ARIEL: Advanced radiofrequency indoor environment localization: Smoke conditions positioning

摘要

Indoor sensor location is a complex task. In normal circumstances laser meters, ultrasonic meters or even image processing may be used to estimate the position of a given node at a particular moment. Indoor localization in low-visibility conditions due to smoke is one of the goals that has been studied within the EU GUARDIANS project (http://vision.eng.shu.ac.uk/mmvlwiki/index.php/GUARDIANS). When the density of the smoke grows beyond the 25%, optical sensors such as laser and cameras are not efficient anymore. In these scenarios other sensors must be studied, such as sonar, radar or radiofrequency signals. In this paper we describe the ARIEL method, which uses ZigBee and Wifi signals combinations to localize a mobile sensor in a building such as a warehouse, office or campus. Moreover, the system presents a high intensity LED panel that can be activated via ZigBee in order to have a fine grained localization to get into doors and other points of interest. In addition, a digital compass and a RFID reader are used as a help to the above. Fingerprinting methods are an alternative to accurate localization of mobile sensors and actuators in indoor environments, which learn a radio map for a given scenario and use this information for calculating the position of a given node. In fact, when using other conventional methods in complex scenarios that may present irregular geometries and materials, fingerprinting techniques can be a very good alternative. Moreover, although they need a previous training of a knowledge database for each scenario, once this is done the method runs in a quite stable and accurate manner without needing any sophisticated hardware.