Open Access

ARTICLE

A Novel Proactive AI-Based Agents Framework for an IoE-Based Smart Things Monitoring System with Applications for Smart Vehicles

Meng-Hua Yen^1,*, Nilamadhab Mishra^2,*, Win-Jet Luo³, Chu-En Lin¹

1 Department of Electronic Engineering, National Chin-Yi University of Technology, Taichung, 411030, Taiwan
2 School of Computing Science & Engineering, VIT Bhopal University, Bhopal, 466114, India
3 Department of Refrigeration, Air-Conditioning and Energy Engineering, National Chin-Yi University of Technology, Taichung, 411030, Taiwan

* Corresponding Authors: Meng-Hua Yen. Email: ; Nilamadhab Mishra. Email:

Computers, Materials & Continua 2025, 82(2), 1839-1855. https://doi.org/10.32604/cmc.2025.060903

Received 12 November 2024; Accepted 18 December 2024; Issue published 17 February 2025

Abstract

The Internet of Everything (IoE) coupled with Proactive Artificial Intelligence (AI)-Based Learning Agents (PLAs) through a cloud processing system is an idea that connects all computing resources to the Internet, making it possible for these devices to communicate with one another. Technologies featured in the IoE include embedding, networking, and sensing devices. To achieve the intended results of the IoE and ease life for everyone involved, sensing devices and monitoring systems are linked together. The IoE is used in several contexts, including intelligent cars’ protection, navigation, security, and fuel efficiency. The Smart Things Monitoring System (STMS) framework, which has been proposed for early occurrence identification and theft prevention, is discussed in this article. The STMS uses technologies based on the IoE and PLAs to continuously and remotely observe, control, and monitor vehicles. The STMS is familiar with the platform used by the global positioning system; as a result, the STMS can maintain a real-time record of current vehicle positions. This information is utilized to locate the vehicle in an accident or theft. The findings of the STMS system are promising for precisely identifying crashes, evaluating incident severity, and locating vehicles after collisions have occurred. Moreover, we formulate an ad hoc STMS network communication scenario to evaluate the efficacy of data communication by utilizing various network parameters, such as round-trip time (RTT), data packet transmission, data packet reception, and loss. From our experimentation, we obtained an improved communication efficiency for STMS across multiple PLAs compared to the standard greedy routing and traditional AODV approaches. Our framework facilitates adaptable solutions with communication competence by deploying Proactive PLAs in a cloud-connected smart vehicular environment.

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Keywords

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