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Indoor localization in smoke conditions is one of the EU GUARDIANS project goals. When smoke density grows, optical sensors such as laser range finders and cameras cease to be efficient. Zigbee sensor networks provide an interesting approach due to the fact that radiofrequency signals are propagated easily in such conditions. This paper focuses on the Zigbee fingerprinting localization method used to obtain the position of the mobile sensors and actuators by training a database of radio signals for each scenario. Once this is done the proposed recognition method runs in a quite stable and accurate manner without needing any sophisticated hardware. Results compare the procedure with others such as KNN, and neural networks, demonstrating the feasibility of the method for a real emergency intervention.
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