A wireless sensor network deployed in an area of interest affected by variations in environmental conditions associated with that area. It must adapt to these variations in order to continue functioning as desired by the user. We present a novel, two-phase solution to the wireless sensor network adaptivity problem. In the first phase, nodes in the network, organized as clusters, execute an efficient algorithm to dynamically calibrate sensed data. Each node provides its current energy level and the state of each on-board sensor to a cluster-head. In the second phase, each cluster-head executes an efficient, ontology-driven algorithm to determine the future state of the network under existing conditions, based on information received from each sensor node. We describe an example application scenario to show how our two-phase solution can be employed to enable a real-world wireless sensor network to adapt itself to variations in environmental conditions.