Energy-Efficient Network Slicing for UAV-enabled Networks

This paper investigates an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery and sensing data gathering services. Two tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, while the sensing tenant aims to gather an adequate amount of uncorrelated data. An energy consumption minimization framework is considered to meet the tenants’ requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, and the user-UAV association. Taking into consideration the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A heuristic solution approach is presented to select the suitable deployment location of the UAVs, activate the optimum subset of the sensing users, and associate the users of both tenants with the deployed UAVs. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.

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Publication Details

Published
2024-11-10
DOI
https://doi.org/10.1109/wf-iot62078.2024.10811335
Citations
2
Primary Topic
UAV Applications and Optimization
Type
article
Field-Weighted Citation Impact
2.60
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article

Energy-Efficient Network Slicing for UAV-enabled Networks

Octavia A. Dobre, Yindi Jing, Ahmed A. Al-Habob
2 citations
UAV Applications and Optimization
2.60
article

Energy-Efficient Network Slicing for UAV-enabled Networks

Octavia A. Dobre, Yindi Jing, Ahmed A. Al-Habob
article en
2 citations

Abstract

This paper investigates an unmanned aerial vehicle (UAV)-enabled network slicing problem to provide content delivery and sensing data gathering services. Two tenants provide services to their clients by sharing a common infrastructure of a set of UAVs. The content delivery tenant needs to guarantee that each of its clients (users) receives the required content, while the sensing tenant aims to gather an adequate amount of uncorrelated data. An energy consumption minimization framework is considered to meet the tenants’ requirements by optimizing the number of deployed UAVs, the deployment location of each UAV, the transmit power of each deployed UAV, and the user-UAV association. Taking into consideration the spatial correlation among the sensing users, a subset of these users is activated to gather the required sensing information. A heuristic solution approach is presented to select the suitable deployment location of the UAVs, activate the optimum subset of the sensing users, and associate the users of both tenants with the deployed UAVs. Simulation results illustrate that the proposed framework significantly reduces the total energy consumption.

Memorial University of Newfoundland (CA), University of Alberta (CA)
Affordable and clean energy
Openalex Percentile: Top 8%
UAV Applications and Optimization
2.60
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