RSSI-Based Machine Learning with Pre- and Post-Processing for Cell-Localization in IWSNs

TL;DR

This paper presents an improved RSSI-based machine learning approach with pre- and post-processing techniques for cell-level localization in industrial wireless sensor networks, achieving high accuracy without direct sensor interaction.

Contribution

It introduces novel feature extraction and a two-stage classification method using Hidden Markov Models to enhance localization accuracy in IWSNs.

Findings

Achieved 93.5% cell-level localization accuracy.

Demonstrated robustness of the method with extensive measurement data.

Improved classification performance over standard approaches.

Abstract

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\%.