Fog Node Planning With Stochastic Sensor Traffic in Dynamic Industrial Environment

The emergence of Industrial Internet of Things along with fog computing (FC) has brought great benefits in the industry field through real-time monitoring, resource optimization configuration, and intelligent cloud control. The deployment of fog nodes in industrial plants is the essential precondition for FC implementation. Existing works are mainly based on pregiven sensor traffic in the deployment of fog nodes, which largely ignores the stochastic uncertainty imposed by the dynamic industrial environment. In this article, without requiring a priori knowledge of the sensor traffic pattern, we establish a mathematical model based on uncertain programming to formulate the fog node location determination and the sensor association problem. Owing to the complexity of the model, we introduce a learning-based algorithm to effectively solve the problem with an acceleration mechanism. Finally, intensive simulations are implemented to verify the performance of the algorithm. The results indicate that our approach outperforms other benchmarks in terms of transmission energy on planning fog nodes in industrial plants with stochastic sensor traffic.