A Data Imputation Model based on an Ensemble Scheme

TL;DR

This paper introduces an ensemble-based data imputation model for edge computing environments that leverages spatio-temporal correlations among IoT devices to improve missing data estimation.

Contribution

It presents a novel ensemble approach that considers device similarity and group opinions for more accurate data imputation in edge computing.

Findings

Effective handling of missing IoT data in edge environments.

Improved imputation accuracy through ensemble and similarity techniques.

Robust performance across various simulation scenarios.

Abstract

Edge Computing (EC) offers an infrastructure that acts as the mediator between the Cloud and the Internet of Things (IoT). The goal is to reduce the latency that we enjoy when relying on Cloud. IoT devices interact with their environment to collect data relaying them towards the Cloud through the EC. Various services can be provided at the EC for the immediate management of the collected data. One significant task is the management of missing values. In this paper, we propose an ensemble based approach for data imputation that takes into consideration the spatio-temporal aspect of the collected data and the reporting devices. We propose to rely on the group of IoT devices that resemble to the device reporting missing data and enhance its data imputation process. We continuously reason on the correlation of the reported streams and efficiently combine the available data. Our aim is to `aggregate' the local view on the appropriate replacement with the `opinion' of the group. We adopt widely known similarity techniques and a statistical modelling methodology to deliver the final outcome. We provide the description of our model and evaluate it through a high number of simulations adopting various experimental scenarios.