A Smart UAV-Femtocell Data Sensing System for Post-Earthquake Localization of People

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. INDEX TERMS Femtocell/UAV system, geo-localization technique, 2G/5G radio technologies, reference signal received power (RSRP), mobile terminal classification/positioning algorithm, energy consumption.

A Realistic Model to Support Rescue Operations After an Earthquake via UAVs

“…In all the experiments, we use the following parameter setting: B = 50 minutes (in agreement with the current technology, see e.g. [16]) and -to ease the reading of the results-the speed is chosen as 1000 meters per minute, corresponding to about 17 meters per second (in line with other recent papers in the literature, such as [40], [41]); is a real value randomly set (with uniform distribution) in the interval (0, 3]; when simulating the second scenario, we assign to sites a not null value of σ ′ randomly chosen with uniform distribution either in the interval (0, 3] or in the interval (7,10] with probability p, p varying among three possible values: 0.25, 0.5 and 0.75. Only when σ ′ ∈ (7, 10], we need to set B = 60, instead of 50, to guarantee that there exists a feasible solution.…”

“…For example, in the majority of the papers dealing with Search and Rescue (e.g., [3], [4]), UAVs are supposed not to have battery constraints at all, while these are usually rather pressing; in other cases, UAVs are assumed to be able to fly back to the base in a very short time (e.g. in [5] UAVs go to recharge their battery when they are at less than 5 minutes out of 28 of battery life left) but there is no certainty that this time will be enough; in [3], [6], [7] mobile phone signals are exploited to localize a person, but it is not certain that people keep their cell phones close to them when they are at home, especially at night; in [8], [9] UAVs send stream videos to the base station through a cellular network in mountain environments, where usually there is no guarantee of coverage; often UAVs have to fly over an entire area and not focus on certain buildings, making it impossible to assign priorities to certain places [10], [11]; finally, the costs that a UAV will face during its trip are usually considered as fully known [12]- [14], while in real-life scenarios, a UAV could spend a time even very different from the expected one to explore or traverse an area. This paper addresses the problem of completely flying over an area just hit by an earthquake with a fleet of UAVs to opportunely direct rescue teams.…”

Joint Transmission Scheme and Coded Content Placement in Cluster-centric UAV-aided Cellular Networks

“…, (10) where p (LoS) k,j (t) is the probability of establishing LoS link between UAV u k and ground user GU j at time slot t, obtained as [15] p…”

“…Although the enhanced connectivity that comes by using UAVs will improve the QoS in outdoor areas, there are key challenges ahead in indoor environments due to attenuation of the received signal [9]. To address this issue and in line with advancements of 5G networks, the paper focuses on coupling UAVs as aerial caching nodes with Femto Access Points (FAPs) [10], equipped with storage. The ultimate goal is to increase the caching network's QoS and its coverage in a heterogeneous environment (i.e., integrated indoor and outdoor settings).…”

Development of Non Expensive Technologies for Precise Maneuvering of Completely Autonomous Unmanned Aerial Vehicles

“…For example, drones are well suited to explore or inspect large environments and buildings [ 10 , 11 , 12 , 13 ] aiming, for instance, to 3D reconstruction. Moreover, drones with high maneuverability in small spaces fits both urban missions [ 14 , 15 , 16 , 17 , 18 ], such as monitoring road traffic, and rescue missions [ 19 , 20 , 21 , 22 , 23 , 24 , 25 ], such as patrolling dangerous zone after natural disasters. In many of these applications Vision-Based Navigation (VBN) algorithms are often used to improve accuracy in the positioning [ 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 , 34 ].…”