E. Eldeeb, J. M. d. S. Sant'Ana, D. E. Pérez, M. Shehab, N. H. Mahmood and H. Alves, "Multi-UAV Path Learning for Age and Power Optimization in IoT With UAV Battery Recharge," in IEEE Transactions on Vehicular Technology, vol. 72, no. 4, pp. 5356-5360, April 2023, doi: 10.1109/TVT.2022.3222092.
Multi-UAV path learning for age and power optimization in IoT with UAV battery recharge
|Author:||Eldeeb, Eslam1; de Souza Sant’Ana, Jean Michel1; Echevarría Pérez, Dian1;|
1Centre for Wireless Communications (CWC), University of Oulu, Finland
|Online Access:||PDF Full Text (PDF, 1.2 MB)|
|Persistent link:|| http://urn.fi/urn:nbn:fi-fe2023032833403
Institute of Electrical and Electronics Engineers,
|Publish Date:|| 2023-03-28
In many emerging Internet of Things (IoT) applications, the freshness of the is an important design criterion. Age of Information (AoI) quantifies the freshness of the received information or status update. This work considers a setup of deployed IoT devices in an IoT network; multiple unmanned aerial vehicles (UAVs) serve as mobile relay nodes between the sensors and the base station. We formulate an optimization problem to jointly plan the UAVs’ trajectory, while minimizing the AoI of the received messages and the devices’ energy consumption. The solution accounts for the UAVs’ battery lifetime and flight time to recharging depots to ensure the UAVs’ green operation. The complex optimization problem is efficiently solved using a deep reinforcement learning algorithm. In particular, we propose a deep Q-network, which works as a function approximation to estimate the state-action value function. The proposed scheme is quick to converge and results in a lower ergodic age and ergodic energy consumption when compared with benchmark algorithms such as greedy algorithm (GA), nearest neighbour (NN), and random-walk (RW).
IEEE transactions on vehicular technology
|Pages:||5356 - 5360|
|Type of Publication:||
A1 Journal article – refereed
|Field of Science:||
213 Electronic, automation and communications engineering, electronics
This work has been partially supported by Academy of Finland 6G Flagship program (Grant no. 346208), FIREMAN (Grant no. 326301), and the European Commission through the Horizon Europe project Hexa-X (Grant Agreement no. 101015956)
|EU Grant Number:||
(101015956) Hexa-X - A flagship for B5G/6G vision and intelligent fabric of technology enablers connecting human, physical, and digital worlds
|Academy of Finland Grant Number:||
346208 (Academy of Finland Funding decision)
326301 (Academy of Finland Funding decision)
© The Author(s) 2022. This work is licensed under a Creative Commons Attribution 4.0 License. For more information, see https://creativecommons.org/licenses/by/4.0.