Optimizing IoT Service Placement in Fog Computing Environments Using Deep Q Networks

The rapid expansion of the Internet of Things (IoT) has created an urgent need for efficient service scheduling strategies in fog computing environments (FCEs), where computational resources are distributed across edge nodes. Traditional service placement methods often struggle to balance latency, energy consumption, and resource utilization. In this research, we deliver a new method for optimizing IoT service placement using Deep Q Networks (DQN), a reinforcement learning technique that can dynamically learn optimal placement strategies based on real-time network conditions. Our model continuously evaluates the state of the fog environment, including factors such as node load, bandwidth availability, and service demands, to intelligently place IoT services in a way that minimizes latency and maximizes resource efficiency. Experimental results demonstrate that the proposed DQN-based placement strategy outperforms conventional heuristic and rule-based methods in terms of latency reduction, energy savings, and overall system performance.