Deep Reinforcement Learning-Based Online One-to-Multiple Charging Scheme in Wireless Rechargeable Sensor Network.

ABSTRACT: Wireless rechargeable sensor networks (WRSN) have been emerging as an effective solution to the energy constraint problem of wireless sensor networks (WSN). However, most of the existing charging schemes use Mobile Charging (MC) to charge nodes one-to-one and do not optimize MC scheduling from a more comprehensive perspective, leading to difficulties in meeting the huge energy demand of large-scale WSNs; therefore, one-to-multiple charging which can charge multiple nodes simultaneously may be a more reasonable choice. To achieve timely and efficient energy replenishment for large-scale WSN, we propose an online one-to-multiple charging scheme based on Deep Reinforcement Learning, which utilizes Double Dueling DQN (3DQN) to jointly optimize the scheduling of both the charging sequence of MC

SUBMITTER: Gong Z 

PROVIDER: S-EPMC10143104 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature