Many Internet of Things applications are deployed over shared ISM (Industrial, Scientific, Medical) radiofrequency spectrum bands. With the recent development of Low Power Wide Area (LPWA) wireless networks, the probability of interference and frame collisions has significantly increased. In this context, real-time interference monitoring is essential to provide precious information for network planning, base-station installation site selection, congested area detection, etc. This work presents a novel, low complexity interference detector for mobile LPWA nodes that is designed using a set of experimental data acquisitions. A first classifier is used to detect the presence of interference and shows a detection accuracy of 94%. If interference is detected, a second classifier is used to classify the interference's relative strength into ten classes with a correct classification rate up to 97%. The detector also provides an estimation of the interference's duration with an average relative error of 2% for medium to strong interference levels.

Current Low Power Wide Area Network (LPWAN) wireless transceivers are designed, or configured at deployment time, to function assuming a worse-case application scenario. Most of the time, they waste a significant amount of energy when operated under favourable channel conditions. Energy efficient and accurate channel state classification is imperative for selecting the optimum trade-off between transceiver performance and amount of saved energy, without impacting transmission quality. This work presents a novel, low complexity channel state indicator and a simple mono-feature classifier for channel state recognition. The classifier is trained using a set of experimental data acquisitions and has a 96% accuracy when tested with a new collected data set. Specially designed for LPWA applications, the classifier is capable of distinguishing undisturbed channels from those suffering from both mobility-induced fading and interference, while operating at low to medium SNR.