Measuring diversity on a low-altitude UAV in a ground-to-air wireless 802.11 mesh network

Kung, H. T.; Lin, Chit-Kwan; Lin, Tsung-Han; Tarsa, Stephen J.; Vlah, Dario

doi:10.1109/glocomw.2010.5700251

Cited by 45 publications

(25 citation statements)

References 10 publications

(8 reference statements)

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“…In a previous publication [5] we reported in detail on a set of UAV field experiments where we measured the ground-to-UAV link qualities; we briefly summarize the experiments here. Figure 5 shows the cyclic UAV trajectory traversed during one 16 minute flight.…”

Section: Simulation Results For Real-world Uav Linksmentioning

confidence: 99%

Collaborative compressive spectrum sensing in a UAV environment

Chen

Kung

Vlah

et al. 2011

2011 - MILCOM 2011 Military Communications Conference

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Abstract-Spectrum sensing is of fundamental importance to many wireless applications including cognitive radio channel assignment and radiolocation. However, conventional spectrum sensing can be prohibitively expensive in computation and network bandwidth when the bands under scanning are wide and highly contested. In this paper we propose distributed spectrum sensing with multiple sensing nodes in a UAV environment. The ground nodes in our scheme sense the spectrum in parallel using compressive sensing. Each sensor node transmits compressive measurements to a nearby UAV in the air. The UAV performs decoding on the received measurements; it decodes information with increasing resolution as it receives more measurements. Furthermore, by a property of compressive sensing decoding, frequencies of large magnitude responses are recovered first. In the proposed scheme, as soon as the UAV detects the presence of such high-power frequencies from a sensor, this information is used to aid decoding for other sensors. We argue that such collaboration enabled by UAV will greatly enhance the decoding accuracy of compressive sensing. We use packet-loss traces acquired in UAV flight experiments in the field, as well as field experiments involving software-defined radios, to validate the effectiveness of this distributed compressive sensing approach.

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Section: Simulation Results For Real-world Uav Linksmentioning

confidence: 99%

Collaborative compressive spectrum sensing in a UAV environment

Chen

Kung

Vlah

et al. 2011

2011 - MILCOM 2011 Military Communications Conference

Self Cite

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“…In Figure 4(b) we further investigate this issue, by varying the altitude h from ground of the aerial mesh. We consider a modified version of the Suburban scenario (medium building density) with building heights uniformly chosen in the range [10,20] m. This result clearly demonstrates that the CI is affected by the altitude h more than the number of RUs. Indeed, CI value significantly increases when h ≥ 15m, i.e.…”

Section: Performance Evaluationmentioning

confidence: 93%

Self-organizing aerial mesh networks for emergency communication

Felice

Trotta

Bedogni

et al. 2014

2014 IEEE 25th Annual International Symposium on Personal, Indoor, and Mobile Radio Communication (PIMRC)

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Abstract-Guaranteeing network connectivity in post-disaster scenarios is challenging yet crucial to save human lives and to coordinate the operations of first responders. In this paper, we investigate the utilization of low-altitude aerial mesh networks composed by Small Unmanned Aerial Vehicles (SUAVs) in order to re-enstablish connectivity among isolated end-user (EU) devices located on the ground. Aerial ad-hoc networks provide the advantage to be deployable also on critical scenarios where terrestrial mobile devices might not operate, however their implementation is challenging from the point of view of mobility management and of coverage lifetime. In this paper, we address both these issues with three novel research contributions. First, we propose a distributed mobility algorithm, based on the virtual spring model, through which the SUAV-based mesh node -called also Repairing Units (RUs) in this study-can self-organize into a mesh structure by guaranteeing Quality of Service (QoS) over the aerial link, and connecting the maximum number of EU devices. Second, we evaluate our scheme on a realistic 3D environment with buildings, and we demonstrate the effectiveness of the aerial deployment compared to a terrestrial one, in terms of coverage and wireless link reliability. Third, we address the problem of energy lifetime, and we propose a distributed charging scheduling scheme, through which a persistent coverage of RUs can be guaranteed over the emergency scenario.

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“…In general, we can divide UAVs or drones into two types: helicopter drones and winged drones [21][22][23]. The helicopter drone uses propellers to fly and has a similar design and behavior as helicopters.…”

Section: Drone Typesmentioning

confidence: 99%

Designing UAV-aided emergency networks for large-scale disaster scenarios

Deruyck

Wyckmans

Joseph

et al. 2018

J Wireless Com Network

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Today's wireless communication networks are very reliable. However, in case of a disaster, these networks can be overwhelmed by a tremendous amount of requests which they can not cope with. We propose a deployment tool for UAV (unmanned aerial vehicle)-aided emergency networks for such disaster scenarios. By using UAVs, femtocell base stations will be brought to and hovered at their assigned location. We applied this deployment tool on a realistic disaster scenario in the city center of Ghent, Belgium. The results are very promising although a large amount of drones (> 1000 type 1 or > 370 type 2 drones) is required to provide full coverage for 1 h. Halving the user coverage results in 1.8 to 2 times less drones. More effectively is to increase the drone's fly height. A 10-m higher fly height can result in a reduction up to 13%. However, above 100 m, the influence is not significant any more. Decreasing the user's service level has no significant influence on the number of required drones for the considered scenario. Furthermore, a prediction model for the number of required drones based on the intervention duration and the user coverage is proposed.

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Measuring diversity on a low-altitude UAV in a ground-to-air wireless 802.11 mesh network

Cited by 45 publications

References 10 publications

Collaborative compressive spectrum sensing in a UAV environment

Collaborative compressive spectrum sensing in a UAV environment

Self-organizing aerial mesh networks for emergency communication

Designing UAV-aided emergency networks for large-scale disaster scenarios

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