DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES

Abstract

: Recently with the high implementation of numerous Internet of Things (IoT) based systems, it becomes a crucial need to have an effective data prediction approach for IoT data analysis that copes with sustainable smart city services. Nevertheless, IoT data add many data perspectives to consider, which complicate the data prediction process. This poses the urge for advanced data fusion methods that would preserve IoT data while ensuring data prediction accuracy, reliability, and robustness. Although different data prediction approaches have been presented for IoT applications, but maintaining IoT data characteristics is still a challenge. This paper presents our proposed approach the domain-independent Data Fusion for Data Prediction (DFDP) that consists of: (1) data fusion, which maintains IoT data massive size, faults, spatiotemporality, and freshness by employing a data input-data output fusion approach, and (2) data prediction, which utilizes the K-Nearest Neighbor data prediction technique on the fused data. DFDP is validated using IoT data from different smart cities datasets. The experiments examine the effective performance of DFDP that reaches 91.8% accuracy level.