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Energy and service-priority aware trajectory design for UAV-BSs using double Q-learning

conference contribution
posted on 08.09.2021, 05:12 by Sayed Amir HoseiniSayed Amir Hoseini, Ayub BokaniAyub Bokani, Jahan HassanJahan Hassan, Shavbo Salehi, Salil S Kanhere
Next generation mobile networks have proposed the integration of Unmanned Aerial Vehicles (UAVs) as aerial base stations (UAV-BS) to serve ground nodes. Despite the advantages of UAV-BSs, their dependence on the on-board, limited-capacity battery hinders their service continuity. Shorter trajectories can save flying energy, however UAV-BSs must also serve nodes based on their service priority since nodes’ service requirements are not always the same. In this paper, we present an energy-efficient trajectory optimization for a UAV assisted IoT system in which the UAV-BS considers the IoT nodes’ service priorities in making its movement decisions. We solve the trajectory optimization problem using Double Q-Learning algorithm. Simulation results reveal that the Q-Learning based optimized trajectory outperforms a benchmark algorithm, namely Greedily served algorithm, in terms of reducing the average energy consumption of the UAVBS as well as the service delay for high priority nodes.

History

Volume

abs/2010.13346

Start Page

1

End Page

4

Number of Pages

4

Start Date

09/01/2021

Finish Date

12/01/2021

ISBN-13

9781728197944

Location

Online

Publisher

IEEE Xplore

Place of Publication

Online

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

UNSW Sydney; Urmia University, Iran

Era Eligible

Yes

Name of Conference

18th IEEE Annual Consumer Communications & Networking Conference (CCNC 2021)

Parent Title

2021 IEEE 18th Annual Consumer Communications and Networking Conference, CCNC 2021