Sensor network traffic load prediction with Markov random field theory

Following recent advances in wireless communications and computing technology, sensor networks are widely deployed in different fields for both monitoring and control purposes. In this work, we focus on using Markov random field (MRF) theory to model traffic intensity of the three types of sensor networks. Shortest path routing is adopted in the three typical lattice network models. Then, the influences, which affect the traffic distribution dynamically in real situations, are modelled by adding the Gaussian noise to the traffic load distribution in the MATLAB simulation. Given measurements of real-time samples of traffic, we are able to predict the traffic at each sensor node for specific network models by a MRF smoothing algorithm.