We propose an opportunistic ad hoc localization algorithm called Urban Pedestrians Localization (UPL), for estimating locations of mobile nodes in urban districts. The design principles of UPL are twofold. First, we assume that location landmarks are deployed sparsely due to deployment-cost constraints. Thus, most mobile nodes cannot expect to meet these location landmarks frequently. Each mobile node in UPL relies on location information received from its neighboring mobile nodes instead in order to estimate its area of presence in which the node is expected to exist. Although the area of presence of each mobile node becomes inexact as it moves, it can be used to reduce the areas of presence of the others. Second, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles for predicting the area of presence of mobile nodes accurately under mobility. This also helps to reduce each node's area of presence. The experimental results have shown that UPL could be limited to 0.7r positioning error in average, where r denotes the radio range by the above two ideas.

Overlapping computations and communication is a key to accelerating stencil applications on parallel computers, especially for GPU clusters. However, such programming is a time-consuming part of the stencil application development. To address this problem, we developed an automatic code generation tool to produce a parallel stencil application with latency hiding automatically from its dataflow model. With this tool, users visually construct the workflows of stencil applications in a dataflow programming model. Our dataflow compiler determines a data decomposition policy for each application, and generates source code that overlaps the stencil computations and communication (MPI and PCIe). We demonstrate two types of overlapping models, a CPU-GPU hybrid execution model and a GPU-only model. We use a CFD benchmark computing 19-point 3D stencils to evaluate our scheduling performance, which results in 1.45 TFLOPS in single precision on a cluster with 64 Tesla C1060 GPUs.

In this paper, we present a range-free ad-hoc localization algorithm called UPL (Urban Pedestrians Localization), for positioning mobile nodes in urban district. The design principle of UPL is two-fold. (1) We assume that location seeds are deployed sparsely due to deployment-cost constraints. Thus most mobile nodes cannot expect to meet these location seeds frequently. Therefore, each mobile node in UPL relies on location information received from its neighboring mobile nodes in order to estimate its area of presence. The area of presence of each mobile node becomes inexact as it moves, but it is helpful to reduce the areas of presence of the other mobile nodes. (2) To predict the area of presence of mobile nodes accurately under mobility, we employ information about obstacles such as walls, and present an algorithm to calculate the movable areas of mobile nodes considering obstacles. This also helps to reduce each node's area of presence. The experimental results have shown that by the above two ideas UPL could achieve 8mpositioning error in average with 10mof radio range.

We have proposed realistic mobility models and developed a network simulator called MobiREAL for performance evaluation of MANET applications. Simulator users can easily generate a mobility scenario on any city section with the support tool of MobiREAL and can easily analyze the impact of mobility through the simulation visualization tool of MobiREAL. In this demonstration, we will show the usefulness of MobiREAL and importance to simulate MANET applications in realistic environments.

In this paper, we propose a probabilistic rule-based model to describe behavior of mobile nodes for accurately evaluating performance of MANET applications. The proposed model allows us to describe how mobile nodes change their destinations, routes and speeds/directions based on their positions, surroundings (e.g. neighboring nodes), information obtained from applications, and so on. We have designed and developed a network simulator called MobiREAL based on the proposed methodology. Through experiments, we show importance to simulate MANET applications in realistic environments.

In order for precise evaluation of MANET applications, more realistic mobility models are needed in wireless network simulations. In this paper, we focus on the behavior of pedestrians in urban areas and propose a new method to generate a mobility scenario called Urban Pedestrian Flows (UPF). In the proposed method, we classify pedestrians in a simulation field into multiple groups by their similar behavior patterns (simply called flows hereafter, which indicate how they move around geographic points). Given the observed road density in the target field, we derive using linear programming techniques how many pedestrians per minute follow each flow. Using the derived flows, we generate a UPF scenario which can be used in network simulators. In particular, we have enhanced a network simulator called MobiREAL, which has been developed in our research group, so that we can generate and use the UPF scenario. MobiREAL simulator has three main facilities: the behavior simulator, network simulator and animator. The behavior simulator can generate/delete mobile nodes according to the UPF scenario. The network simulator can simulate MANET protocols and applications. The animator offers elegant visualization of simulation traces as well as graphical user interfaces for facilitating derivation of UPF scenarios. Through several case studies, we show similarity of the derived flows to the observed ones, as well as the metrics that characterize the mobility of the scenario.