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Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
by
Huang, Hailong
, Savkin, Andrey
in
aerial monitoring
/ aerial surveillance
/ Algorithms
/ Art galleries & museums
/ coverage control
/ Drones
/ flying robots
/ Health surveillance
/ Infrastructure
/ International conferences
/ internet of drones
/ internet of flying robots
/ Kershner’s theorem
/ Optimization
/ Robots
/ Sensors
/ triangular coverage
/ UAVs
/ Unmanned aerial vehicles
/ Vehicles
/ Wireless communications
2019
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Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
by
Huang, Hailong
, Savkin, Andrey
in
aerial monitoring
/ aerial surveillance
/ Algorithms
/ Art galleries & museums
/ coverage control
/ Drones
/ flying robots
/ Health surveillance
/ Infrastructure
/ International conferences
/ internet of drones
/ internet of flying robots
/ Kershner’s theorem
/ Optimization
/ Robots
/ Sensors
/ triangular coverage
/ UAVs
/ Unmanned aerial vehicles
/ Vehicles
/ Wireless communications
2019
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Do you wish to request the book?
Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
by
Huang, Hailong
, Savkin, Andrey
in
aerial monitoring
/ aerial surveillance
/ Algorithms
/ Art galleries & museums
/ coverage control
/ Drones
/ flying robots
/ Health surveillance
/ Infrastructure
/ International conferences
/ internet of drones
/ internet of flying robots
/ Kershner’s theorem
/ Optimization
/ Robots
/ Sensors
/ triangular coverage
/ UAVs
/ Unmanned aerial vehicles
/ Vehicles
/ Wireless communications
2019
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Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
Journal Article
Asymptotically Optimal Deployment of Drones for Surveillance and Monitoring
2019
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Overview
This paper studies the problem of placing a set of drones for surveillance of a ground region. The main goal is to determine the minimum number of drones necessary to be deployed at a given altitude to monitor the region. An easily implementable algorithm to estimate the minimum number of drones and determine their locations is developed. Moreover, it is proved that this algorithm is asymptotically optimal in the sense that the ratio of the number of drones required by this algorithm and the minimum number of drones converges to one as the area of the ground region tends to infinity. The proof is based on Kershner’s theorem from combinatorial geometry. Illustrative examples and comparisons with other existing methods show the efficiency of the developed algorithm.
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