Fog Computing for IoT

Fog computing is a paradigm that provides services to user requests at the edge networks. As the definition suggests, the fog computing platform lies between the cloud servers and the users. In a fog-enabled environment, the devices at the fog layer usually perform operations related to networking such as routers, gateways, bridges, and hubs. Researchers envision these devices to be capable of performing both computational and networking operations, simultaneously. Although these devices are resource-constrained compared to the cloud servers, the geological spread and the decentralized nature help in offering reliable services with coverage over a wide area. Further, with fog computing, manufacturers and service providers offer their services at affordable rates. Another advantage of fog computing is the physical location of the devices, which are much closer to the users than the cloud servers. Such placement of the devices reduces operational latency significantly.

We study the suitability of fog computing in IoT environments and theoretically modeled its parameters to support IoT applications. We then study its impact on the IoT environments from different perspectives: computation offloading, while reducing operational latencies and energy consumption. We also offer a detailed analysis of the behavior of the devices and enhance the QoS for the users.

 

Assessment and Theoretical Modelling of Fog Computing

In this work, we perform a comparative study of the fog computing paradigm with that of the cloud. Towards this, we mathematically formulate the parameters affecting the devices involved, such as power consumption, latency, CO2 emission, and cost. As one of the first attempts, we assess how the fog computing helps in enhancing services for the users in IoT environments, outperforming the cloud computing paradigm.

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In this work, we present a theoretical model for the fog computing paradigm along with definitions of the individual components responsible in the context of IoT applications. This work was one of the first attempts to provide mathematical formulations for the fog computing paradigm. We then perform a comparative study against the cloud computing paradigm and present how fog computing outperforms the former for various parameters.

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Computation Offloading in Fog Computing

In this work, we propose a method for controlling a micro-quadcopter using gestures instead of traditional remote controllers. Towards this, we designed a fog-based architecture for taking inputs through a camera and then send commands to the micro-quadcopter, which makes the flight more responsive and adaptive. The proposed architecture also provides better accuracy than the state-of-the-art technologies, such as Kinect-based hardware and CNN-based solutions. The fog-based architecture also allows stabilization of the micro-quadcopter even during the presence of unbalanced payloads, which reduces frequent replacement of micro-quadcopters.

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In this work, we present an Integer Linear Program (ILP) based solution for multi-hop computation offloading in a software-defined access network. With the ILP, we make optimal decisions for local/remote computations, fog node selection, and path selection. To cope with the dynamic nature of the IoT environments, we formulate our ILP by considering essential parameters such as delay, energy consumption, multi-hop paths, and network conditions. Experimental results of this work shows reduced delay and energy consumption compared to existing solutions.

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