Planning Energy-Efficient Smart Industrial Spaces for Industry 4.0
Given the significant increase in electricity consumption, especially in the industrial and commercial categories, exploring new energy sources and developing innovative technologies are essential. The fourth industrial revolution (Industry 4.0) and digital transformation are not just buzzwords; the...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
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| Series: | Eng |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2673-4117/6/3/53 |
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| Summary: | Given the significant increase in electricity consumption, especially in the industrial and commercial categories, exploring new energy sources and developing innovative technologies are essential. The fourth industrial revolution (Industry 4.0) and digital transformation are not just buzzwords; they offer real opportunities for energy sustainability, using technologies such as cloud computing, artificial intelligence, and the Internet of Things (IoT). In this context, this study focuses on improving energy efficiency in smart spaces within the context of Industry 4.0 by utilizing the SmartParcels framework. This framework creates a detailed and cost-effective plan for equipping specific areas of smart communities, commonly referred to as parcels. By adapting this framework, we propose an integrated model for planning and implementing IoT applications that optimizes service utilization while adhering to operational and deployment cost constraints. The model considers multiple layers, including sensing, communication, computation, and application, and adopts an optimization approach to meet the needs related to IoT deployment. In simulated industrial environments, it demonstrated scalability and economic viability, achieving high service utility and ensuring broad geographic coverage with minimal redundancy. Furthermore, the use of heuristics for device reuse and geophysical mapping selection promotes cost-effectiveness and energy sustainability, highlighting the framework’s potential for large-scale applications in diverse industrial contexts. |
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| ISSN: | 2673-4117 |