  Industrial wireless sensor networks are becoming crucial for modern
manufacturing. If the sensors in those networks are mobile, the position
information, besides the sensor data itself, can be of high relevance. E.g.
this position information can increase the trustability of a wireless sensor
measurement by assuring that the sensor is not physically removed, off track,
or otherwise compromised.
  In certain applications, localization information at cell-level, whether the
sensor is inside or outside a room or cell, is sufficient. For this,
localization using Received Signal Strength Indicator (RSSI) measurements is
very popular since RSSI values are available in almost all existing
technologies and no direct interaction with the mobile sensor node and its
communication in the network is needed. For this scenario, we propose methods
to improve the robustness and accuracy of common machine learning classifiers,
by using features based on short-term moments and a second classification stage
using Hidden Markov Models. With the data from an extensive measurement
campaign, we show the applicability of our method and achieve a cell-level
localization accuracy of 93.5\%.
