SDN-Enabled Energy-Aware Routing and Distribution for Circular Sustainable Industrial Ecosystems: A Dynamic Optimization Approach

The transition to circular sustainable industrial ecosystems necessitates innovative energy management solutions that balance environmental responsibility, efficiency, and social adaptability. This research introduces a Software-Defined Networks (SDN) enabled framework that integrates energy-aware optimization techniques to achieve real-time, dynamic energy management. The proposed system addresses key challenges in circular sustainable business models (CSBMs), such as optimizing energy distribution, minimizing waste, and integrating renewable energy sources, thereby supporting the transition to a circular economy. Industrial ecosystems often suffer from inefficient energy management, leading to high operational costs, increased carbon emissions, and poor resource utilization. To overcome these challenges, this research proposes an intelligent and dynamic energy management framework that leverages SDN’s centralized control and energy-aware routing algorithms to optimize energy flow in real time. This ensures efficient energy utilization, reducing both waste and costs while enhancing sustainability. The framework incorporates an energy-aware routing algorithm that prioritizes energy-efficient paths based on power consumption, latency, and carbon footprint. It integrates an SDN controller with Industrial Internet of Things (IIoT) sensors, which monitor energy consumption, environmental conditions, and renewable energy availability. This real-time data enables the system to dynamically adjust energy distribution, ensuring that energy supply meets demand efficiently. A key contribution of this research is the integration of renewable energy sources (e.g., solar panels) and energy storage systems (e.g., batteries) into industrial networks. This enhances sustainability by reducing dependence on non-renewable energy and lowering the carbon footprint. The framework is designed to be scalable and flexible, accommodating new energy sources, storage units, and production demands as industrial ecosystems expand. This research contributes to circular sustainable business models by enabling smarter, greener, and more resilient industrial energy management, aligning with Triple Bottom Line (TBL) principles to promote economic, environmental, and social sustainability.

Authors

Institutions

Publication Details

Journal
Wireless World Research and Trends Magazine
Published
2025-08-11
DOI
https://doi.org/10.13052/2794-7254.016
Primary Topic
Software-Defined Networks and 5G
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

SDN-Enabled Energy-Aware Routing and Distribution for Circular Sustainable Industrial Ecosystems: A Dynamic Optimization Approach

Shivaleela Arlimatti, Peter Lindgren, Shrikant Mane, Rajesh Gade
Wireless World Research and Trends Magazine
Software-Defined Networks and 5G
article

SDN-Enabled Energy-Aware Routing and Distribution for Circular Sustainable Industrial Ecosystems: A Dynamic Optimization Approach

Shivaleela Arlimatti, Peter Lindgren, Shrikant Mane, Rajesh Gade
article en

Abstract

The transition to circular sustainable industrial ecosystems necessitates innovative energy management solutions that balance environmental responsibility, efficiency, and social adaptability. This research introduces a Software-Defined Networks (SDN) enabled framework that integrates energy-aware optimization techniques to achieve real-time, dynamic energy management. The proposed system addresses key challenges in circular sustainable business models (CSBMs), such as optimizing energy distribution, minimizing waste, and integrating renewable energy sources, thereby supporting the transition to a circular economy. Industrial ecosystems often suffer from inefficient energy management, leading to high operational costs, increased carbon emissions, and poor resource utilization. To overcome these challenges, this research proposes an intelligent and dynamic energy management framework that leverages SDN’s centralized control and energy-aware routing algorithms to optimize energy flow in real time. This ensures efficient energy utilization, reducing both waste and costs while enhancing sustainability. The framework incorporates an energy-aware routing algorithm that prioritizes energy-efficient paths based on power consumption, latency, and carbon footprint. It integrates an SDN controller with Industrial Internet of Things (IIoT) sensors, which monitor energy consumption, environmental conditions, and renewable energy availability. This real-time data enables the system to dynamically adjust energy distribution, ensuring that energy supply meets demand efficiently. A key contribution of this research is the integration of renewable energy sources (e.g., solar panels) and energy storage systems (e.g., batteries) into industrial networks. This enhances sustainability by reducing dependence on non-renewable energy and lowering the carbon footprint. The framework is designed to be scalable and flexible, accommodating new energy sources, storage units, and production demands as industrial ecosystems expand. This research contributes to circular sustainable business models by enabling smarter, greener, and more resilient industrial energy management, aligning with Triple Bottom Line (TBL) principles to promote economic, environmental, and social sustainability.

Wireless World Research and Trends Magazine
Shivaji University (IN), Copenhagen Business School (DK), D.Y. Patil University (IN), Dr. Hari Singh Gour University (IN)
Openalex Percentile: Top 77%
Software-Defined Networks and 5G
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.