Towards Energy Impact on AI-Powered 6G IoT Networks: Centralized vs. Decentralized
Summary: arXiv:2604.19377v1 Announce Type: new
Abstract: The emergence of sixth-generation (6G) technologies has introduced new challenges and opportunities for machine learning (ML) applications in Internet of Things (IoT) networks, particularly concerning energy efficiency. As model training and data transmission contribute significantly to energy consumption, optimizing these processes has become critical for sustainable system design. This study first conducts an analysis on the energy consumption model for both centralized and decentralized architecture and then presents a testbed deployed within the German railway infrastructure, leveraging sensor data for ML-based predictive maintenance. A comparative analysis of distributed versus Centralized Learning (CL) architectures reveals that distributed models maintain competitive predictive accuracy (~90%) while reducing overall electricity consumption by up to 70%. These findings underscore the potential of distributed ML to improve energy efficiency in real-world IoT deployments, particularly by mitigating transmission-related energy costs.
Introduction
The transition to 6G technology marks a significant advancement in IoT networks, where the integration of AI and machine learning is expected to play a pivotal role. However, this advancement is accompanied by an urgent need to address energy consumption concerns that arise from data processing and model training.
Energy Consumption in IoT Networks
Energy consumption in IoT networks is primarily driven by:
- Data transmission between devices and centralized servers.
- Model training processes that require substantial computational resources.
- The operational energy costs associated with maintaining network infrastructure.
As IoT devices proliferate, the energy required for these processes can escalate, presenting challenges for sustainable network design.
Centralized vs. Decentralized Architectures
This study analyzes two predominant architectures in IoT networks:
- Centralized Learning (CL): In this architecture, data is sent to a central server for processing and model training. While this may simplify management and increase accuracy, it also leads to higher energy consumption due to extensive data transmission.
- Distributed Learning: This approach allows for processing data closer to the source, reducing the need for data transmission and therefore minimizing energy consumption. Models can be trained collaboratively across devices, leading to comparable accuracy.
Testbed Implementation
The research team deployed a testbed within the German railway infrastructure, utilizing sensor data for machine learning-based predictive maintenance. This real-world application provided valuable insights into the energy dynamics of both architectures.
Findings and Implications
The comparative analysis yielded noteworthy findings:
- Distributed models achieved predictive accuracy of approximately 90%, comparable to centralized models.
- Electricity consumption in distributed systems was reduced by up to 70%, highlighting a significant energy efficiency advantage.
These results illustrate the potential of distributed ML frameworks to enhance energy efficiency in IoT applications, particularly in scenarios where transmission costs are a critical concern.
Conclusion
As 6G technologies evolve, addressing energy consumption through optimized ML architectures will be essential for sustainable IoT networks. This study demonstrates that adopting distributed learning can significantly reduce energy usage while maintaining predictive accuracy, paving the way for more efficient and environmentally friendly IoT implementations.