An Ensemble Model for Data Imputation to Support Pervasive Applications

Abstract

Pervasive Computing (PC) opens up the room for the adoption of devices very close to end users that gives the opportunity to interact with them and execute various applications to facilitate their every day activities. Pervasive applications are supported by the evolution of the Internet of Things (IoT) as well as the Edge Computing (EC) that offer vast infrastructures where data can be collected and processed. EC acts as the mediator between the IoT and the Cloud becoming the middle point where data are transferred before they become the subject of processing by Cloud services. IoT devices can assist in the collection of data and EC nodes could play the role of intermediate processing points executing the desired tasks requested by applications. Any processing can be affected by the presence of missing values that may jeopardize the quality of the outcomes. In this paper, we propose a setting where EC nodes play the aforementioned processing role for data reported by IoT devices and adopt an ensemble scheme for data imputation in the case where missing values are present. Our model relies on the local view of the IoT devices reporting a data vector with a missing value, the view of the group that consists of the IoT nodes with a high similarity in the reported data and a probabilistic approach that reveals the statistics of data as realized in the group of similar reports. The proposed scheme continuously detects the correlation between the incoming data streams and efficiently combines the available data vectors before it is in a position to suggest replacements for missing values. The envisioned aggregation mechanism is capable of resulting the appropriate replacements aligned with the aforementioned views on the collected data. Our ensemble model relies on a number of similarity metrics and statistics to derive the final outcome. The paper reports on the description of the proposed model and elaborates on its validation based on various evaluation scenarios.