PowerShark: IEEE 802.15.4 Mote Activity Analysis Using Power Traces and Neural Networks
Vilen Looga, Zhonghong Ou, Yang Deng, Antti Ylä-Jääski · 2016
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.