Abstract
AbstractTraditional IoT topologies involve access and core networks that share a common edge. On this edge, border routers and gateways are responsible for converting protocols at different layers of the stack. Devices like sensors and actuators sit on the access network while applications are located on the core network. The application performs predictions that trigger actuation based on received sensor readouts. Prediction, in turn, is the result of machine learning (ML) algorithms that are typically executed on the cloud. An alternative to this approach consists of performing the prediction on constrained devices on the IoT access network. This leads to Tiny ML (TinyML) and mist computing. In this context, there is a trade‐off between latency and computational power that becomes a deciding factor when choosing the application to carry on predictions. This paper introduces an algorithm that can be used to dynamically select the right application based on network layer parameters.
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