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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. 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.