Adaptive uplink data compression in spectrum crowdsensing systems

Yijing Zeng, Roberto Calvo-Palomino, Domenico Giustiniano, Gerome Bovet, Suman Banerjee

IEEE/ACM Transactions on Networking

This research paper focuses on FlexSpec, a framework for adaptive uplink data compression in spectrum crowdsensing systems.

Key Considerations in Edge Computing Context:

Reduced Uplink Traffic: Spectrum crowdsensing often involves a large number of sensors transmitting data to a central server. Uplink traffic can become a bottleneck, especially in resource-constrained edge networks. FlexSpec addresses this by:
- Compressing spectrum data: Using the Walsh-Hadamard transform to significantly reduce the size of spectrum data (both IQ and PSD) transmitted from sensors.
Adaptive Compression:
- FlexSpec dynamically adjusts the compression ratio based on real-time factors like:
  - Spectrum activity
  - Application requirements
  - Available bandwidth
- This ensures optimal data compression while maintaining the accuracy and performance of spectrum sensing applications.
Energy Efficiency:
- Reduced uplink traffic translates to lower energy consumption at the sensor nodes, which is crucial for battery-powered devices commonly used in edge deployments.
Improved Scalability:
- By optimizing data transmission, FlexSpec enables more efficient utilization of network resources, improving the scalability of large-scale spectrum crowdsensing systems.

In essence, FlexSpec demonstrates how edge computing principles can be applied to optimize spectrum crowdsensing systems. By leveraging adaptive data compression techniques, it addresses the challenges of limited uplink bandwidth and energy constraints, enabling more efficient and scalable spectrum sensing in edge environments.

Read the paper here.