# Leveraging online learning for CSS in frugal IoT network

## TL;DR

This paper introduces an online learning-based method for centralized spectrum sensing in IoT networks, improving detection accuracy and extending device lifespan by selectively activating sensors.

## Contribution

It presents a novel online learning algorithm for collaborative spectrum sensing that adapts based on detector performance, optimizing network efficiency and device longevity.

## Key findings

- Improved detection accuracy and reduced misdetection.
- Enhanced network performance with lower inter-user collision.
- Extended device operational lifetime without sacrificing sensing quality.

## Abstract

We present a novel method for centralized collaborative spectrum sensing for IoT network leveraging cognitive radio network. Based on an online learning framework, we propose an algorithm to efficiently combine the individual sensing results based on the past performance of each detector. Additionally, we show how to utilize the learned normalized weights as a proxy metric of detection accuracy and selectively enable the sensing at detectors. Our results show improved performance in terms of inter-user collision and misdetection. Further, by selectively enabling some of the devices in the network, we propose a strategy to extend the field life of devices without compromising on detection accuracy.