We conduct the most comprehensive study of WLAN traces to date. Measurements collected from four major university campuses are analyzed with the aim of developing fundamental understanding of realistic user behavior in wireless networks. Both individual user and inter-node (group) behaviors are investigated and two classes of metrics are devised to capture the underlying structure of such behaviors. For individual user behavior we observe distinct patterns in which most users are 'on' for a small fraction of the time, the number of access points visited is very small and the overall on-line user mobility is quite low. We clearly identify categories of heavy and light users. In general, users exhibit high degree of similarity over days and weeks. For group behavior, we define metrics for encounter patterns and friendship. Surprisingly, we find that a user, on average, encounters less than 6 of the network user population within a month, and that encounter and friendship relations are highly asymmetric. We establish that number of encounters follows a biPareto distribution, while friendship indexes follow an exponential distribution. We capture the encounter graph using a small world model, the characteristics of which reach steady state after only one day. We hope for our study to have a great impact on realistic modeling of network usage and mobility patterns in wireless networks.
Recent research works on protocol design in wireless networks usually utilize synthetic, random mobility models for performance evaluation @cite_9 , such as random waypoint model or random walk model. MNs in such synthetic models are always on and homogeneous in their behavior. Both of these characteristics are not observed in real wireless traces. We argue that to better serve the purpose of testing new protocols, we need models that capture on-off and heterogeneous behavior we observed from the traces.
@cite_9
2002169759 1988651441 2160005388 2804572498
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 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 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.