Yijing Zeng, Bangya Liu, Yilong Li, Domenico Giustiniano, Suman Banerjee
IEEE International Symposium on Dynamic Spectrum Access Networks
This research paper focuses on Sustainable Spectrum Crowdsensing, a paradigm that leverages the power of edge computing to measure spectrum usage in wireless networks using crowdsourced data from diverse sensors.
Key Considerations in Edge Computing Context:
- Energy Efficiency:
- Traditional spectrum sensing often involves significant energy consumption due to continuous monitoring and data transmission.
- Edge computing enables local processing and decision-making, reducing the need for constant communication with a central server and significantly conserving energy on edge devices.
- Scalability and Flexibility:
- Edge computing allows for decentralized processing and data management, making the system more scalable and adaptable to dynamic network conditions.
- This is crucial for large-scale spectrum crowdsensing deployments where the number of participating devices and the volume of data can vary significantly.
- Improved Latency:
- By performing data processing and analysis closer to the source, edge computing minimizes latency, which is critical for real-time spectrum sensing and dynamic spectrum access.
- This enables faster responses to changing spectrum conditions and improved resource utilization.
- Enhanced Privacy:
- Edge computing can help enhance privacy by minimizing the amount of sensitive data transmitted to the cloud.
- Local processing and analysis can reduce the need to share raw data, protecting user privacy and ensuring data security.
In essence, this research explores how edge computing principles can be applied to create more sustainable and efficient spectrum crowdsensing systems. By leveraging local processing, decentralized control, and optimized data management, these systems can unlock the full potential of spectrum sharing while minimizing energy consumption and maximizing resource utilization.
Read the paper here.
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