AI-Driven Dynamic Resource Allocation for IoT Networks Using Graph-Convolutional Transformer and Hybrid Optimization

Abstract

Effective resource allocation is a fundamental challenge for software systems in Internet of Things (IoT) networks, influencing their performance, energy consumption, and scalability in dynamic environments. This study introduces a new framework, DRANet–graph convolutional network (GCN)+, which integrates GCNs, transformer architectures, and reinforcement learning (RL) with adaptive metaheuristics to improve real-time decision making in IoT resource allocation. The framework employs GCNs to model spatial relationships among heterogeneous IoT devices, transformer-based architectures to capture temporal patterns in resource demands, and RL with fairness-aware reward functions to dynamically optimize allocation strategies. Unlike previous approaches, DRANet–GCN+ addresses computational overhead through efficient graph partitioning and parallel processing, making it suitable for resource-constrained environments. Comprehensive evaluation includes sensitivity analysis of key parameters and benchmarking against recent hybrid approaches, including GCN–RL and attention-enhanced multiagent RL (MARL) methods. Performance evaluation on real-world and large-scale synthetic datasets (up to 5000 nodes) demonstrates the framework’s capabilities under varied conditions, achieving 93.2% resource allocation efficiency, 50 ms average latency with 12 ms standard deviation, and 990 Mbps throughput while consuming 15% less energy than baseline approaches. These findings establish DRANet–GCN+ as a robust solution for intelligent resource management in heterogeneous IoT networks, with detailed quantification of computational overhead, scalability limitations, and fairness–energy–throughput trade-offs.