Optimization of MANET connectivity via smart deployment/movement of unmanned air vehicles

Han, Zhu; Swindlehurst, A. Lee; Liu, K.

doi:10.1109/tvt.2009.2015953

Cited by 257 publications

(23 citation statements)

References 18 publications

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“…A three-dimensional UAV rotational and polarization model is presented in [32]. Han et al [25,33] proposed a heuristic based adaptive scheme for UAV deployment. A Delaunay triangulation mechanism is used to increase connectivity and the changing topology is monitored constantly in order to adapt to the inevitable dynamic topology.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs

Sayeed

Kumar

2018

Drones

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Multi-Unmanned Aerial Vehicle (UAV) enabled Wireless Sensor Networks (WSNs) provide a wide range of applications, covering civilian and military expeditions along with geographical navigation, control, and reconnaissance. The coordinated networks formed between the UAVs and the WSNs help in enhancing the issues related to quality as well as coverage. The overall coverage issues result in starvation as an effect of long waiting time for the nodes, while forwarding the traffic. The coverage problem can be resolved by an intelligent choice of UAV way-points. Therefore, a specialized UAV mobility model is required which takes into account the topological structure as well as the importance of strategic locations to fix UAV way-points and decide the data transmission paradigm. To resolve this problem, a novel mobility model is proposed, which takes into account the attraction factor for setting up the way-points for UAV movements. The model is capable of deciding between the locations which result in more coverage, increased throughput with lesser number of UAVs employed, as justified by the simulation results and comparative evaluations.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs

Sayeed

Kumar

2018

Drones

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“…(6)(7)(8)(9), the necessary information from the status message of each AUV must be supplied to the waypoint generator. Specifically, it is necessary for the ASV to know the location of each AUV at the beginning of the next communication cycle in order to position itself optimally for the beginning of the next set of underwater transmissions.…”

Section: A Optimal Waypoint Generatormentioning

confidence: 99%

“…(6)(7)(8)(9), consider the case n 6 (that is, five AUVs plus the ASV), 150 s, and 750 m. In this example, shown in Fig. 1, the AUVs were placed in positions that made it impossible for all AUVs to be within communication range of the ASV.…”

Section: A Optimal Waypoint Generatormentioning

confidence: 99%

Optimal Control of a Surface Vehicle to Improve Underwater Vehicle Network Connectivity

Francolin¹,

Rao²,

Duarte³

et al. 2012

Journal of Aerospace Computing, Information, and Communication

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The use of an autonomous surface vehicle as an auxiliary agent to improve connectivity in a network of autonomous underwater vehicles is investigated. An algorithm is developed that consists of an optimal waypoint generator and a minimum-time guidance law that is used to steer the vehicle to the waypoint. This algorithm is used together with a communication architecture to improve underwater vehicle connectivity in a region of interest. The approach is simulated using various underwater vehicle configurations, both with and without the autonomous surface vehicle, where it is found that network connectivity is improved significantly via the inclusion of the autonomous surface vehicle. An in-water hardware test is then performed and is shown to be consistent with the simulation results. The results of this study show that adding an autonomous surface vehicle to an underwater vehicle network can improve the connectivity of the network.

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“…But the ground nodes' communication status is not mentioned in this method. In [9], the authors study the UAVs deployment methods to improve four types of network in wireless ad hoc network. However, how the ground nodes' movement and communication requirement impact the four network connectivity are not also investigated.…”

Section: Introductionmentioning

confidence: 99%

Analyzing two connectivities in UAV-ground mobile ad hoc networks

Huo

Yuan

et al. 2011

2011 IEEE International Conference on Computer Science and Automation Engineering

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Network connectivity in UAV-ground mobile ad hoc networks is constantly changing especially when air UAVs network and ground nodes network are unmatched in movement. In this paper, two types of network connectivity are quantified: ground node to UAV connectivity and ground node to ground via connectivity. We model the primary dynamic behavior of ground nodes network's movement and communication as well as air UAVs network's movement to simulate UAV-ground mobile ad hoc network. Simulation results show that ground nodes' average speed and communication duration as well as the UAVs' trajectories update period have distinctly effect on the two connectivities.

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Optimization of MANET connectivity via smart deployment/movement of unmanned air vehicles

Cited by 257 publications

References 18 publications

An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs

An Efficient Mobility Model for Improving Transmissions in Multi-UAVs Enabled WSNs

Optimal Control of a Surface Vehicle to Improve Underwater Vehicle Network Connectivity

Analyzing two connectivities in UAV-ground mobile ad hoc networks

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