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In many scenarios, control information dissemination becomes a bottleneck, which limits the scalability and the performance of wireless networks. Such a problem is especially crucial in mobile ad hoc networks, dense networks, networks of vehicles and drones, sensor networks. In other words, this problem occurs in any scenario with frequent changes in topology or interference level on one side and with strong requirements on delay, reliability, power consumption, or capacity on the other side. If the control information changes partially, it may be worth sending only differential updates instead of messages containing full information to reduce overhead. However, such an approach needs accurate tuning of dissemination parameters, since it is necessary to guarantee information relevance in error-prone wireless networks. In the paper, we provide a deep study of two approaches for generating differential updates - namely, incremental and cumulative - and compare their efficiency. We show that the incremental approach allows significantly reducing the amount of generated control information compared to the cumulative one, while providing the same level of information relevance. We develop an analytical model for the incremental approach and propose an algorithm which allows tuning its parameters, depending on the number of nodes in the network, their mobility, and wireless channel quality. Using the developed analytical model, we show that the incremental approach is very useful for static dense network deployments and networks with low and medium mobility, since it allows us to significantly reduce the amount of control information compared to the classical full dump approach.