Open Access

ARTICLE

Internet of Things Software Engineering Model Validation Using Knowledge-Based Semantic Learning

by Mahmood Alsaadi, Mohammed E. Seno^*, Mohammed I. Khalaf

Department of Computer Sciences, College of Sciences, University of Al Maarif, Al Anbar, 31001, Iraq

* Corresponding Author: Mohammed E. Seno. Email:

(This article belongs to the Special Issue: Machine Learning for Privacy and Security in Internet of Things (IoT))

Intelligent Automation & Soft Computing 2025, 40, 29-52. https://doi.org/10.32604/iasc.2024.060390

Received 31 October 2024; Accepted 12 December 2024; Issue published 10 January 2025

Abstract

The agility of Internet of Things (IoT) software engineering is benchmarked based on its systematic insights for wide application support infrastructure developments. Such developments are focused on reducing the interfacing complexity with heterogeneous devices through applications. To handle the interfacing complexity problem, this article introduces a Semantic Interfacing Obscuration Model (SIOM) for IoT software-engineered platforms. The interfacing obscuration between heterogeneous devices and application interfaces from the testing to real-time validations is accounted for in this model. Based on the level of obscuration between the infrastructure hardware to the end-user software, the modifications through device replacement, capacity amendments, or interface bug fixes are performed. These modifications are based on the level of semantic obscurations observed during the application service intervals. The obscuration level is determined using knowledge learning as a progression from hardware to software semantics. The results reported were computed using specific metrics obtained from these experimental evaluations: an 8.94% reduction in interfacing complexity and a 15.04% improvement in integration progression. The knowledge of obscurations maps the modifications appropriately to reinstate the agility testing of the hardware/software integrations. This modification-based semantics is verified using semantics error, modification time, and complexity.

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Keywords

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