kumoh national institute of technology
Networked Systems Lab.

Rizki Rivai Ginanjar, Si-Wan Kim, and Dong-Seong Kim, "Extreme Learning Machine-Based Real-Time Node Localization: UAV Approach", 9th International Workshop on Wireless Networking & Control for Unmanned Autonomous Vehicles (Wi-UAV'18) as part of GLOBECOM 2018, December 9th - 13th, 2018, Abu Dhabi, United Arab Emirate (R)
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Date : 2018-06-08
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Abstract - In this paper, a novel real-time neural network based localizations technique designed for industrial WSN environment is introduced. The localization is done using mobile unmanned aerial vehicle (UAV) as the anchor node to send the beacon signals, and every unknown node can predict it's current position based on the RSSI values of the received beacon signals using Extreme Learning Machine (ELM). There are no deployed ground anchor nodes and require fewer anchor nodes compared to RSSI based traditional localization technique to yields better accuracy. Simulation results show that this technique can perform real-time unknown nodes localization with less error.