Reliable IoT analytics at scale

Societies and legislations are moving towards automated decision-making based on measured data in safety-critical environments. Over the next years, density and frequency of measurements will increase to generate more insights and get a more solid basis for decisions, including through redundant low-cost sensor deployments. The resulting data characteristics lead to large-scale system design in which small input data errors may lead to severe cascading problems including ultimately wrong decisions. To ensure internal data consistency to mitigate this risk in such IoT environments, fast-paced data fusion and consensus among redundant measurements need to be achieved. In this context, we introduce history-aware sensor fusion powered by accurate voting with clustering as a promising approach to achieve fast and informed consensus, which can converge to the output up to 4X faster than the state of the art history-based voting. Leveraging three case studies, we investigate different voting schemes and show how this approach can improve data accuracy by up to 30% and performance by up to 12% compared to state-of-the-art sensor fusion approaches. We furthermore contribute a specification format for easily deploying our methods in practice and use it to develop a pilot implementation.