Adaptive Recovery of Incomplete Datasets for Edge Analytics

The Internet of Things (IoT) has attracted significant attention from both academia and industry, thanks to applications such as smart cities, smart buildings and intelligent traffic management. These systems rely on data, collected from IoT devices, that are sent to the cloud for analytics. Data are either used for near real-time decisions or stored for long-term analysis. However, in highly distributed IoT systems, missing or invalid data may appear because of different reasons including sensor failures, monitoring system failures and network failures. Analyzing incomplete datasets can lead to inaccurate results and imprecise decisions, with negative effects on the target systems. Also, due to the increasing size of such systems and the consequently increasing amount of data generated from sensors, recovery of incomplete datasets for analytics on the cloud is often infeasible, due to the limited bandwidth available and the strict latency constraints of IoT applications. We propose a novel semi-automatic recursive mechanism for recovery of incomplete datasets on the edge that is closer to the source of data. This mechanism enables efficient recovery of incomplete datasets employing different forecasting techniques for multiple gaps, based on user specifications. We evaluate our approach on datasets coming from the context of smart buildings and smart homes. The experimental results show that our approach is able to identify multiple gaps, then recover incomplete datasets, decreasing forecasting error by up to 82.68%, and reducing running time by up to 52.38%.