Title

Automating WiFi Fingerprinting Based on Nano-Scale Unmanned Aerial Vehicles

Abstract

Explosive growth in the number of portable devices like smartphones, tablets, and smart-watches has escalated the demand for localization-based services, spurring development of numerous indoor localization techniques. Especially, widespread deployment of wireless LANs prompted ever increasing commercial interests in WiFi-based indoor localization mechanisms. However, a critical shortcoming of such localization techniques is that collecting WiFi fingerprints of an entire target area is an extremely time and labor intensive task. In this paper, we propose to automate this WiFi fingerprint collection process using a group of nano-scale unmanned aerial vehicles. Since these vehicles explore a 3D space, the WiFi fingerprints of a 3D space can be obtained without user intervention. The proposed system is implemented on a commercially available miniature open-source quadcopter platform by integrating a contemporary WiFi-fingerprint-based localization system. Experimental results demonstrate that the localization error is about 2m, which exhibits only about 20cm of accuracy degradation compared with manual WiFi fingerprint survey methods.

Publication Title

IEEE Vehicular Technology Conference

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