Wireless multimedia sensor networks are of interest to researchers from different disciplines and many studies have been proposed in a wide variety of application domains, such as military surveillance systems, environmental monitoring, fault monitoring and distributed smart cameras in the last decade. In a wireless sensor network, a large number of sensors can be deployed to monitor target areas and autonomously collect sensor data. This produces a large amount of raw data that needs to be stored, processed, and analyzed. In this paper, we propose a graph-based big data model for simulating multimedia wireless sensor networks. The big sensor data is stored in a graph database for the purpose of advanced analytics like statistics, data mining, and prediction. A prototype implementation of the proposed model has been developed and a number of experiments have been done for measuring the accuracy and efficiency of our solution. In addition, we present a case study using the military surveillance domain with a number of complex experimental queries by using our prototype. The experimental results show that our proposed multimedia wireless sensor network model is efficient and applicable in large-scale real life applications.