In wireless sensor networks, the certainty of the data created by a node can change due to many reasons such as drained energy, outer factors. In addition dynamic physical environment and hardware failures (broken sensor etc.) might cause sensors to produce erred or incomplete data. Therefore raw wireless sensor network data reflects an approximate observation of the monitored environment and it can be faulty or partially wrong. Wireless sensor network applications that are used in military and health systems are used in critical decision-making. To make a healthy decision and to analyze the data correctly, the end users have to know the uncertainty level of the collected data. As a result, a model that can represent the uncertainty associated with different types of sensor data in varying models is needed. There has been some efforts of modeling uncertainty in database community. We aim to develop an uncertainty-modeling framework to aid decision-making. The methodology that we will use in this work is as follows (1) developing an uncertainty model by referring to database literature (2) designing the algorithms and equations (3) modeling finite state machines to characterize the behavior of the nodes and the transitions between different uncertainty states (4) improving a distributed architecture that defines the actions taken by the network based on the uncertainty levels (such as omitting a node with a low uncertainty level and replacing it) (5) calculating the energy and memory requirements of our architecture by carrying simulations. We have done previous work on trust computation of wireless sensor networks. In this position paper, we give a general overview of the planned research.