Optimizing energy usage and reducing waste Digital Twin applications in industrial IoT
(2025) p.175-188- Abstract
Industrial sector, by boosting energy efficiency and reducing waste through the use of digital twin (DT) technology in the integration of industrial IoT (IIoT). DT generates realistic simulations for predictive analytics and decision-making with the help of real-time simulations of physical objects. In this chapter, the authors focus on the DT application for the optimum utilization of energy and the resource efficiency by utilizing the IoT sensors, the AI, and big data analytics. Industries can find inefficiencies, use predictive maintenance and environmental practices by simulating operational scenarios. The chapter also outlines important challenges including that of data integration, cybersecurity, and cost and provides some... (More)
Industrial sector, by boosting energy efficiency and reducing waste through the use of digital twin (DT) technology in the integration of industrial IoT (IIoT). DT generates realistic simulations for predictive analytics and decision-making with the help of real-time simulations of physical objects. In this chapter, the authors focus on the DT application for the optimum utilization of energy and the resource efficiency by utilizing the IoT sensors, the AI, and big data analytics. Industries can find inefficiencies, use predictive maintenance and environmental practices by simulating operational scenarios. The chapter also outlines important challenges including that of data integration, cybersecurity, and cost and provides some recommendations which may be used to successfully implement such technology. An exploration into future trends such as: 5G; Edge; Ai-driven models: Drawing on this work, the role of DT in creating sharper, greener and more resilient industrial ecosystems rightfully stand in the limelight.
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- author
- Kumar, Sanjeev ; Tiwari, Geeta ; Tiwari, Neeraj and Kumar, Krishna LU
- organization
- publishing date
- 2025-05-08
- type
- Chapter in Book/Report/Conference proceeding
- publication status
- published
- subject
- host publication
- Accelerating Product Development Cycles With Digital Twins and IoT Integration
- pages
- 14 pages
- publisher
- IGI Global
- external identifiers
-
- scopus:105009677795
- ISBN
- 9798337320281
- 9798337320304
- DOI
- 10.4018/979-8-3373-2028-1.ch009
- language
- English
- LU publication?
- yes
- additional info
- Publisher Copyright: © 2025, IGI Global Scientific Publishing. All rights reserved.
- id
- 1b12c285-abec-403f-a34b-14c5aa1d6157
- date added to LUP
- 2025-12-22 14:15:40
- date last changed
- 2026-01-05 15:03:32
@inbook{1b12c285-abec-403f-a34b-14c5aa1d6157,
abstract = {{<p>Industrial sector, by boosting energy efficiency and reducing waste through the use of digital twin (DT) technology in the integration of industrial IoT (IIoT). DT generates realistic simulations for predictive analytics and decision-making with the help of real-time simulations of physical objects. In this chapter, the authors focus on the DT application for the optimum utilization of energy and the resource efficiency by utilizing the IoT sensors, the AI, and big data analytics. Industries can find inefficiencies, use predictive maintenance and environmental practices by simulating operational scenarios. The chapter also outlines important challenges including that of data integration, cybersecurity, and cost and provides some recommendations which may be used to successfully implement such technology. An exploration into future trends such as: 5G; Edge; Ai-driven models: Drawing on this work, the role of DT in creating sharper, greener and more resilient industrial ecosystems rightfully stand in the limelight.</p>}},
author = {{Kumar, Sanjeev and Tiwari, Geeta and Tiwari, Neeraj and Kumar, Krishna}},
booktitle = {{Accelerating Product Development Cycles With Digital Twins and IoT Integration}},
isbn = {{9798337320281}},
language = {{eng}},
month = {{05}},
pages = {{175--188}},
publisher = {{IGI Global}},
title = {{Optimizing energy usage and reducing waste Digital Twin applications in industrial IoT}},
url = {{http://dx.doi.org/10.4018/979-8-3373-2028-1.ch009}},
doi = {{10.4018/979-8-3373-2028-1.ch009}},
year = {{2025}},
}