Abstract
The paper proposes an intelligent data sensing and geo-localization algorithm, based on an innovative mobile computing system that measures the power level of RF sources through a 2G/5G femtocell-UAV system. In natural disasters (mainly earthquakes and floods) the system can identify any missing persons under the rubble within a range of precision between 1 to 2 meters. In this paper, more specifically, the algorithm allows classifying the terminal even in the presence of obstacles that cause anisotropic propagation of radio signals, through a series of power measurements based on the Reference Signal Received Power (RSRP). An attenuation model that takes into account the different types of materials is introduced, and a method for optimizing the drone's flight path and duration is proposed. The performances, expressed in terms of accuracy in identifying the mobile terminal and in terms of position estimation average error, are evaluated according to the material's density and its attenuation.
Highlights
The recent development of the Internet of Things (IoT), has enabled new types of sensors that can be interconnected through the Internet
This will have a significant impact on the management of natural disasters, when the aim is improving effectiveness in research, identifying and recovering missing persons, and increasing the possibility of saving lives [1]–[5]
In [6], [7], we proposed an innovative technique for searching and identifying missing persons in natural disaster scenarios by employing a new UAV-femtocell mobile computing system
Summary
The recent development of the Internet of Things (IoT), has enabled new types of sensors that can be interconnected through the Internet. – a new criterion for classification and geolocation in the presence of non-isotropic radio signal propagation; The algorithm allows to classify the terminal inside or outside the monitoring area and, subsequently, to identify the position with a certain precision, even in the presence of obstacles that act in such a way as to render the radio signal’s propagation anisotropic. Beritelli: Smart UAV-Femtocell Data Sensing System for Post-Earthquake Localization of People followed by section 3 which illustrates the radiomobile signal propagation and material attenuation; Section 4 describes the propounded smart femtocell-UAV data sensing system; Sections 5 and 6 highlight the proposed technique which is divided into two phases: classification, and localization; Section 7 presents a performance analysis in terms of accuracy when identifying the mobile terminal and in terms of position estimation average error; Section 8 is devoted to conclusions
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