In this work we introduce two practical and interesting models of ad-hoc mobile networks: (a) hierarchical ad-hoc networks, comprised of dense subnetworks of mobile users interconnected by a very fast yet limited backbone infrastructure, (b) highly changing ad-hoc networks, where the deployment area changes in a highly dynamic way and is unknown to the protocol. In such networks, we study the problem of basic communication, i.e., sending messages from a sender node to a receiver node. For highly changing networks, we investigate an efficient communication protocol exploiting the coordinated motion of a small part of an ad-hoc mobile network (the ldquorunners supportrdquo) to achieve fast communication. This protocol instead of using a fixed sized support for the whole duration of the protocol, employs a support of some initial (small) size which adapts (given some time which can be made fast enough) to the actual levels of traffic and the (unknown and possibly rapidly changing) network area, by changing its size in order to converge to an optimal size, thus satisfying certain Quality of Service criteria. Using random walks theory, we show that such an adaptive approach is, for this class of ad-hoc mobile networks, significantly more efficient than a simple non-adaptive implementation of the basic ldquorunners supportrdquo idea, introduced in [9,10]. For hierarchical ad-hoc networks, we establish communication by using a ldquorunnersrdquo support in each lower level of the hierarchy (i.e., in each dense subnetwork), while the fast backbone provides interconnections at the upper level (i.e., between the various subnetworks). We analyze the time efficiency of this hierarchical approach. This analysis indicates that the hierarchical implementation of the support approach significantly outperforms a simple implementation of it in hierarchical ad-hoc networks. Finally, we discuss a possible combination of the two approaches above (the hierarchical and the adaptive ones) that can be useful in ad-hoc networks that are both hierarchical and highly changing. Indeed, in such cases the hierarchical nature of these networks further supports the possibility of adaptation.