support communication with non-IP based resources, cheap wireless sensors, and controllers deployed in rooms, pipes, and even floor and walls.
Deploying a huge number of things in a smart home environment results in an impressive amount of produced data. One must consider that the data produced has to be transported to the processing units, assuring privacy and providing high availability. Since personal data must be consumed in the home, an architecture based only on the cloud computing paradigm is not suited for a smart home. In contrast, edge computing is perfect for building a smart home where data reside on an edge device running edge operating system (edgeOS). As a result, all deployed edge devices can be connected and managed easily and data can be processed locally by an edge device.
Figure 2.6 shows the structure of a variant of edgeOS in the smart home environment. EdgeOS provides a communication layer that supports multiple communication methods, such as WiFi, Bluetooth, ZigBee, or a cellular network. By using one of the methods, edgeOS collects data from the deployed things and mobile devices. In a smart home, most of the things will send data periodically to the edge device, respectively to the edgeOS. Collected data from different things need to be fused and massaged in the data abstraction layer. It is desirable that human interaction with edge devices is minimized. Hence, the edge device should consume/process all the data and interact with users in a proactive fashion. Additionally, the data abstraction layer will serve as a public interface for all things connected to edge devices where it enables the applicability of operations on the things.
Finally, on top of the data abstraction layer is the service management layer. This layer is responsible to guarantee a reliable system including differentiation (i.e. critical services must have higher priority compared to a normal service), extensibility (i.e. new things can be added dynamically), and isolation (i.e. if something crashes or is not responding, the user should be able to control things without crashing the whole edgeOS).
Figure 2.6 Structure of edgeOS in the smart home environment [3].
2.4.2 Fog Computing Use Cases
The new fog computing provides an improved quality of service (QoS), low latency and ensures that specific latency-sensitive applications meet their requirements. There are many areas like the healthcare, oil and gas, automotive, and gaming industries that can benefit from adopting this new paradigm. For example, by performing predictive maintenance the downtime of manufacturing machines can be reduced, optimizing the workflow in a manufacturing plant, or fog computing can simply monitor the structural integrity of buildings, ensuring the safety of workers and clients. However, by implementing such architecture not only businesses can profit. At the same time, life in the city as we know it today can be improved. Multiple day-to-day activities can be optimized to yield better living comfort. For example, consider the following scenario: we can improve congestion on the highway by using smart traffic congestion systems, optimize energy by creating smart grids, and lower the fuel consumption and waiting time in traffic by using a smart traffic light system. All such examples can benefit from this paradigm and, to demonstrate the role of fog in different scenarios, we describe in this section two possible use cases in a smart city, i.e. a smart traffic light system [10] and a smart pipeline monitoring system [27].
2.4.2.1 Smart Traffic Light System
In a smart traffic light system scenario, the objective is to lower the congestion in the city and optimize traffic flow. The immediate outcome of adopting this approach is the protection of the environment by lowering CO2 emissions and reducing fuel consumption. Enabling an optimization like this requires the implementation of a hierarchical approach that enables real-time and near real-time operations, as well as analysis of data over long periods of time.
Each intersection in the city represents a component of our system where a smart traffic light application is deployed. The application is in charge of analyzing the collected data from local sensors and CCTV cameras and performs three major tasks: (1) compute the distance of every approaching vehicle in all directions and adapt the traffic light accordingly; (2) monitor pedestrians and cyclists to prevent any accidents, and (3) collect relevant data to help improve the overall system performance. Note that these functionalities require fast response time in case of (1) and (2), the exception being the last functionality (3), which only sends data to a higher layer for further investigation, without waiting for a response.
Another important component of our use case is the global node that creates a control function for each intersection. The key role for a global node is to collect all data from each smart traffic light and determine different commands, such that a steady flow of traffic is maintained. Notice that compared with the time requirements for the tasks deployed at an intersection, the functionality here requires a near real-time response.
The aforementioned hierarchical architecture of our traffic light system benefits from the advantages introduced by the fog computing paradigm. An immediate advantage over the cloud computing paradigm is its capabilities of orchestrating a wide range of distributed devices placed at each intersection. At the same time, it enables devices capable of analyzing data and performing fast response-time actions. Our system can be designed as a four-layer architecture, composed by the sensor layer, a fog device layer present locally at each intersection, another fog layer composed of the global node and the cloud layer. An overview of this architecture is presented in Figure 2.7.
2.4.2.2 Smart Pipeline Monitoring System
The smart pipeline monitoring system is an application deployed in the concept of smart cities, with the scope of monitoring the integrity of pipelines and preventing any serious economic and ecologic consequences. As an illustration, consider the case in which a pipeline that transports extracted oil from an offshore platform has failed, and the repercussion of failure has a big impact on the environment.
A pipeline system has an important role in our lives, being an essential infrastructure used to transport gas and liquids. It spreads throughout the entire city and provides us with basic needs like drinkable water. However, the integrity of a pipeline diminishes due to aging and sudden environmental changes. In the end, the risk of failure rises as corrosion and leaks appear.
To prevent such threats, a pipeline monitoring system has the capabilities of detecting any serious threats, reducing the overall failure by predicting three types of emergencies: (1) local disturbances (leakage, corrosion), (2) significant perturbations (damaged pipelines or approaching fire), and (3) city emergency situations. Since the infrastructure covers an entire city, our use case requires an architecture that supports geo-distribution of devices and low/predictable latency. An immediate solution is a four-layer fog computing architecture, described in Figure 2.8.
Figure 2.7 Smart Traffic light system.
As in the case of a smart traffic light system, the fog architecture requires distinct layers to enable different time-scale responses.
