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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 |
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Journal | Proceedings - IEEE Global Communications Conference, GLOBECOM |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE Global Communications Conference, GLOBECOM 2021 - Madrid, Spain Duration: 7 Dec 2021 → 11 Dec 2021 |
Keywords
- LoRa
- energy harvesting
- reinforcement learning
- resource management
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Dive into the research topics of 'Reinforcement Learning for Hybrid Energy LoRa Wireless Networks'. Together they form a unique fingerprint.Projects
- 1 Finished
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EX-QNRF-NPRPS-38: AI-Based Next Generation Edge Platform for Heterogeneous Services using 5G Technologies
Abdallah, M. M. (Principal Investigator), Abegaz, M. S. (Post Doctoral Fellow), Hevesli, M. (Graduate Student), Student-1, G. (Graduate Student), Saad, M. R. (Consultant), Assistant-1, R. (Research Assistant), Assistant-3, R. (Research Assistant), Mohamed, D. A. (Principal Investigator), Al-Jaber, D. H. (Principal Investigator), Chiasserini, P. C. F. (Principal Investigator) & Al Fuqaha, A. (Lead Principal Investigator)
11/04/21 → 30/09/24
Project: Applied Research