Scalable K-Nearest Neighbors Implementation using Distributed Embedded Systems

The distributed embedded systems paradigm is a promising platform for high-performance embedded applications. We present a distributed algorithm and system based on cost-effective devices. The proof of concept shows how a parallelized approach leveraging a distributed embedded platform can address the computational of the Machine Learning K-Nearest Neighbors (K-NN) algorithm with large and heterogeneous datasets.