Quality-aware aggregation & predictive analytics at the edge

We investigate the quality of aggregation and predictive analytics in edge computing environments. Edge analytics require pushing processing and inference to the edge of a network of sensing & actuator nodes, which enables huge amount of contextual data to be processed in real time that would be prohibitively complex and costly to transfer on centralized locations. We propose a quality-aware, timeoptimized edge analytics model that supports communication efficient predictive modeling within the edge network. Our idea rests on the capability of edge nodes to intelligently decide when and which data to deliver and process in light of minimizing the communication overhead and maximizing the quality of analytics results. We provide mathematical modeling, performance and comparative assessment over real datasets showing its benefits in edge computing environments. Keywords-Edge predictive analytics, quality of analytics, communication efficiency, optimal stopping theory.