Reinforcement Learning for Hybrid Energy LoRa Wireless Networks

Rami Hamdi; Emna Baccour; Aiman Erbad; Marwa Qaraqe; Mounir Hamdi

doi:10.1109/GLOBECOM46510.2021.9685784

Reinforcement Learning for Hybrid Energy LoRa Wireless Networks

Rami Hamdi, Emna Baccour, Aiman Erbad, Marwa Qaraqe, Mounir Hamdi

Engineering

Hamad bin Khalifa University

Research output: Chapter in Book/Report/Conference proceeding › Published conference contribution

1 Citation (Scopus)

Abstract

LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

Original language	English
Title of host publication	2021 IEEE Global Communications Conference (GLOBECOM)
Publisher	Institute of Electrical and Electronics Engineers Inc.
Number of pages	7
ISBN (Electronic)	9781728181042
DOIs	https://doi.org/10.1109/GLOBECOM46510.2021.9685784
Publication status	Published - 2 Feb 2021
Event	2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain Duration: 7 Dec 2021 → 11 Dec 2021

Conference

Conference	2021 IEEE Global Communications Conference, GLOBECOM 2021
Country/Territory	Spain
City	Madrid
Period	7/12/21 → 11/12/21

Bibliographical note

Funding Information:
ACKNOWLEDGMENT This work was made possible by NPRP-Standard (NPRP-S) Thirteen (13th) Cycle grant # NPRP13S-0205-200265 from the Qatar National Research Fund (QNRF) (a member of Qatar Foundation) and the TÜBITAK—QNRF Joint Funding Program grant (AICC03-0324-200005) from the Scientific and Technological Research Council of Turkey and QNRF. The findings herein reflect the work, and are solely the responsibility, of the authors.

Keywords

energy harvesting
LoRa
reinforcement learning
resource management

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/GLOBECOM46510.2021.9685784Licence: Unspecified

Cite this

Hamdi, R, Baccour, E, Erbad, A, Qaraqe, M & Hamdi, M 2021, Reinforcement Learning for Hybrid Energy LoRa Wireless Networks. in 2021 IEEE Global Communications Conference (GLOBECOM). Institute of Electrical and Electronics Engineers Inc., 2021 IEEE Global Communications Conference, GLOBECOM 2021, Madrid, Spain, 7/12/21. https://doi.org/10.1109/GLOBECOM46510.2021.9685784

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title = "Reinforcement Learning for Hybrid Energy LoRa Wireless Networks",

abstract = "LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.",

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author = "Rami Hamdi and Emna Baccour and Aiman Erbad and Marwa Qaraqe and Mounir Hamdi",

note = "Funding Information: ACKNOWLEDGMENT This work was made possible by NPRP-Standard (NPRP-S) Thirteen (13th) Cycle grant # NPRP13S-0205-200265 from the Qatar National Research Fund (QNRF) (a member of Qatar Foundation) and the T{\"U}BITAK—QNRF Joint Funding Program grant (AICC03-0324-200005) from the Scientific and Technological Research Council of Turkey and QNRF. The findings herein reflect the work, and are solely the responsibility, of the authors.; 2021 IEEE Global Communications Conference, GLOBECOM 2021 ; Conference date: 07-12-2021 Through 11-12-2021",

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AU - Hamdi, Mounir

N1 - Funding Information: ACKNOWLEDGMENT This work was made possible by NPRP-Standard (NPRP-S) Thirteen (13th) Cycle grant # NPRP13S-0205-200265 from the Qatar National Research Fund (QNRF) (a member of Qatar Foundation) and the TÜBITAK—QNRF Joint Funding Program grant (AICC03-0324-200005) from the Scientific and Technological Research Council of Turkey and QNRF. The findings herein reflect the work, and are solely the responsibility, of the authors.

PY - 2021/2/2

Y1 - 2021/2/2

N2 - LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

AB - LoRa supports the exponential growth of connected devices. In this paper, we investigate green LoRa wireless networks powered by both the grid power and a renewable energy source. The grid power compensates for the randomness and intermittency of the harvested energy. We propose an efficient and smart resource management scheme of the limited number of channels and spreading factors (SFs) with the objective of improving the LoRa gateway (LG) energy efficiency. We formulate the problem of grid power consumption minimization while satisfying the quality of service demands. The optimal resource management problem is solved by decoupling the formulated problem into two sub-problems: channel and SF assignment problem and energy management problem. Next, we develop an adaptable resource management schemes based on Reinforcement Learning (RL) taking into account the channel and energy correlation. Simulations results show that the proposed resource management schemes offer efficient use of renewable energy in LoRa wireless networks.

KW - energy harvesting

KW - LoRa

KW - reinforcement learning

KW - resource management

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DO - 10.1109/GLOBECOM46510.2021.9685784

M3 - Published conference contribution

AN - SCOPUS:85127288888

BT - 2021 IEEE Global Communications Conference (GLOBECOM)

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE Global Communications Conference, GLOBECOM 2021

Y2 - 7 December 2021 through 11 December 2021

ER -

Reinforcement Learning for Hybrid Energy LoRa Wireless Networks

Abstract

Conference

Bibliographical note

Keywords

UN SDGs

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