International Journal of Science, Engineering and TechnologyAuthors: Nafisa S, Dr. Balaji. K, Shruthi N
Abstract: Due to the rapid rise in the use of artificial intelligence in things (AIoT), the dynamic resource allocation problem is now more complex than ever. In this research paper, a new dynamic resource allocation framework based on multi-agent reinforcement learning (MARL) for AIoT systems is described. A CTDE architecture based on improved MAPPO (IMAPPO) is utilized, which optimizes AoI for action spaces with both discrete and continuous variables. In simulation tests, the framework has achieved 99% successful task completion with 18 MEC servers available, resulting in a minimal probability of failure of 0.01% at 30 dBm. Furthermore, comparative studies show that MARL has better performance than conventional deep Q network (DQN) and proximal policy optimization (PPO) algorithms by 22.01% and 8.26%, respectively. Moreover, a maximum cumulative reward value of 62,306.58 and 99.98% accuracy were obtained, marking a 6.9% improvement over other MARL models.
DOI:
