously without reliable network connectivity to the cloud, increasing the availability of an application.

5 Better security and privacy. A fog device can process the private data locally without sending it to the cloud for further processing, ensuring better privacy and offering total control of collected data to the user. Furthermore, such devices can increase security as well, being able to perform a wide range of security functions, manage and update the security credential of constrained devices and monitor the security status of nearby devices.

The previous section introduces the edge computing paradigm as a solution to the cloud computing inefficiency for data processing when the data is produced and consumed at the edge of the network. Fog computing focuses more on the infrastructure side by providing more powerful fog devices (i.e. fog node may be high computation device, access points, etc.) while edge computing focuses more toward the “things” side (i.e. smartphone, smartwatch, gateway, etc.). The key difference between the edge computing and fog computing is where the computation is placed. While fog computing pushes processing into the lowest level of the network, edge computing pushes computation into devices, such as smartphones or devices with computation capabilities.