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Journal ArticleDOI

A Method for Optimized Deployment of a Network of Surveillance Aerial Drones

Andrey V. Savkin, +1 more
- 23 Apr 2019 - 
- Vol. 13, Iss: 4, pp 4474-4477
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TLDR
This letter considers the deployment problem for a network of aerial drones to maximize the coverage of an area for surveillance and monitoring and develops a distributed optimization model and a coverage maximizing algorithm.
Abstract
This letter considers the deployment problem for a network of aerial drones to maximize the coverage of an area for surveillance and monitoring. A distributed optimization model is proposed and a coverage maximizing algorithm is developed. It is proved that the proposed algorithm converges to a local maximum in a finite number of steps. Simulations with a real dataset demonstrate the effectiveness of the algorithm.

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Citations
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UAV assistance paradigm: State-of-the-art in applications and challenges

TL;DR: This comprehensive survey both studies and summarizes the existing UAV-assisted research, such as routing, data gathering, cellular communications, Internet of Things (IoT) networks, and disaster management that supports existing enabling technologies.
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An Adaptive Clustering-Based Algorithm for Automatic Path Planning of Heterogeneous UAVs

TL;DR: In this paper , an adaptive clustering-based algorithm with a symbiotic organisms search-based optimization strategy is proposed to efficiently settle the path planning problem and generate feasible paths for heterogeneous UAVs with a view to minimizing the time consumption of the search tasks.
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Coverage Path Planning of Heterogeneous Unmanned Aerial Vehicles Based on Ant Colony System

TL;DR: In this article, an ant colony system (ACS)-based algorithm was proposed to obtain good enough paths for UAVs and fully cover all regions efficiently, inspired by the foraging behavior of ants that they can obtain the shortest path between their nest and food.
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Coverage path planning of heterogeneous unmanned aerial vehicles based on ant colony system

TL;DR: In this paper , an ant colony system (ACS)-based algorithm was proposed to obtain good enough paths for UAVs and fully cover all regions efficiently, inspired by the foraging behavior of ants that they can obtain the shortest path between their nest and food.
Journal ArticleDOI

An Adaptive Clustering-Based Algorithm for Automatic Path Planning of Heterogeneous UAVs

TL;DR: An adaptive clustering-based algorithm with a symbiotic organisms search-based optimization strategy is proposed to efficiently settle the path planning problem and generate feasible paths for heterogeneous UAVs with a view to minimizing the time consumption of the search tasks.
References
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Book

Introductory Real Analysis

TL;DR: Introductory real analysis, Introductoryreal analysis , کتابخانه دیجیتال جندی شاپور اهواز
Journal ArticleDOI

Unmanned Aerial Vehicle With Underlaid Device-to-Device Communications: Performance and Tradeoffs

TL;DR: In this article, a tractable analytical framework for the coverage and rate analysis is derived for the deployment of an unmanned aerial vehicle (UAV) as a flying base station used to provide the fly wireless communications to a given geographical area is analyzed.
Journal ArticleDOI

Efficient Deployment of Multiple Unmanned Aerial Vehicles for Optimal Wireless Coverage

TL;DR: The results show that, in order to mitigate interference, the altitude of the UAVs must be properly adjusted based on the beamwidth of the directional antenna as well as coverage requirements.
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Placement Optimization of UAV-Mounted Mobile Base Stations

TL;DR: A polynomial-time algorithm with successive MBS placement, where the MBSs are placed sequentially starting on the area perimeter of the uncovered GTs along a spiral path toward the center, until all GTs are covered.
Posted Content

3D Placement of an Unmanned Aerial Vehicle Base Station (UAV-BS) for Energy-Efficient Maximal Coverage

TL;DR: In this article, the authors proposed an optimal placement algorithm for UAV-BSs that maximizes the number of covered users using the minimum transmit power, which decouple the UAV deployment problem in the vertical and horizontal dimensions without any loss of optimality.
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