Realistic mobility models are fundamental to evaluate the performance of protocols in mobile ad hoc networks. Unfortunately, there are no mobility models that capture the non-homogeneous behaviors in both space and time commonly found in reality, while at the same time being easy to use and analyze. Motivated by this, we propose a time-variant community mobility model, referred to as the TVC model, which realistically captures spatial and temporal correlations. We devise the communities that lead to skewed location visiting preferences, and time periods that allow us to model time dependent behaviors and periodic re-appearances of nodes at specific locations. To demonstrate the power and flexibility of the TVC model, we use it to generate synthetic traces that match the characteristics of a number of qualitatively different mobility traces, including wireless LAN traces, vehicular mobility traces, and human encounter traces. More importantly, we show that, despite the high level of realism achieved, our TVC model is still theoretically tractable. To establish this, we derive a number of important quantities related to protocol performance, such as the average node degree, the hitting time, and the meeting time, and provide examples of how to utilize this theory to guide design decisions in routing protocols.
Mobility models have been long recognized as one of the fundamental components that impacts the performance of wireless ad hoc networks. A wide variety of mobility models are available in the research community (see @cite_5 for a good survey). Among all mobility models, the popularity of random mobility models (e.g., random walk, random direction, and random waypoint) roots in its simplicity and mathematical tractability. A number of important properties for these models have been studied, such as the stationary nodal distribution @cite_43 , the hitting and meeting times @cite_31 , and the meeting duration @cite_35 . These quantities in turn enable routing protocol analysis to produce performance bounds @cite_34 @cite_3 . However, random mobility models are based on over-simplified assumptions, and as has been shown recently and we will also show in the paper, the resulting mobility characteristics are very different from real-life scenarios. Hence, it is debatable whether the findings under these models will directly translate into performance in real-world implementations of MANETs.
@cite_35 @cite_3 @cite_43 @cite_5 @cite_31 @cite_34
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In this paper, we analyze the mobility patterns of users of wireless hand-held PDAs in a campus wireless network using an eleven week trace of wireless network activity. Our study has two goals. First, we characterize the high-level mobility and access patterns of hand-held PDA users and compare these characteristics to previous workload mobility studies focused on laptop users. Second, we develop two wireless network topology models for use in wireless mobility studies: an evolutionary topology model based on user proximity and a campus waypoint model that serves as a trace-based complement to the random waypoint model. We use our evolutionary topology model as a case study for preliminary evaluation of three ad hoc routing algorithms on the network topologies created by the access and mobility patterns of users of modern wireless PDAs. Based upon the mobility characteristics of our trace-based campus waypoint model, we find that commonly parameterized synthetic mobility models have overly aggressive mobility characteristics for scenarios where user movement is limited to walking. Mobility characteristics based on realistic models can have significant implications for evaluating systems designed for mobility. When evaluated using our evolutionary topology model, for example, popular ad hoc routing protocols were very successful at adapting to user mobility, and user mobility was not a key factor in their performance. Mobile ad hoc networks (MANETs) are multihop networks that are capable of establishing communication in the absence of any pre-existing infrastructure. Due to frequent node mobility and unreliable wireless links, the network is characterized by unpredictable topological changes. For more robust and reliable communications, it is important that a mobile node anticipates address changes and predicts its future routes in the network. This Chapter describes prediction-based mobility management schemes for mobile ad hoc networks. We propose a Markov model-based mobility management scheme that provides an adaptive location prediction mechanism. We used simulation method to evaluate the prediction accuracy as well as the probability of making the correct predictions. The simulation results indicated that higher order Markov models have slightly greater prediction accuracy than lower order Markov models. However, the prediction accuracy decreases with increase in the probability of random movement both for network sizes and number of hops. This paper presents an analysis of the behavior of mobile ad hoc networks when group mobility is involved. We propose four different group mobility models and present a mobility pattern generator, called grcmob that we designed to be used with the ns-2 simulator. Using 2k factorial analysis we determine the most representative factors for protocol performance. We then evaluate the performance of a dynamic source routing (DSR) based MANET, using both TCP and UDP data traffic. The results are compared with the classical random waypoint mobility model. It is shown that the number of groups parameter is more important than the number of nodes one and that the impact of the area size is almost negligible. We make also evident that the mix of inter- and intra-group communication has the strongest impact on the performance. Finally, it is evidenced that the presence of groups forces the network topology to be sparser and therefore the probability of network partitions and node disconnections grows. The random waypoint model is a commonly used mobility model in the simulation of ad hoc networks. It is known that the spatial distribution of network nodes moving according to this model is, in general, nonuniform. However, a closed-form expression of this distribution and an in-depth investigation is still missing. This fact impairs the accuracy of the current simulation methodology of ad hoc networks and makes it impossible to relate simulation-based performance results to corresponding analytical results. To overcome these problems, we present a detailed analytical study of the spatial node distribution generated by random waypoint mobility. More specifically, we consider a generalization of the model in which the pause time of the mobile nodes is chosen arbitrarily in each waypoint and a fraction of nodes may remain static for the entire simulation time. We show that the structure of the resulting distribution is the weighted sum of three independent components: the static, pause, and mobility component. This division enables us to understand how the model's parameters influence the distribution. We derive an exact equation of the asymptotically stationary distribution for movement on a line segment and an accurate approximation for a square area. The good quality of this approximation is validated through simulations using various settings of the mobility parameters. In summary, this article gives a fundamental understanding of the behavior of the random waypoint model.