  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.
