Organized Optimization Integration Validation Model for Internet of Things (IoT)-Based Real-Time Applications

Abdullah Alghuried, Moahd Khaled Alghuson (), Turki S. Alahmari and Khaled Ali Abuhasel ()
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Abdullah Alghuried: Department of Industrial Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia
Moahd Khaled Alghuson: Department of Industrial Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia
Turki S. Alahmari: Department of Civil Engineering, Faculty of Engineering, University of Tabuk, Tabuk 47512, Saudi Arabia
Khaled Ali Abuhasel: Industrial Engineering Department, College of Engineering, University of Bisha, Bisha 61922, Saudi Arabia

Mathematics, 2024, vol. 12, issue 15, 1-20

Abstract: Emerging technology like the Internet of Things (IoT) has great potential for use in real time in many areas, including healthcare, agriculture, logistics, manufacturing, and environmental surveillance. Many obstacles exist alongside the most popular IoT applications and services. The quality of representation, modeling, and resource projection is enhanced through interactive devices/interfaces when IoT is integrated with real-time applications. The architecture has become the most significant obstacle due to the absence of standards for IoT technology. Essential considerations while building IoT architecture include safety, capacity, privacy, data processing, variation, and resource management. High levels of complexity minimization necessitate active application pursuits with variable execution times and resource management demands. This article introduces the Organized Optimization Integration Validation Model (O2IVM) to address these issues. This model exploits k-means clustering to identify complexities over different IoT application integrations. The harmonized service levels are grouped as a single entity to prevent additional complexity demands. In this clustering, the centroids avoid lags of validation due to non-optimized classifications. Organized integration cases are managed using centroid deviation knowledge to reduce complexity lags. This clustering balances integration levels, non-complex processing, and time-lagging integrations from different real-time levels. Therefore, the cluster is dissolved and reformed for further integration-level improvements. The volatile (non-clustered/grouped) integrations are utilized in the consecutive centroid changes for learning. The proposed model’s performance is validated using the metrics of execution time, complexity, and time lag.

Keywords: integration validation; IoT; k-means clustering; prioritization; real-time applications (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2024
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