Energy sampling-based interference detection and identification (IDI) methods collide with the limitations of commercial off-the-shelf (COTS) IoT hardware. Moreover, long sensing times, complexity and inability to track concurrent interference strongly inhibit their applicability in most IoT deployments. Motivated by the increasing need for on-device IDI for wireless coexistence, we develop a lightweight and efficient method targeting interference identification already at the level of single interference bursts. Our method exploits real-time extraction of envelope and model-aided spectral features, specifically designed considering the physical properties of signals captured with COTS hardware. We adopt manifold supervised-learning (SL) classifiers ensuring suitable performance and complexity trade-off for IoT platforms with different computational capabilities. The proposed IDI method is capable of real-time identification of IEEE 802.11b g n, 802.15.4, 802.15.1 and Bluetooth Low Energy wireless standards, enabling isolation and extraction of standard-specific traffic statistics even in the case of heavy concurrent interference. We perform an experimental study in real environments with heterogeneous interference scenarios, showing 90 -97 burst identification accuracy. Meanwhile, the lightweight SL methods, running online on wireless sensor networks-COTS hardware, ensure sub-ms identification time and limited performance gap from machine-learning approaches.
In @cite_28 , the identification of concurrent multi-source interference is based on @math -means clustering of RSSI-samples. Using RSSI sampling of @math kHz and sampling window of @math s, the authors @cite_28 in achieved a classification accuracy of 90$
@cite_28
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Sensor networks operating in the 2.4 GHz band often face cross-technology interference from co-located WiFi and Bluetooth 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 and Bluetooth data transfers. We present SpeckSense, an interference detector that distinguishes between different types of interference using a unsupervised learning technique. Additionally, SpeckSense 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 SpeckSense 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 existing multichannel data collection protocol by 30 .