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.
A different approach to mobility modeling is by empirical mobility trace collection . Along this line, researchers have exploited existing wireless network infrastructure, such as wireless LANs (e.g., @cite_19 @cite_28 @cite_24 ) or cellular phone networks (e.g., @cite_4 ), to track user mobility by monitoring their locations. Such traces can be replayed as input mobility patterns for simulations of network protocols @cite_27 . More recently, DTN-specific testbeds @cite_7 @cite_23 @cite_20 aim at collecting encounter events between mobile nodes instead of the mobility patterns. Some initial efforts to mathematically analyze these traces can be found in @cite_7 @cite_25 . Yet, the size of the traces and the environments in which the experiments are performed can not be adjusted at will by the researchers. To improve the flexibility of traces, the approach of trace-based mobility models have also been proposed @cite_26 @cite_6 @cite_45 . These models discover the underlying mobility rules that lead to the observed properties (such as the duration of stay at locations, the arrival patterns, etc.) in the traces. Statistical analysis is then used to determine proper parameters of the model to match it with the particular trace.
@cite_26 @cite_4 @cite_7 @cite_28 @cite_6 @cite_24 @cite_19 @cite_27 @cite_45 @cite_23 @cite_25 @cite_20
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In this paper we present a trace-driven framework capable of building realistic mobility models for the simulation studies of mobile systems. With the goal of realism, this framework combines coarse-grained wireless traces, i.e., association data between WiFi users and access points, with an actual map of the space over which the traces were collected. Through a sequence of data processing steps, including filtering the data trace and converting the map to a graph representation, this framework generates a probabilistic mobility model that produces user movement patterns that are representative of real movement. This is done by adopting a set of heuristics that help us infer the paths users take between access points. We describe our experience applying this approach to a college campus, and study a number of properties of the trace data using our framework. 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. Validation of mobile ad hoc network protocols relies almost exclusively on simulation. The value of the validation is, therefore, highly dependent on how realistic the movement models used in the simulations are. Since there is a very limited number of available real traces in the public domain, synthetic models for movement pattern generation must be used. However, most widely used models are currently very simplistic, their focus being ease of implementation rather than soundness of foundation. As a consequence, simulation results of protocols are often based on randomly generated movement patterns and, therefore, may differ considerably from those that can be obtained by deploying the system in real scenarios. Movement is strongly affected by the needs of humans to socialise or cooperate, in one form or another. Fortunately, humans are known to associate in particular ways that can be mathematically modelled and that have been studied in social sciences for years.In this paper we propose a new mobility model founded on social network theory. The model allows collections of hosts to be grouped together in a way that is based on social relationships among the individuals. This grouping is then mapped to a topographical space, with movements influenced by the strength of social ties that may also change in time. We have validated our model with real traces by showing that the synthetic mobility traces are a very good approximation of human movement patterns. In wireless networking R&D we typically depend on experimentation to further evaluate a solution, as simulation is inherently a simplification of the real-world. However, experimentation is limited in aspects where simulation excels, such as repeatability and reproducibility. Real wireless experiments are hardly repeatable. Given the same input they can produce very different output results, since wireless communications are influenced by external random phenomena such as noise, interference, and multipath. Real experiments are also difficult to reproduce due to testbed operational constraints and availability. We have previously proposed the Trace-based Simulation (TS) approach, which uses the TraceBasedPropagationLossModel to successfully reproduce past experiments. Yet, in its current version, the TraceBasedPropagationLossModel only supports point-to-point scenarios. In this paper, we introduce a new version of the model that supports Multiple Access wireless scenarios. To validate the new version of the model, the network throughput was measured in a laboratory testbed. The experimental results were then compared to the network throughput achieved using the ns-3 trace-based simulation and a pure ns-3 simulation, confirming the TS approach is valid for multiple access scenarios too.