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 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.
2.3.1 Fog Computing Architecture
Fog computing architecture is composed of highly dispersed heterogeneous devices with the intent of enabling deployment of IoT applications that require storage, computation, and networking resources distributed at different geographical locations [21]. Multiple high-level fog architectures have been proposed in the literature [22–24] that describe a three-layer architecture containing (1) the smart devices and sensor layer which collects data and send it forward to layer two for further processing, (2) the fog layer applies computational resources to analyze the received data and prepares it for the cloud, and (3) the cloud layer, which performs high intensive analysis tasks.
Bonomi et al. [10] present a fog software architecture (see Figure 2.4) consisting of the following key objectives:
Heterogeneous physical resources. Fog nodes are heterogeneous devices deployed on different components, such as edge routers, access points, and high-end servers. Each component has a different set of characteristics (i.e. RAM and storage) that enables a new set of functionalities. This platform can run on multiple OSes and software applications, deriving a wide range of hardware and software capabilities.
Fog abstraction layer. The fog abstraction layer consists of multiple generic application programming interfaces (APIs) enabling monitoring and controlling available physical resources like CPU, memory, energy, and network. This layer has the role of making accessible the uniform and programmable interface for seamless resource management and control. Furthermore, using generic APIs, it supports virtualization by monitoring and managing multiple hypervisors and OSes on a single machine with the purpose of improving resource utilization. Using virtualization enables the possibility of having multitenancy by supporting security,
