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Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

Revolutionizing Urban Mobility: IoT-Enhanced Autonomous Parking Solutions with Transfer Learning for Smart Cities.

Qaiser Abbas^1,2, Gulzar Ahmad³, Tahir Alyas⁴

¹Faculty of Computer and Information Systems, Islamic University of Madinah, Madinah 42351, Saudi Arabia.

Sensors (Basel, Switzerland)

|November 14, 2023

Summary

This study introduces the SCOPE model for smart city operations, focusing on a car parking system using AI. It employs deep learning models like Alex Net and YOLO for efficient vacant slot identification and sustainable urban management.

Keywords:

IoT cloud computing modeling performance secure data management smart city

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Area of Science:

Computer Science
Urban Planning
Artificial Intelligence

Background:

Smart cities integrate diverse technologies like IoT, SDN, 5G, AI, and analytics for streamlined operations.
Complexity from multiple cloud providers necessitates a stable platform for sustainable smart city development.
The Smart City Operational Platform Ecology (SCOPE) model addresses these demands, incorporating machine learning and ecosystem management.

Purpose of the Study:

To present a module of the SCOPE model focused on data processing and learning for object identification in smart cities.
To introduce a smart car parking system utilizing intelligent identification techniques for vacant slot detection.
To enhance procedural stability and improvement in smart city operations through advanced learning mechanisms.

Main Methods:

Development of the SCOPE model integrating machine learning, cognitive correlates, ecosystem management, and security.
Implementation of a two-tier learning controller within the SCOPE framework.
Utilization of deep learning models, specifically Alex Net and YOLO, for object identification in the car parking system.

Main Results:

Demonstration of a functional car parking system leveraging smart identification for vacant slot detection.
Validation of the two-tier learning controller's effectiveness in ensuring procedural stability.
Successful application of Alex Net and YOLO models for accurate object identification within the smart city context.

Conclusions:

The SCOPE model provides a balanced ecosystem for smart city sustainability and progress.
The proposed car parking system effectively utilizes AI and deep learning for efficient resource management.
The research contributes to advancing intelligent systems for smart city operational efficiency and automation.