SADQN-Based Residual Energy-Aware Beamforming for LoRa-Enabled RF Energy Harvesting for Disaster-Tolerant Underground Mining Networks

Details

• Journal Article:
  Sensors
• Personal Author:
  Anabi, Hilary K. ; Frimpong, Samuel ; Madria, Sanjay
• Description:
  The end-to-end efficiency of radio-frequency (RF)-powered wireless communication networks (WPCNs) in post-disaster underground mine environments can be enhanced through adaptive beamforming. The primary challenges in such scenarios include (i) identifying the most energy-constrained nodes, i.e., nodes with the lowest residual energy to prevent the loss of tracking and localization functionality; (ii) avoiding reliance on the computationally intensive channel state information (CSI) acquisition process; and (iii) ensuring long-range RF wireless power transfer (LoRa-RFWPT). To address these issues, this paper introduces an adaptive and safety-aware deep reinforcement learning (DRL) framework for energy beamforming in LoRa-enabled underground disaster networks. Specifically, we develop a Safe Adaptive Deep Q-Network (SADQN) that incorporates residual energy awareness to enhance energy harvesting under mobility, while also formulating a SADQN approach with dual-variable updates to mitigate constraint violations associated with fairness, minimum energy thresholds, duty cycle, and uplink utilization. A mathematical model is proposed to capture the dynamics of post-disaster underground mine environments, and the problem is formulated as a constrained Markov decision process (CMDP). To address the inherent NP hardness of this constrained reinforcement learning (CRL) formulation, we employ a Lagrangian relaxation technique to reduce complexity and derive near-optimal solutions. Comprehensive simulation results demonstrate that SADQN significantly outperforms all baseline algorithms: increasing cumulative harvested energy by approximately 11% versus DQN, 15% versus Safe-DQN, and 40% versus PSO, and achieving substantial gains over random beamforming and non-beamforming approaches. The proposed SADQN framework maintains fairness indices above 0.90, converges 27% faster than Safe-DQN and 43% faster than standard DQN in terms of episodes, and demonstrates superior stability, with 33% lower performance variance than Safe-DQN and 66% lower than DQN after convergence, making it particularly suitable for safety-critical underground mining disaster scenarios where reliable energy delivery and operational stability are paramount. Description provided by NIOSH
• Subjects:
  • Mining
  • Occupational Health
  • Radio Waves
  • Safety
• Keywords:
  • Sensors; Underground mines; Communications equipment; Mine disasters; Radio waves; Mining engineering;
  • Author Keywords: adaptive beamforming; energy beamforming; EB; Safe Deep Q-Network; Safe-DQN; energy harvesting; EH; Long Range; LoRa; wireless power transfer; WPT; reinforcement learning; RL
• Source:
  Sensors 2026 Jan; 26(1):730
• ISSN:
  1424-8220
• Document Type:
  Text
• Funding:
  Cooperative Agreement
• Genre:
  Journal Article
• Place as Subject:
  Kansas City Region OSHA ; Missouri
• CIO:
  National Institute for Occupational Safety and Health (NIOSH)
• Topic:
  Workplace Safety & Health
• Location:
  North America
• Pages in Document:
  33 pdf pages
• Volume:
  26
• NIOSHTIC Number:
  nn:20071671
• Contact Point Address:
  Hilary Kelechi Anabi, Department of Mining and Explosives Engineering, Missouri University of Science and Technology, Rolla, MO 65409, USA
• Email:
  ha5wv@mst.edu
• Federal Fiscal Year:
  2026
• Performing Organization:
  Missouri University of Science and Technology
• Peer Reviewed:
  True
• Start Date:
  20210901
• End Date:
  20250831
• Download URL:
  https://stacks.cdc.gov/view/cdc/259443/cdc_259443_DS1.pdf
• File Type:
  [PDF - 3.63 MB]
• Collection(s):
  National Institute for Occupational Safety and Health
• Main Document Checksum:
  urn:sha-512:38d0983c944d651b7b74faa5db3b20342b6275b01ec6e3e253211634477affeb8aca366b870fbfa9a5b67115ba5cda0105160ac32f9290f18a4bba959b8d129a