Accuracy vs. traffic trade-off of learning IoT data patterns at the edge with hypothesis transfer learning

Right now, the dominant paradigm to support knowledge extraction from raw IoT data is through global cloud platform, where data is collected from IoT devices, and analysed. However, with the ramping trend of the number of IoT devices spread in the physical environment, this approach might simply not scale. The data gravity concept, one of the basis of Fog and Mobile Edge Computing, points towards a decentralisation of computation for data analysis, whereby the latter is performed closer to where data is generated. Along this trend, in this paper we explore the accuracy vs. network traffic trade-off when using Hypothesis Transfer Learning (HTL) to learn patterns from data generated in a set of distributed physical locations. HTL is a standard machine learning technique used to train models on separate disjoint training sets, and then transfer the partial models (instead of the data) to reach a unique learning model. We have previously applied HTL to the problem of learning human activities when data are available in different physical locations (e.g., areas of a city). In our approach, data is not moved from where it is generated, while partial models are exchanged across sites. The HTL-based approach achieves lower (though acceptable) accuracy with respect to a conventional solution based on global cloud computing, but drastically cuts the network traffic. In this paper we explore the trade-off between accuracy and traffic, by assuming that data are moved to a variable number of data collectors where partial learning is performed. Centralised cloud and completely decentralised HTL are the two extremes of the spectrum. Our results show that there is no significant advantage in terms of accuracy, in using fewer collectors, and that therefore a distributed HTL solutions, along the lines of a fog computing approach, is the most promising one.