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Task-centric wireless sensor network environments are often characterized by the simultaneous operation of multiple tasks. Individual tasks compete for constrained resources and thus need resource mediation algorithms at two levels. First, different sensors must be allocated to different tasks based on the combination of sensor attributes and task requirements. Subsequently, sensor data rates on various data routes must be dynamically adapted to share the available wireless bandwidth, especially when links experience traffic congestion. In this paper we investigate heuristics for incrementally modifying the sensor-task matching process to incorporate changes in the transport capacity constraints or feasible task utility values.