Environment-Aware Deployment of Wireless Drones Base Stations with Google Earth Simulator

French, Aaron M.; Mozaffari, Mohammad; Eldosouky, AbdelRahman; Saad, Walid

doi:10.1109/percomw.2019.8730845

Cited by 14 publications

(4 citation statements)

References 22 publications

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“…In this scenario, the number of normal UAVs is fixed to N and the number of CH UAVs is K. The main objective is to determine the optimal number of CH UAVs to achieve full coverage to N users. Let γ ij is the signal to interference plus noise ratio (SINR) between UAVs i and j, and I ij be an indicator of whether or not UAV i is connected to UAV j such that [24]:…”

Section: A Efficient Deployment Of Ch Uavsmentioning

confidence: 99%

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

2020

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In Unmanned Aerial Vehicle (UAV) networks, mobility of the UAV and the corresponding network dynamics cause frequent network adaptation. One key challenge caused by this in Flying Ad-hoc Network (FANET) is how to maintain the link stability such that both the packet loss rate and network latency can be reduced. Clustering of UAVs could effectively improve the performance of large-scale UAV swarm. However, the use of conventional clustering schemes in dynamic and high mobility FANET will lead to more link outages. Besides, frequent updates of cluster structure would cause the instability of network topology and the increase of control overhead and latency. To solve this problem, we propose a locationbased k-means UAV clustering algorithms by incorporating the mobility and relative location of the UAVs to enhance the performance and reliability of the UAV network with limited resource. The objective of the proposed Mobility and Location-aware Stable Clustering (MLSC) mechanism is to enhance the stability and accuracy of the network by reducing unnecessary overheads and network latency through incorporating several design factors with minimum resource constraints. Furthermore, we derive the relationship between the maximum coverage probability of Cluster Head (CH) and cluster size to find the optimal cluster size to minimize the network overhead. Our simulation results show that the proposed MLSC scheme significantly reduces the network overheads, and also improves packet delivery ratio and network latency as compared to the conventional clustering methods. INDEX TERMS Unmanned aerial vehicles (UAVs), coverage probability, stable clustering, k-means clustering.

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Section: A Efficient Deployment Of Ch Uavsmentioning

confidence: 99%

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

2020

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“…Due to their ability to hover and their high-mobility capability without being restricted to specific routes, UAVs can provide services in hard-to-reach locations such as natural disaster sites. Considering their ease of deployment, UAVs can play a major rule in time-critical systems [8] and to provide urgent Internet and communication services when necessary [9] and [10].…”

Section: Introductionmentioning

confidence: 99%

Drones in Distress: A Game-Theoretic Countermeasure for Protecting UAVs Against GPS Spoofing

Eldosouky

Ferdowsi

Saad

2020

IEEE Internet Things J.

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115

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One prominent security threat that targets unmanned aerial vehicles (UAVs) is the capture via GPS spoofing in which an attacker manipulates a UAV's global positioning system (GPS) signals in order to capture it. Given the anticipated widespread deployment of UAVs for various purposes, it is imperative to develop new security solutions against such attacks. In this paper, a mathematical framework is introduced for analyzing and mitigating the effects of GPS spoofing attacks on UAVs. In particular, system dynamics are used to model the optimal routes that the UAVs will adopt to reach their destinations. The GPS spoofer's effect on each UAV's route is also captured by the model. To this end, the spoofer's optimal imposed locations on the UAVs, are analytically derived; allowing the UAVs to predict their traveling routes under attack. Then, a countermeasure mechanism is developed to mitigate the effect of the GPS spoofing attack. The countermeasure is built on the premise of cooperative localization, in which a UAV can determine its location using nearby UAVs instead of the possibly compromised GPS locations. To better utilize the proposed defense mechanism, a dynamic Stackelberg game is formulated to model the interactions between a GPS spoofer and a drone operator. In particular, the drone operator acts as the leader that determines its optimal strategy in light of the spoofer's expected response strategy. The equilibrium strategies of the game are then analytically characterized and studied through a novel proposed algorithm. Simulation results show that, when combined with the Stackelberg strategies, the proposed defense mechanism will outperform baseline strategy selection techniques in terms of reducing the possibility of UAV capture.

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“…In [9], a multiple drone based positioning is proposed using exact game theory and nash equilibrium in order to perform coverage maximization and power control. In [10], UAV positioning is investigated in three different approaches. First, the positions of UAVs are optimized such that the number of ground users covered by UAVs is maximized.…”

Section: Introductionmentioning

confidence: 99%

Positioning of Multiple Unmanned Aerial Vehicle Base Stations in future Wireless Network

Sivalingam¹,

Manosha²,

Rajatheva³

et al. 2019

Preprint

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Unmanned aerial vehicle (UAV) base stations (BSs) are reliable and efficient alternative to full fill the coverage and capacity requirements when the backbone network fails to provide such requirements due to disasters. In this paper, we consider optimal UAV-deployment problem in 3D space for a mmWave network. The objective is to deploy multiple aerial BSs simultaneously to completely serve the ground users. We develop a novel algorithm to find the feasible positions for a set of UAV-BSs from a predefined set of locations, subject to a signalto-interference-plus-noise ratio (SINR) constraint of every associated user, UAV-BS's limited hovering altitude constraint and restricted operating zone constraint. We cast this 3D positioning problem as an 0 minimization problem. This is a combinatorial, NP-hard problem. We approximate the 0 minimization problem as non-combinatorial 1-norm problem. Therefore, we provide a suboptimal algorithm to find a set of feasible locations for the UAV-BSs to operate. The analysis shows that the proposed algorithm achieves a set of location to deploy multiple UVA-BSs simultaneously while satisfying the constraints.

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Environment-Aware Deployment of Wireless Drones Base Stations with Google Earth Simulator

Cited by 14 publications

References 22 publications

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

Mobility and Location-Aware Stable Clustering Scheme for UAV Networks

Drones in Distress: A Game-Theoretic Countermeasure for Protecting UAVs Against GPS Spoofing

Positioning of Multiple Unmanned Aerial Vehicle Base Stations in future Wireless Network

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