We discuss the derivation of an empirical model for spatial registration patterns of mobile users in a campus wireless local area network (WLAN). Such a model can be very useful in a variety of simulation studies of the performance of mobile wireless systems, such as that of resource management and mobility management protocols. We base the model on extensive experimental data from a campus WiFi LAN installation. We divide the empirical data available to us into training and test data sets, develop the model based on the training set, and evaluate it against the test set.The model shows that user registration patterns exhibit a distinct hierarchy, and that WLAN access points (APs) can be clustered based on registration patterns. Cluster size distributions are highly skewed, as are intra-cluster transition probabilities and trace lengths, which can all be modeled well by the heavy-tailed Weibull distribution. The fraction of popular APs in a cluster, as a function of cluster size, can be modeled by exponential distributions. There is general similarity across hierarchies, in that inter-cluster registration patterns tend to have the same characteristics and distributions as intra-cluster patterns. In this context, we also introduce and discuss the modeling of the disconnected state as an integral part of real traffic characteristics.We generate synthetic traffic traces based on the model we derive. We then compare these traces against the real traces from the test set using a set of metrics we define. We find that the synthetic traces agree very well with the test set in terms of the metrics. We compare the derived model to a simple modified random waypoint model, and show that the latter is not at all representative of the real data. We also show how the model parameters can be varied to allow designers to consider 'what-if' scenarios easily. Finally we develop an extended version of Model T that uses an alternative modeling of relative popularity of APs and clusters, with certain generalization advantages, and evaluate its fidelity to the real data also, with positive results.
Abstract-The increased availability and usage of multimedia information have created a critical need for efficient multimedia processing algorithms. These algorithms must offer capabilities related to browsing, indexing, and retrieval of relevant data. A crucial step in multimedia processing is that of reliable video segmentation into visually coherent video shots through scene change detection. Video segmentation enables subsequent processing operations on video shots, such as video indexing, semantic representation, or tracking of selected video information. Since video sequences generally contain both abrupt and gradual scene changes, video segmentation algorithms must be able to detect a large variety of changes. While existing algorithms perform relatively well for detecting abrupt transitions (video cuts), reliable detection of gradual changes is much more difficult. In this paper, a novel one-pass, real-time approach to video scene change detection based on statistical sequential analysis and operating on compressed multimedia bitstream is proposed. Our approach models video sequences as stochastic processes, with scene changes being reflected by changes in the characteristics (parameters) of the process. Statistical sequential analysis is used to provide an unified framework for the detection of both abrupt and gradual scene changes.
The evaluation of a great deal of research on ad hoc networks, as well as cellular networks, depends on models of user mobility. Many models have been developed and utilized, such as the random walk and random waypoint models. These are simple to implement and analyze but unlikely to be realistic. We develop a model based on extensive experimental data from a campus Wi-Fi LAN installation, representing traces from about 6000 users over a period of about 2 years. This data does not enable us to develop a user mobility model directly. However, as a first step, we develop a model of the time and sequence of locations at which user devices register. Note that this can be very useful, for instance to evaluate protocols that attempt to manage routing or resource allocations at different nodes. This paper reports work in progress on developing a user registration model. It shows the key time domain as well as space domain features we have extracted from the data. In particular, we show that the time features indicate heavy-tailed, although not power-law, distributions. The spatial features strongly indicate registration localization and hierarchy. The model itself can be represented as a set of probability distributions for various parameters. The modeler, for example a protocol designer, can then generate traces that conform to these distributions while varying the scale of the model in terms of the number of users. We close with a brief discussion of further work to refine and extend the model.
Figure 1: From left to right-(a) The PiCam Camera Array Module (b) Raw 4×4 array images each 1000×750 pixels (c) Parallax corrected and superresolved high resolution 8MP Image; (d) A high resolution filtered 8MP depth map
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