Detecting and avoiding multiple sources of interference in the 2.4 GHz spectrum

Abstract

Sensor networks operating in the 2.4 GHz band often face cross-technology interference from co-locatedWiFi andBluetooth devices. To enable effective interference mitigation, a sensor network needs to know the type of interference it is exposed to. However, existing approaches to interference detection are not able to handle multiple concurrent sources of interference. In this paper, we address the problem of identifying multiple channel activities impairing a sensor network’s communication, such as simultaneous WiFi traffic andBluetooth data transfers.We present Speck- Sense, an interference detector that distinguishes between different types of interference using a unsupervised learning technique. Additionally, Speck- Sense features a classifier that distinguishes between moderate and heavy channel traffic, and also identifies WiFi beacons. In doing so, it facilitates interference avoidance through channel blacklisting. We evaluate Speck- Sense on common mote hardware and show how it classifies concurrent interference under real-world settings. We also show how SpeckSense improves the performance of an existingmultichannel data collection protocol by 30%.