PrePro2004: a data model with pre and post-processor for HEC-HMS

This thesis presents the design concepts and development of an interface (Pre- Pro2004) utilizing geodatabases for the Hydrologic Modeling System (HMS) of the Hydrologic Engineering Center (HEC). HMS is a rainfall-runoff model which supports lumped-parameter as well as distributed-parameter based modeling. PrePro2004 uses the spatial-analysis as well as data handling capabilities of ArcGIS. The spatial data are processed to create input files for HMS. These input files and the output from HMS are stored in two geodatabases which were developed using data model concepts. The tools are provided to reproduce an HMS model from the data inside these geodatabases. The interface is developed based on the DataCentric approach which brings different hydrologic and hydraulic models together. This approach aims to attain a long-term goal of utilizing the same data for different hydrologic or hydraulic models with additional model specific requirements. Two case studies are presented to show the applications of the tools developed. The first case study details the creation of HMS input files for Salado Creek watershed with Digital Elevation Model as input. It includes the importation of an existing HMS model for Salado Creek watershed as Appendix C. The second case study details the creation of HMS input files for the Bull Creek watershed, with land use and soil type data as inputs. It describes the capabilities of tools developed in detail

Assessing NoSQL databases for telecom applications

The constant evolution of access technologies are turning Internet access more ubiquitous, faster, better and cheaper. In connection with the proliferation of Internet access, Cloud Computing is changing the way users look at data, moving from local applications and installations to remote services, accessible from any device. This new paradigm presents numerous opportunities that even traditional businesses like telecoms cannot ignore, in particular, enabling new and more cost effective solutions to old problems. The work presented in this paper provides a detailed description of how a telecom application can be migrated to a NoSQL database. Particularly, by pointing out the necessary change of how we reason about data as well as the data structures that support it, in order to take full advantage of Cloud Computing. In addition, we also present a preliminary evaluation of different data persistency paradigms based on a fully tunable simulation platform that mimics the operation of a telecom business

Reaction-diffusion patterns in smart sensor networks

technical reportWe introduced the use of Turing?s reaction-diffusion pattern formation to support high-level tasks in smart sensor networks (S-Nets). This has led us to explore various biologically motivated mechanisms. In this paper we address some issues that arise in trying to get reliable, efficient patterns in irregular grids with error in inter-node distances

PrePro2004: a data model with pre and post-processor for HEC-HMS

This thesis presents the design concepts and development of an interface (Pre- Pro2004) utilizing geodatabases for the Hydrologic Modeling System (HMS) of the Hydrologic Engineering Center (HEC). HMS is a rainfall-runoff model which supports lumped-parameter as well as distributed-parameter based modeling. PrePro2004 uses the spatial-analysis as well as data handling capabilities of ArcGIS. The spatial data are processed to create input files for HMS. These input files and the output from HMS are stored in two geodatabases which were developed using data model concepts. The tools are provided to reproduce an HMS model from the data inside these geodatabases. The interface is developed based on the DataCentric approach which brings different hydrologic and hydraulic models together. This approach aims to attain a long-term goal of utilizing the same data for different hydrologic or hydraulic models with additional model specific requirements. Two case studies are presented to show the applications of the tools developed. The first case study details the creation of HMS input files for Salado Creek watershed with Digital Elevation Model as input. It includes the importation of an existing HMS model for Salado Creek watershed as Appendix C. The second case study details the creation of HMS input files for the Bull Creek watershed, with land use and soil type data as inputs. It describes the capabilities of tools developed in detail

Pattern formation in wireless sensor networks

technical reportBiological systems exhibit an amazing array of distributed sensor/actuator systems, and the exploitation of principles and practices found in nature will lead to more effective artificial systems. The retina is an example of a highly tuned sensing organ, and the human skin is comprised of a set of heterogeneous sensor and actuator elements. Moreover, the specific organization and architecture of these systems depends on contextual influences during the developmental stages of the organism. Comparable theoretical and technological methodologies need to be found for wireless sensor networks. We propose the study of reaction-diffusion systems from mathematical biology as a starting point for this endeavor. Algorithms and experiments are described here for a useful set of pattern formation methods in wireless sensor networks

Towards a high performance parallel library to compute fluid flexible structures interactions

Indiana University-Purdue University Indianapolis (IUPUI)LBM-IB method is useful and popular simulation technique that is adopted ubiquitously to solve Fluid-Structure interaction problems in computational fluid dynamics. These problems are known for utilizing computing resources intensively while solving mathematical equations involved in simulations. Problems involving such interactions are omnipresent, therefore, it is eminent that a faster and accurate algorithm exists for solving these equations, to reproduce a real-life model of such complex analytical problems in a shorter time period. LBM-IB being inherently parallel, proves to be an ideal candidate for developing a parallel software. This research focuses on developing a parallel software library, LBM-IB based on the algorithm proposed by [1] which is first of its kind that utilizes the high performance computing abilities of supercomputers procurable today. An initial sequential version of LBM-IB is developed that is used as a benchmark for correctness and performance evaluation of shared memory parallel versions. Two shared memory parallel versions of LBM-IB have been developed using OpenMP and Pthread library respectively. The OpenMP version is able to scale well enough, as good as 83% speedup on multicore machines for <=8 cores. Based on the profiling and instrumentation done on this version, to improve the data-locality and increase the degree of parallelism, Pthread based data centric version is developed which is able to outperform the OpenMP version by 53% on manycore machines. A distributed version using the MPI interfaces on top of the cube based Pthread version has also been designed to be used by extreme scale distributed memory manycore systems

Energy Efficient Routing Protocols and algorithms for Wireless Sensor Networks a A Survey

Wireless Sensor Networks (WSNs) are an emerging technology for monitoring physical world. The sensor nodes are capable of sensing various types of environmental conditions, have some processing capabilities and ability to communicate the sensed data through wireless communication. Routing algorithms for WSNs are responsible for selecting and maintaining the routes in the network and ensure reliable and effective communication in limited periods. The energy constraint of WSNs make energy saving become the most important objective of various routing algorithms. In this paper, a survey of routing protocols and algorithms used in WSNs is presented with energy efficiency as the main goal

Guest Editorial: Special issue on data analytics and machine learning for network and service management-Part II

Network and Service analytics can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, analytics and machine learning have found ways to improve reliability, configuration, performance, fault and security management. In particular, we see a growing trend towards using machine learning, artificial intelligence and data analytics to improve operations and management of information technology services, systems and networks

On the expressiveness and trade-offs of large scale tuple stores

Proceedings of On the Move to Meaningful Internet Systems (OTM)Massive-scale distributed computing is a challenge at our doorstep. The current exponential growth of data calls for massive-scale capabilities of storage and processing. This is being acknowledged by several major Internet players embracing the cloud computing model and offering first generation distributed tuple stores. Having all started from similar requirements, these systems ended up providing a similar service: A simple tuple store interface, that allows applications to insert, query, and remove individual elements. Further- more, while availability is commonly assumed to be sustained by the massive scale itself, data consistency and freshness is usually severely hindered. By doing so, these services focus on a specific narrow trade-off between consistency, availability, performance, scale, and migration cost, that is much less attractive to common business needs. In this paper we introduce DataDroplets, a novel tuple store that shifts the current trade-off towards the needs of common business users, pro- viding additional consistency guarantees and higher level data process- ing primitives smoothing the migration path for existing applications. We present a detailed comparison between DataDroplets and existing systems regarding their data model, architecture and trade-offs. Prelim- inary results of the system's performance under a realistic workload are also presented

Guest Editorial: Special issue on data analytics and machine learning for network and service management-Part II

Network and Service analytics can harness the immense stream of operational data from clouds, to services, to social and communication networks. In the era of big data and connected devices of all varieties, analytics and machine learning have found ways to improve reliability, configuration, performance, fault and security management. In particular, we see a growing trend towards using machine learning, artificial intelligence and data analytics to improve operations and management of information technology services, systems and networks