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Epidemic-based communications, or 'gossiping', provides a robust and scalable method for maintaining a knowledge base in a sensor network faced with an unpredictable network environment. Since sensed information is often periodic in time, protocols should be able to manage multiple messages in an efficient way. We describe a mathematical model of gossiping dealing with multiple messages. We present simulation results that suggest the model can provide insights into the design and optimisation of sensor networks in the case of dissemination of periodically generated data. We show that it is possible to control data freshness without increasing overhead, and quantify the importance of topology in achieving timely dissemination.