PowerShark: IEEE 802.15.4 mote activity analysis using power traces and neural networks

Vilen Looga, Zhonghong Ou*, Yang Deng, Antti Ylä-Jääski

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)

Abstract

Analysing the power usage of an Internet of Things (IoT) device is essential in contexts such as smart building, testbed and forensics. For example, in the smart building case, schemes have been proposed to detect and analyse the activity of electric appliances based on the total power usage. In this work, we intend to take this concept a step further and devise a method to analyse the activity of a single IoT device (mote) based on its power usage. To that end, we develop a neural network (NN) based classifier PowerShark that is capable of classifying mote activity based solely on the power usage of the mote, both in online and offline mode. PowerShark can detect radio activity, hardware, operating system (OS), and the MAC layer used. To verify the accuracy of PowerShark, we use power traces from OpenMote and Zolertia Z1 motes, with ContikiOS configured either with ContikiMAC or X-MAC. Experimental results demonstrate the accuracy of our proposed classifier. Specifically, PowerShark can achieve over 95% accuracy in both training and testing phases.

Original languageEnglish
Title of host publication2016 IEEE Global Communications Conference, GLOBECOM 2016 - Proceedings
PublisherIEEE
ISBN (Electronic)9781509013289
DOIs
Publication statusPublished - 2 Feb 2017
MoE publication typeA4 Article in a conference publication
EventIEEE Global Communications Conference - Washington, United States
Duration: 4 Dec 20168 Dec 2016
Conference number: 59

Conference

ConferenceIEEE Global Communications Conference
Abbreviated titleGLOBECOM
CountryUnited States
CityWashington
Period04/12/201608/12/2016

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