EA-RDSP: Energy Aware Rapidly Deployable Wireless Ad hoc System for Post Disaster Management

Khan, Ajmal; Mukhtar, Mubashir; Ullah, Farman; Bilal, Muhammad; Kwak, Kyung Sup

doi:10.32604/cmc.2021.017952

Cited by 1 publication

(1 citation statement)

References 41 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the limited power after the disaster, emitting power optimization of rapidly deployable resource units by satisfying the demand of UEs (search and rescue robots, drones, smartphones, etc.) has paid great attention [ 38 ]. In [ 39 ], authors proposed radio access control based on DRL for selecting the van type MDRUs/relay and optimizing the power of MDRUs.…”

Section: Related Workmentioning

confidence: 99%

Mobility-Aware Resource Allocation in IoRT Network for Post-Disaster Communications with Parameterized Reinforcement Learning

Kabir

Tham

Chang

et al. 2023

Sensors

View full text Add to dashboard Cite

Natural disasters, including earthquakes, floods, landslides, tsunamis, wildfires, and hurricanes, have become more common in recent years due to rapid climate change. For Post-Disaster Management (PDM), authorities deploy various types of user equipment (UE) for the search and rescue operation, for example, search and rescue robots, drones, medical robots, smartphones, etc., via the Internet of Robotic Things (IoRT) supported by cellular 4G/LTE/5G and beyond or other wireless technologies. For uninterrupted communication services, movable and deployable resource units (MDRUs) have been utilized where the base stations are damaged due to the disaster. In addition, power optimization of the networks by satisfying the quality of service (QoS) of each UE is a crucial challenge because of the electricity crisis after the disaster. In order to optimize the energy efficiency, UE throughput, and serving cell (SC) throughput by considering the stationary as well as movable UE without knowing the environmental priori knowledge in MDRUs aided two-tier heterogeneous networks (HetsNets) of IoRT, the optimization problem has been formulated based on emitting power allocation and user association combinedly in this article. This optimization problem is nonconvex and NP-hard where parameterized (discrete: user association and continuous: power allocation) action space is deployed. The new model-free hybrid action space-based algorithm called multi-pass deep Q network (MP-DQN) is developed to optimize this complex problem. Simulations results demonstrate that the proposed MP-DQN outperforms the parameterized deep Q network (P-DQN) approach, which is well known for solving parameterized action space, DQN, as well as traditional algorithms in terms of reward, average energy efficiency, UE throughput, and SC throughput for motionless as well as moveable UE.

show abstract

Section: Related Workmentioning

confidence: 99%