Managing the data generated by Internet of Things (IoT) sensors and actuators is one of the biggest challenges faced when deploying an IoT system. Traditional cloud-based IoT systems are challenged by the large scale, heterogeneity, and high latency witnessed in some cloud ecosystems. One solution is to decentralize applications, management, and data analytics into the network itself using a distributed and federated compute model. This approach has become known as fog computing. This document presents the conceptual model of fog and mist computing and how they relate to cloud-based computing models for IoT. This document further characterizes important properties and aspects of fog computing, including service models, deployment strategies, and provides a baseline of what fog computing is, and how it may be used.
Managing the data generated by Internet of Things (IoT) sensors and actuators is one of the biggest challenges faced when deploying an IoT system. Traditional cloud-based IoT systems are challenged by the large scale, heterogeneity, and high latency witnessed in some cloud ecosystems. One solution...
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Managing the data generated by Internet of Things (IoT) sensors and actuators is one of the biggest challenges faced when deploying an IoT system. Traditional cloud-based IoT systems are challenged by the large scale, heterogeneity, and high latency witnessed in some cloud ecosystems. One solution is to decentralize applications, management, and data analytics into the network itself using a distributed and federated compute model. This approach has become known as fog computing. This document presents the conceptual model of fog and mist computing and how they relate to cloud-based computing models for IoT. This document further characterizes important properties and aspects of fog computing, including service models, deployment strategies, and provides a baseline of what fog computing is, and how it may be used.
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