A task classification strategy for IoT/Cloud collaboration

Abstract

There is a growing need for efficient computational offloading technologies that can handle large data flows while adhering to strict response time and power consumption constraints as a result of the explosive growth of Internet of Things (IoT) devices. Real-time IoT applications require scalability and responsiveness, which traditional cloud computing cannot provide. To address these issues, this thesis presents a task offloading framework based on Deep Q-Network (DQN) in a collaborative architecture between IoT, fog computing, and cloud computing levels for intelligent and adaptive decision-making that maximizes system performance. The DQNbased strategy achieves superior performance, with reductions of up to 50% in total cost, 33% in latency, and 25% in energy consumption compared to competing methods, according to extensive simulation experiments against state-of-the-art algorithms, such as bat, DJA, and DDPG-based approaches. The DQN algorithm also shows strong convergence behavior, low variance, and high reliability across multiple scenarios, confirming its robustness and adaptability in distributed computing environments. In order to improve Quality of Service (QoS) and Quality of Experience (QoE) in IoT–fog–cloud ecosystems, this work offers a scalable, data-driven offloading solution, which advances the expanding field of intelligent task management. The suggested framework opens perspectives for more independent and energy-conscious computing paradigms by laying the groundwork for future studies on multiagent deep reinforcement learning, federated offloading techniques, and optical network-enhanced IoT infrastructures.