ASGRR: Adaptive Swarm-Guided Graph Policy Routing for energy-efficient WSN-IoT networks

Abstract

Wireless Sensor Networks (WSNs) integrated with the Internet of Thing (IoT) face critical challenges. These include limited energy resources leading to rapid node depletion, dynamic topologies due to node failures or mobility disrupting connectivity, and fluctuating traffic demands in applications like healthcare, smart grids, and environmental monitoring. These factors collectively result in reduced reliability, increased latency, and scalability limitations. These issues are exacerbated by unpredictable node behavior, heterogeneous device capabilities, and the need for real-time data delivery in mission-critical scenarios, necessitating robust, adaptive routing solutions. This paper proposes ASGRR (Adaptive Swarm-Guided Graph Policy Routing), a novel hybrid routing framework that integrates Message Passing Neural Networks (MPNN), Policy Gradient Reinforcement Learning (PGRL), and the Artificial Bee Colony (ABC) algorithm in a self-adaptive hybrid form. The MPNN employs a long-term memory module to predict topology changes, capturing complex spatial-temporal node relationships. The Policy Gradient method uses shared experience memory and predictive link stability metric to optimize multi-criteria path selection. The Bees Algorithm leverages entropy-based dynamic clustering to refine paths locally, adjusting scout bee allocation based on network volatility. ASGRR dynamically tunes parameters to adapt to real-time network changes. Simulation analysis demonstrates up to 38.06% improvement in residual energy, 14.88% reduction in Delay, and 13.49% increase in Packet delivery ratio compared to MPSORP, BWOA + FIS, and FC-CRA. ASGRR is highly suitable for energy-constrained, dynamic WSN-IoT applications like healthcare and smart grids.