  Wireless Sensor Network (WSN) applications reshape the trend of warehouse
monitoring systems allowing them to track and locate massive numbers of
logistic entities in real-time. To support the tasks, classic Radio Frequency
(RF)-based localization approaches (e.g. triangulation and trilateration)
confront challenges due to multi-path fading and signal loss in noisy warehouse
environment. In this paper, we investigate machine learning methods using a new
grid-based WSN platform called Sensor Floor that can overcome the issues.
Sensor Floor consists of 345 nodes installed across the floor of our logistic
research hall with dual-band RF and Inertial Measurement Unit (IMU) sensors.
Our goal is to localize all logistic entities, for this study we use a mobile
robot. We record distributed sensing measurements of Received Signal Strength
Indicator (RSSI) and IMU values as the dataset and position tracking from Vicon
system as the ground truth. The asynchronous collected data is pre-processed
and trained using Random Forest and Convolutional Neural Network (CNN). The CNN
model with regularization outperforms the Random Forest in terms of
localization accuracy with aproximate 15 cm. Moreover, the CNN architecture can
be configured flexibly depending on the scenario in the warehouse. The
hardware, software and the CNN architecture of the Sensor Floor are open-source
under https://github.com/FLW-TUDO/sensorfloor.
