Sign in Subscribe
Machine learning

Machine Learning Optimizes Carbon Footprint in IoT Networks

Strategic Insights

— 2 min read

Artificial intelligence and machine learning techniques can significantly reduce the environmental impact of Internet of Things (IoT) networks. Researchers from the Department of Information and Communication Technologies, Universidad Politécnica de Madrid, have developed an optimization framework leveraging machine learning to minimize the carbon footprint associated with IoT multi-hop network deployments. The study found that placing gateways using neural networks can achieve a 14% reduction in carbon footprint for simple networks, compared to those not using optimization. The integer linear programming (ILP) approach, while more efficient, incurs a computational cost 250 times higher, highlighting the superior scalability of machine learning techniques.

Key Takeaways:

  • The IoT is gaining attention for its potential to digitally transform various sectors through seamless connectivity and data exchange.
  • Deploying IoT networks is challenging due to diverse application requirements, with limited focus on minimizing their carbon footprint.
  • The research presents an optimization framework using machine learning techniques to minimize the carbon footprint associated with IoT multi-hop network deployments.
  • The framework involves varying the placement of required gateways using neural networks, achieving a 14% reduction in carbon footprint for simple networks compared to those not using optimization.
  • The integer linear programming (ILP) approach can reduce the CF by 16.6% for identical networks, but incurs a computational cost more than 250 times higher.
  • Machine learning techniques are more scalable and advantageous for larger networks, as discussed in the study's concluding remarks.
  • The research highlights the need for more focus on examining and enhancing the carbon footprint associated with IoT network deployments.
  • The results have implications for the development of more sustainable IoT networks and the reduction of their environmental impact.

Statistics:

  • A 14% reduction in carbon footprint for simple networks using neural networks compared to those not using optimization.
  • A 16.6% reduction in carbon footprint for identical networks using the integer linear programming (ILP) approach.
  • A 250 times higher computational cost associated with the ILP approach compared to machine learning techniques.

Sources:

  • Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning. IEEE Open Journal of the Computer Society, 2025,6():531-542.
  • Department of Information and Communication Technologies, Universidad Politécnica de Madrid.
  • Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez, Joan Garcia-Haro (researchers).
  • NewsRx. New Machine Learning Findings from Department of Information and Communication Technologies Published (Minimizing the Carbon Footprint in LoRa-Based IoT Networks: A Machine Learning Perspective on Gateway Positioning). Journal of Engineering. May 12, 2025; p 1968.

Read more