Agents acting in physical space use perception in combination with their own world models and shared context models to orient. The shared context models have to be adapted permanently to the conditions of the real world in dynamic environments. If a measurement of an agent's sensor does not fit to the corresponding data in the shared context model we get an inconsistency. In this case it is necessary to decide whether the reason for the discrepancy is a change in the real world or a measurement error. If there is a change in the real world the shared context model has to be corrected. If the measurement is erroneous it could be necessary to check the sensor. A single agent can hardly answer this question using it's local world model. Extensive knowledge about the represented world and the utilized sensors would be necessary. In this work we examine the scenario of a context model that is shared with and updated by many agents. In occurrence of an inconsistency it is possible to call other agents to validate a measurement. The functions to call the other agents are provided by the Nexus platform, a federation of systems that manages users and objects in shared dynamic context models, integrates web technologies and interfaces to the world wide web. We describe and evaluate procedures that combine a multitude of measurements to a single result that can be integrated in the shared context model. We enhanced the statistically optimized procedure based on ratings of the participating agents. The procedure is altered so that the system can recover from running into singularities caused by the feedback from the ratings. In addition we describe a preprocessing based on fuzzy clustering that detects aberrant measurements which are excluded from further processing.