Unleashing Collaborative Potentials: Multifaceted Collaboration Among Agents in Multitask Internet of Things Networks

The rapid advancement of Internet of Things (IoT) and multiagent systems has transformed how complex IoT tasks are managed across domains. While individual edge agents demonstrate proficiency in specialized tasks such as data collection and edge learning, they encounter substantial challenges when confronting complex IoT scenarios that demand diverse skill sets. This article introduces a novel group formation framework facilitating effective IoT agent collaboration in complex task environments, including smart manufacturing, intelligent transportation, and smart cities. We propose a hybrid competition mechanism that optimizes initial multiagent cooperation strategies by integrating task requirements, agent capabilities, and system-wide performance metrics. Our approach combines intratask and intertask competition to achieve optimal agent-task matching and resource allocation in large-scale IoT networks. Through comprehensive simulations across various IoT scenarios, we demonstrate that our framework substantially enhances task completion efficiency and system performance compared to existing methods. The results confirm our approach’s effectiveness in resource-constrained environments, achieving minimal agent grouping time costs while increasing total task revenue by 30%-42% and resource utilization by 38% compared to baseline heuristic methods.