Fog Computing. Группа авторов

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Fog Computing - Группа авторов


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or is not responding, the user should be able to control things without crashing the whole edgeOS).

Structure of a variant of edgeOS in the smart home environment, with EdgeOS providing a communication layer that supports multiple communication methods such as WiFi, Bluetooth, ZigBee, or a cellular network.

      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.

      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.

      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.

An overview of the smart traffic light system designed as a four-layer architecture, composed of the sensor layer, a fog device layer present at each intersection, another fog layer composed of the global node, and the cloud layer. A smart pipeline four-layer fog computing architecture: (1) Data Centers; (2) Intermediate computing nodes; (3) Edge computing nodes; (4) Sensing networks on critical infrastructures.

      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


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