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A Clustering Approach for Profiling LoRaWAN IoT Devices

  • Jacopo Maria Valtorta
  • Alessio Martino
  • Francesca Cuomo
  • Domenico GarlisiEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11912)

Abstract

Internet of Things (IoT) devices are starting to play a predominant role in our everyday life. Application systems like Amazon Echo and Google Home allow IoT devices to answer human requests, or trigger some alarms and perform suitable actions. In this scenario, any data information, related device and human interaction are stored in databases and can be used for future analysis and improve the system functionality. Also, IoT information related to the network level (wireless or wired) may be stored in databases and can be processed to improve the technology operation and to detect network anomalies. Acquired data can be also used for profiling operation, in order to group devices according to their characteristics. LoRaWAN (Long Range Wide Area Network) is one of the emerging IoT technologies in today’s world, it is a protocol based on LoRa modulation. In this work, we propose a methodology to process LoRaWAN packets and perform profiling of the IoT devices. Specifically, we use the k-means algorithm to group devices according to their radio and network behaviour. We tested our approach on a real LoRaWAN network where the entire captured traffic is stored in a proprietary database. Our analysis, performed on 286, 753 packets with 765 devices involved, leads to remarkable clustering performance according to validation indices such as the Silhouette and the Davies-Bouldin indices. Further, with the help of field-experts, we were able to analyze clusters’ contents, revealing results both in line with the current network behaviour and alerts on malfunctioning devices, remarking the reliability of the proposed approach.

Keywords

IoT LoRa LoRaWAN Machine Learning k-means Anomaly detection Cluster analysis 

Notes

Acknowledgement

We thank UNIDATA S.p.A. who provided insight and expertise that greatly assisted our research, as well the access to a subset of the data for the analysis.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Information Engineering, Electronics and TelecommunicationsUniversity of Rome “La Sapienza”RomeItaly
  2. 2.University of PalermoPalermoItaly
  3. 3.Consorzio Nazionale Interuniversitario per le Telecomunicazioni (CNIT)ParmaItaly

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