Depending on how wide the monitored area is, four or more layers can be used. The first layer represents the cloud and has the purpose of offering a global perspective of the entire system. In this layer, hard computational analysis is performed to prevent and respond to citywide disasters. Since the layer performs analysis on historical data, it does not require real-time or near real-time responses. Following, the second layer represents fog devices that are responsible to prevent any failure, at a smaller scale than cloud, in every neighborhood. The key purpose behind this layer is to identify potential hazardous events, based on the collected measurements from multiple devices. In this situation, near real-time responses are required for better prediction. Next, the third layer is composed of local fog nodes that identify potential threats based on the data received from the sensors and act by outputting control signals when it is necessary for fast recovery from a found threat. Furthermore, these devices process the raw data and prepare it for the layer above. Finally, the last layer is represented by sensors that generate measurements for the aforementioned layers in the architecture.
Figure 2.8 Smart pipeline monitoring system architecture.
2.5 Future Challenges
Fog and edge computing vision introduce multiple advantages by migrating some computational resources at the edge of the network. The underlining of these paradigms is to create an IoT network environment covered with a vast amount of interconnected distributed heterogeneous devices having the purpose to deploy and manage demanding applications closer to the user. Yet, it is a nontrivial task to design platforms where all these required characteristics are met.
In this section, we identify and discuss the challenges that these paradigms must conquer in order to fulfill their full potential. We group these challenges in three main areas, i.e. resource management, security, and privacy, and network management.
2.5.1 Resource Management
Moving computational resources from the cloud closer to the end nodes stand in the center of the fog and edge paradigm. Therefore, novel resource management to fully utilize the available resources and process applications in close proximity of the user is imperative to the successful adoption of these systems. Since IoT devices are resource-constrained devices, applying resource management techniques at the edge will allow edge nodes to optimize their resource utilization (e.g. energy-aware smart devices that increase their battery levels by of loading computation to other nearby nodes), improve data privacy, and enable devices to collaborate and share resources to process IoT applications.
A taxonomy of resource management at the edge, based on the current state-of-the-art research in this area, is presented in [28]. According to this classification, a total of five different categories are identified considering the objective of the technique.
The first category refers to resource estimation and represents one of the fundamental requirements in resource management, i.e. the capability of estimating how many resources a certain task requires. This is important for handling the uncertainties found in an IoT network and providing at the same time a satisfactory QoS for deployed IoT applications. The second category is represented by resource discovery and aims to aid the user to discover available resources already deployed at the edge. Resource discovery complements resource estimation by keeping the pool of available computational resources updated.
Once the system can estimate and discover resources, a third category appears having the purpose of allocating IoT applications in close proximity to the users. This technique, called resource allocation, utilizes the knowledge of available resources to map parts of the applications at different edge devices such that its requirements are met. There are two different perspectives of the allocation: (1) it represents the initial deployment to the edge of the network, deciding where to map the application; and (2) it serves as a migration technique by self-adapting when a node has failed. Moreover, one challenge arises when sharing resources between distributed edge devices, i.e. a close collaboration between nodes enforced by security and privacy is required. Solving this challenge creates the fourth category, i.e. resource sharing.
Finally, the last technique is called resource optimization and is obtained by combining the aforementioned resource management approaches. The main objective is to optimize the usage of available resources at the edge according to the IoT application constraints. Usually, the developer creates the QoS requirement of his application before deploying it to the edge.
2.5.2 Security and Privacy
Adopting the vision of fog and edge computing, more applications that today reside in the cloud are moved to the edge of the network. By deploying and connecting IoT devices, we can transform our homes in a more digitalized environment that adapts automatically, based on our behavior. However, with such benefits arise a set of privacy and security issues that we must address. For example, one can easily study the behavior of a family by simply accessing the generated data from sensors deployed in the house. Hence, ensuring data privacy and security remains a crucial factor in the evolution of edge and fog paradigms.
To evaluate the security and privacy enforced in systems based on fog and edge devices, the designer can use the confidentiality, integrity, and availability (CIA) triad model, representing the most critical characteristics of a system [29]. While any breach of the confidentiality and integrity components yields a data privacy issue, the availability component refers to the property of the nodes to share their resources when required. Since fog and edge represents an extension of the cloud, such systems inherit not only the computational resources but also the security and privacy challenges. Besides these challenges, due to the deployment of devices at the edge of the network more security challenges appear. Yi et al. identify the most important security issues of fog computing as authentication, access control, intrusion attack, and privacy [9].
Considering the dynamic structure of an IoT network, authentication is an important key feature of fog and edge computing and was identified. as the main security issue in fog computing [20]. The authentication serves as the connectivity mechanism that allows to securely accept new nodes into the IoT network. By providing means to identify each device and establish its credentials, a trust is created between the new added node and network. The current security solutions proposed for cloud computing may have to be updated for fog/edge computing to account for threats that do not exist in its controlled environment [21]. One solution to securely authenticate edge devices is presented in [30].
A comprehensive study of security threats for edge paradigms (i.e. fog and edge computing, and MEC, among others) was presented in [31], where the importance of security is motivated for the overall system and each individual component. An edge ecosystem consists of different edge nodes and communication components, ranging from wireless to sensors and Internet-connected mobile devices, distributed in a multilayer fog architecture. While each individual component has its own security issues, new different security challenges appear by combining and creating an edge ecosystem. By reviewing the scope and nature of potential security attacks, the authors propose a threat model that analyzes possible security risks (see Table 2.1).
For this model, the authors in [31] discover all important components of edge paradigms and describe all attacks that can occur against them. As depicted from Table 2.1., we can observe that five different targets i.e. network infrastructure, service infrastructure composed of edge data
