An intelligent routing algorithm called Reinforcement Learning-based Congestion-Aware Routing (RLbCAR) is introduced for intelligent routing in IoT sensor networks and ensures reliable, congestion-adaptive, and computationally efficient routing in a resource-limited IoT sensor network.
In Underwater wireless sensor network attracted massive attention from researchers. In underwater wireless sensor network, manysensor nodes are distributed at different depths in the sea. Due to its complex nature, updating their location or adding new devices ispretty challenging. Due to the constraints on energy stor...
The core research goal of this paper is to optimize dynamic routing protocols to improve the performance of the classic LEACH protocol in heterogeneous WSNs through a real-time adaptive scheme, which relies on two core methods: a cluster head selection mechanism based on the residual energy criterion, and a priority ho...
Clustering and routing protocols are widely regarded as effective methods for improving energy efficiency and extending network lifetime in wireless sensor networks. However, existing protocols often insufficiently consider dynamic energy distribution and topology characteristics, which may lead to imbalanced cluster...
Simulations indicate that the proposed MSA (Mosquito Swarm Algorithm) technique is highly effective at extending network longevity and increasing throughput, making it the preferred option to lower energy consumption in WSNs.
This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks, Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony algorithm in a self-adaptive hybrid form.
We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.
