A Q-Learning Inspired Context-Aware Dynamic Resource Matchmaking Scheme for Critical Electric Vehicle Infrastructure

The rapid expansion of the Internet of Things (IoT)
marketplace requires intelligent and adaptable infrastructures
capable of efficient resource allocation and management among
diverse entities, such as electric vehicle charging stations and
energy providers. As the IoT marketplace increasingly intersects
with critical electricity and transportation infrastructures, the
need for coordinated, intelligent decision-making becomes more
urgent. Despite its promising potential, the IoT marketplace
currently faces critical challenges, including inefficient resource
allocation, system congestion, and unpredictable patterns of
energy demand, all of which hinder its seamless operation. In
response, this paper proposes an innovative AI-driven framework for the IoT marketplaces that integrates context-aware
techniques with Q-learning to transform resource allocation and
matchmaking processes. Using EV charging as the primary
case study, we implement context-aware similarity matching to
accurately pair EVs with optimal charging stations, while Qlearning algorithms dynamically enhance matchmaking decisions.
Our experimental results clearly demonstrate that this integrated
approach effectively reduces charging delays, optimizes energy
allocation, and substantially improves overall system efficiency.
This research marks a significant advancement toward an intelligent and agile IoT marketplace infrastructure, which addresses*Corresponding authorcritical challenges and supports the sustainable evolution of
future electricity and transportation infrastructures.