Experimental characterization of UAV-to-car communications

Abstract

Unmanned Aerial Vehicles (UAVs), popularly known as drones, can be deployed in conjunction with a network of ground vehicles. In situations where no infrastructure is available, drones can be deployed as mobile infrastructure elements to offer all types of services. Examples of such services include safety in rural areas where, upon an emergency event, drones can be quickly deployed as information relays for distributing critical warning to vehicles. In this work, we analyze the communications performance on the link between cars and drones taking into account the altitude, the antenna orientation, and the relative distance. The presented results show that the communication between a drone and a car can reach up to three kilometers in a rural area, and achieves at least a fifty percent success ratio for the delivery rate at a 2.7 kilometer range. Finally, to allow integrating the communications link behaviour in different network simulators, the experimental results were also modeled with a modified Gaussian function that offers a suitable representation for this kind of communication.

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

“…For the areas with weak-connection, we list four typical regions that are construction sites in urban, disaster regions in urban, blind coverage spots in the city, and the transportation road. In these areas, some recent studies use UAVs to offer an extended network coverage and perform some specified applications such Areas with weak-connection Urban construction sites Construction project management [119]- [121] Indoor construction monitoring [122], [123] Disaster regions Disaster surveillance [80], [124], [125] Emergency networks construction [126]- [129] Urban coverage blind spots Enhanced coverage in urban area [29], [80], [130]- [133] Patrolling and surveillance [134]- [139] Transportation systems Intelligent transportation systems [140]- [143] Connection between ground vehicles [144]- [147] Areas without network deployment Farms Survey of UAV in agriculture [63], [148] Imagery analysis of crops [149]- [153] Deserts Disaster monitoring [154]- [156] Geomorphological analysis [61], [155], [157] Military detection [158] Forests Trees and plants monitoring [159]- [162] Forest growing volume prediction [163], [164] Oceans Coastal environment analysis [165]- [168] Ocean environment monitoring [169]- [171] Marine science and observation [18]...…”

3D Simulation Modeling of UAV-to-Car Communications

“…Afterwards, path loss was defined based on the geographical information that provides elevation data. By using the sample parameters used in our previous real testbeds [12], [22] (see Fig. 1), our goal is to validate the model by obtaining comparable results to achieve an improved level of realism.…”

“…In this paper, we propose and implement a novel simulation model for UAV-to-car communications. The model takes into account the results obtained from a real testbed [12], and it was developed for the OMNeT++ simulation tool [13]. The simulation model takes into account three-dimensional communications.…”

A Location-Aware Waypoint-Based Routing Protocol for Airborne DTNs in Search and Rescue Scenarios

“…It is clear that all the waypoints of the RWP model are already planned at the start of the simulation, in order to simulate a planned path for each UAV. Communication wise, we adopt the 802.11 g standard as, with respect to other technologies, it offers an excellent tradeoff between coverage and bandwidth; in our previous test [ 22 , 23 , 24 ], we were able to communicate without packet losses up to 1.5 km. Energy wise, the electromechanical part of the drone is the most consuming one, hence employing another communication technology does not have a great impact on the final outcome.…”