AI-Powered Wireless Charging Framework for SustainableIoT in Power Plants
DOI:
https://doi.org/10.31185/wjcms.479Keywords:
Renewable Energy, coordinate charging, Green Infrastructure, SENSE technique, Artificial Intelligent, power plant.Abstract
Despite the importance of wireless sensor networks (WSN) in the supervision of industrial
infrastructure, the use of this technology is frequently and negatively affected by the energy consumption of its use,
especially in risky areas. The current paper introduces the SENSE framework, which is an AI-based wireless power
transfer paradigm that employs three different powerful AI methods to radically improve the sustainability of energy
of the mission-critical WSNs. To begin with, proactive charging is enabled by the enhanced predictivity of a hybrid
LSTMARIMA neural network, which includes patterns of node-level energy depletion. Data confidentiality is
ensured through the use of gradient only federated updates in the system especially when it is deployed in nuclear
plants. Secondly, the deep Q -network (DQN) is based on the Voronoi partitioning and optimizes the mobile charging
paths, thus dynamically balancing the efficiency of these trips with the spatial coverage. Thirdly, federated Q-learning
introduces adaptive weight optimization in order to make privacy-saving priority scheduling possible. Photovoltaicintegrated MCUs with weather-adaptive charge schedules generate a 40 % reduction in maintenance expenses in the
case of renewable energy. The framework lays the groundwork of a new paradigm of autonomous WSN energy
management by demonstrating how coherently engineered AI subsystems can collaboratively overcome the inherent
constraints of wireless power transfer, and achieve the simultaneously competing requirements of reliability,
efficiency and privacy. In its turn, SENSE approach finds itself at the forefront of next-generation industrial IoT
systems destined to be used in the domain of monitoring the critical infrastructure, manufacturing processes and
energy.
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