Dynamic Collaboration Of Centralized & Edge Processing For Coordinated Data Management In An Iot Paradigm

Over the past decade, much focus in the area of Technology has deviated towards two relatively new areas; "The Internet of Things" and "Machine Learning". Although completely separate technologies, they have one major factor in common, Data. The IoT paradigm relies on sensor devices to ingest data and gain valuable insight on their surrounding environment. Data is often considered the newest natural resource. Analysing data instantaneously can give companies a leading edge in their market. Machine learning algorithms are helping companies achieve this feat in the most efficient way possible. In this paper, we propose a governance architecture for dynamic distributed data mining, utilizing a flow based programming inspired model. We illustrate a collaborative protocol between edge devices and central controllers where computation and distribution may be driven by factors including hardware limitations, latency, or energy consumption. Our proposed architecture is evaluated in a connected vehicle use case. To demonstrate the feasibility of our work, we present two scenarios; local real-time prediction of driver alertness, and task/computation offloading based on CPU usage of the edge device.