In-network machine learning predictive analytics: A swarm intelligence approach

Abstract

This chapter addresses the problem of collaborative Predictive Modelling via in-network processing of contextual information captured in Internet of Things (IoT) environments. In-network predictive modelling allows the computing and sensing devices to disseminate only their local predictive Machine Learning (ML) models instead of their local contextual data. The data center, which can be an Edge Gate- way or the Cloud, aggregates these local ML predictive models to predict future outcomes. Given that communication between devices in IoT environments and a centralised data center is energy consuming and communication bandwidth demanding, the local ML predictive models in our proposed in-network processing are trained using Swarm Intelligence for disseminating only their parameters within the network. We further investigate whether dissemination overhead of local ML predictive models can be reduced by sending only relevant ML models to the data center. This is achieved since each IoT node adopts the Particle Swarm Optimisation algorithm to locally train ML models and then collaboratively with their network neighbours one representative IoT node fuses the local ML models. We provide comprehensive experiments over Random and Small World network models using linear and non-linear regression ML models to demonstrate the impact on the predictive accuracy and the benefit of communication-aware in-network predictive modelling in IoT environments.