Harnessing Large Language Models for Intelligent Resource Allocation in the Internet of Everything

As the Internet of Everything (IoE) grows, managing resources across massive, heterogeneous, and dynamic devices becomes increasingly complex. Traditional reinforcement learning (RL) algorithms often struggle to capture the intricate relationships between task requirements and network states, leading to slow convergence and poor generalization. This paper investigates whether integrating Large Language Models (LLMs) into the resource scheduling loop can enhance decision-making efficiency and adaptability in these challenging environments.

The authors propose the LLM-Guided Prompt Reinforcement Optimizer (LLM-PRO), a hierarchical architecture consisting of three main components: an LLM-based semantic engine, a local RL agent, and an external evaluation-feedback module. The LLM acts as a global policy guide, translating environmental states and task requirements into heuristic Q-value vectors that inform the RL agent's decision-making process. To ensure robustness, an external evaluator performs real-time constraint verification and performance estimation, feeding this data back into the LLM to refine future prompts and reward functions. This creates a closed-loop system where the LLM provides high-level semantic guidance while the RL agent handles low-level, real-time scheduling.

Simulation results indicate that the LLM-PRO framework outperforms traditional RL-based resource allocation schemes. By leveraging the LLM's reasoning capabilities to provide heuristic guidance, the system achieves faster convergence, lower processing latency, and reduced energy consumption. The integration of the external evaluation module ensures that the scheduling policies remain feasible and adaptive, effectively managing the trade-offs between computational, storage, and bandwidth resources in dynamic IoE scenarios.