Towards 3D Deployment Of Uav Base Stations In Uneven Terrain
Keywords
Coverage and Connectivity; Mobile Networks; Network Performance Optimization; Unmanned Aerial Vehicles
Abstract
Unmanned Aerial Vehicles (UAVs), also known as drones, have become a new paradigm to provide emergency wireless communication infrastructure when conventional base stations are damaged or unavailable. In this paper, we propose new schemes to enable the 3D deployment of drones, which can provide network coverage and connectivity services for users located in uneven terrain. We formalize two models, including optimal coverage model and optimal connectivity model, which belong to NP-hard. To be specific, we first consider both the quality of service (QoS) requirements of users and the capacity of drones. We then formalize the problem and design a heuristic scheme, called Particle Swarm Optimization (PSO) algorithm to achieve a cost-effective solution. We also address the optimal connectivity problem in a scenario, in which a number of isolated local networks have been established by users through ad hoc communication and/or device-to-device (D2D) communication. We further develop the cost-effective heuristic algorithm to effectively minimize the total number of required drones. Via extensive performance evaluation, our experimental results demonstrate that the proposed schemes can achieve the effective deployment of drones for users in uneven terrain with respect to the number of required drones.
Publication Date
10-9-2018
Publication Title
Proceedings - International Conference on Computer Communications and Networks, ICCCN
Volume
2018-July
Document Type
Article; Proceedings Paper
Personal Identifier
scopus
DOI Link
https://doi.org/10.1109/ICCCN.2018.8487319
Copyright Status
Unknown
Socpus ID
85060436344 (Scopus)
Source API URL
https://api.elsevier.com/content/abstract/scopus_id/85060436344
STARS Citation
He, Xiaofei; Yu, Wei; Xu, Hansong; Lin, Jie; and Yang, Xinyu, "Towards 3D Deployment Of Uav Base Stations In Uneven Terrain" (2018). Scopus Export 2015-2019. 7640.
https://stars.library.ucf.edu/scopus2015/7640