DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES
Fawzy, D., Moussa, S., Badr, N. (2023). DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES. EKB Journal Management System, 23(2), 13-27. doi: 10.21608/ijicis.2023.188202.1249
Dina Fawzy; Sherin Moussa; Nagwa Badr. "DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES". EKB Journal Management System, 23, 2, 2023, 13-27. doi: 10.21608/ijicis.2023.188202.1249
Fawzy, D., Moussa, S., Badr, N. (2023). 'DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES', EKB Journal Management System, 23(2), pp. 13-27. doi: 10.21608/ijicis.2023.188202.1249
Fawzy, D., Moussa, S., Badr, N. DATA FUSION FOR DATA PREDICTION: AN IoT-BASED DATA PREDICTION APPROACH FOR SMART CITIES. EKB Journal Management System, 2023; 23(2): 13-27. doi: 10.21608/ijicis.2023.188202.1249

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

International Journal of Intelligent Computing and Information Sciences
Volume 23, Issue 2, June 2023, Page 13-27  XML PDF (447.36 K)
Document Type: Original Article
DOI: 10.21608/ijicis.2023.188202.1249
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Authors
Dina Fawzy1; Sherin Moussa email orcid 2; Nagwa Badrorcid 3
1Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
2Department of Information Systems, Faculty of Computer and Information Scences
3Department of Information Systems, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, 11566, Egypt
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.
Keywords
IoT; Data Prediction; Data Fusion; KNN; Smart Cities
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