Rajiv Ramnath

ExScal: elements of an extreme scale wireless sensor network

Project ExScal (for extreme scale) fielded a 1000+ node wireless sensor network and a 200+ node peer-to-peer ad hoc network of 802.11 devices in a 13km by 300m remote area in Florida, USA during December 2004. In comparison with previous deployments, the ExScal application is relatively complex and its networks are the largest ones of either type fielded to date. In this paper, we overview the key requirements of ExScal, the corresponding design of the hardware/software platform and application, and some results of our experiments.

Kansei: a testbed for sensing at scale

The Kansei testbed at the Ohio State University is designed to facilitate research on networked sensing applications at scale. Kansei embodies a unique combination of characteristics as a result of its design focus on sensing and scaling: (i) Heterogeneous hardware infrastructure with dedicated node resources for local computation, storage, data exfiltration and back-channel communication, to support complex experimentation, (ii) Time accurate hybrid simulation engine for simulating substantially larger arrays using testbed hardware resources, (iii) High fidelity sensor data generation and real-time data and event injection, (iv) Software components and associated job control language to support complex multi-tier experiments utilizing real hardware resources and data generation and simulation engines. In this paper, we present the elements of Kansei testbed architecture, including its hardware and software platforms as well as its hybrid simulation and sensor data generation engines

Kansei: a high-fidelity sensing testbed

Hardware and software testbeds are becoming the preferred basis for experimenting with embedded wireless sensor network applications. The Kansei testbed at the Ohio State University features a heterogeneous hardware infrastructure, with dedicated node resources for local computation, storage, data retrieval, and back-channel communication. Kansei includes a time-accurate hybrid simulation engine that uses testbed hardware resources to simulate large arrays. It supports high-fidelity sensor data generation as well as real-time data and event injection. The testbed also includes software components and an associated job-control language for complex multi-tier experiments.

NIIIP-SMART: an investigation of distributed object approaches to support MES development and deployment in a virtual enterprise

The National Industrial Information Infrastructure Protocols (NIIIP) consortiums solutions for MES adaptable replicable technology (SMART) subgroup is developing an information infrastructure to enable the integration and interoperation among manufacturing execution systems (MES) and enterprise information systems within or among enterprises. The goal of these developments is an adaptable, affordable, reconfigurable, integratable manufacturing system. Key innovative aspects of NIIIP SMART are: Design of a standards-oriented configurable object model that represents the diverse aspects of MES. Application of distributed object architecture, work-flow, events, policy rules, intelligent agents, and knowledge management technologies to implement manufacturing and business procedures and policy. Product data exchange based on standard for the exchange of product data (STEP) and EXPRESS (ISO 10303), and enterprise resource planning interaction using open application group interface specification (OAGIS) business service requests (BSR).

Towards building large-scale distributed systems for twitter sentiment analysis

In recent years, social networks have become very popular. Twitter, a micro-blogging service, is estimated to have about 200 million registered users and these users create approximately 65 million tweets a day. Twitter users usually show their opinion about topics of their interest. The challenge is that each tweet is limited in 140 characters, and is hence very short. It may contain slang and misspelled words. Thus, it is difficult to apply traditional NLP techniques which are designed for working with formal languages, into Twitter domain. Another challenge is that the total volume of tweets is extremely high, and it takes a long time to process. In this paper, we describe a large-scale distributed system for real-time Twitter sentiment analysis. Our system consists of two components: a lexicon builder and a sentiment classifier. These two components are capable of running on a large-scale distributed system since they are implemented using a MapReduce framework and a distributed database model. Thus, our lexicon builder and sentiment classifier are scalable with the number of machines and the size of data. The experiments also show that our lexicon has a good quality in opinion extraction, and the accuracy of the sentiment classifier can be improved by combining the lexicon with a machine learning technique.

A language and system for the construction and tuning of parallel programs

The programming of efficient parallel software typically requires extensive experimentation with program prototypes. To facilitate such experimentation, any programming system that supports rapid prototyping of parallel programs should provide high-level language primitives with which programs can be explicitly, statically, or dynamically tuned with respect to performance and reliability. Such language primitives should be able to refer conveniently to the information about the executing program and the parallel hardware required for tuning. Such information may include monitoring data about the current or previous program or even hints regarding appropriate tuning decisions. Language primitives and an associated programming system for program tuning are presented. The primitives and system have been implemented, and have been tested with several parallel applications on a network of Unix workstations. >

IT-enabled sense-and-respond strategies in complex public organizations

City governments face difficult challenges in serving their increasingly Net-connected constituencies in an environment of change, uncertain demand, and reduced budgets. These conditions require their IT departments to enable governments to adapt to citizen requests in a sense-and-respond (S-R) manner. In this article, the application of S-R concepts is demonstrated by the approaches used in developing an IT strategic plan for Columbus, Ohio. The fractal-based, request-focused strategy used here creates a unified organizational and IT context for connecting the Department of Technology and city government departments to their customers by utilizing an incremental, lean portfolio-management-based action plan and architecture.

Multi-Resolution Multimedia QoE Models for IPTV Applications

Internet television (IPTV) is rapidly gaining popularity and is being widely deployed in content delivery networks on the Internet. In order to proactively deliver optimum user quality of experience (QoE) for IPTV, service providers need to identify network bottlenecks in real time. In this paper, we develop psycho-acoustic-visual models that can predict user QoE of multimedia applications in real time based on online network status measurements. Our models are neural network based and cater to multi-resolution IPTV applications that include QCIF, QVGA, SD, and HD resolutions encoded using popular audio and video codec combinations. On the network side, our models account for jitter and loss levels, as well as router queuing disciplines: packet-ordered and time-ordered FIFO. We evaluate the performance of our multi-resolution multimedia QoE models in terms of prediction characteristics, accuracy, speed, and consistency. Our evaluation results demonstrate that the models are pertinent for real-time QoE monitoring and resource adaptation in IPTV content delivery networks.

A taxonomy of mobile and pervasive applications

In this paper we present a taxonomy for characterizing pervasive applications. This taxonomy focuses on abstracting <u>application</u> characteristics, independent of the characteristics of the middleware or infrastructure that support the application, and provides a controlled vocabulary for thinking about the application. We provide an informal verification for the taxonomy by using it to categorize a range of pervasive applications, culled from the literature and from projects we are involved in, and showing that the taxonomy is (a) <u>consistent and complete</u> - similar applications are categorized similarly and applications that are different are not similarly categorized and (b) <u>useful</u> - each characteristic provides new information about applications not explained by the other characteristics. Finally, we present concrete uses for the taxonomy.

Enterprise Interaction Ontology for Change Impact Analysis of Complex Systems

Reasoning about the impact of change is critical throughout the information technology (IT) architecture lifecycle management processes and this is especially challenging because installed architectures are complex, evolve constantly, and most changes have some global impact. We present an enterprise-interaction ontology for integrated query, analysis, and monitoring that supports features to allow architects and engineers pin-point the impact of change to the installed architecture before implementation. The ontology represents select associations between the enterprisepsilas business processes, services and infrastructure so that significant consequences of a change are propagated to affected areas based on underlying rules. Thus, interdependencies and relationships that are not obvious are identified and the impact is quantified. This allows the architect to know the complete scope of modifications required in order to accomplish a change in a manner consistent with best practices (like ITIL version 3). We illustrate - 1) the rules and taxonomy relationships that give us the ability to propagate changes and determine the impact, and 2) how actual questions and decision-making during the architecture management processes can be better supported using a more precise and factual understanding. Not only does the interaction methodology help analyze the potential impact of adding a new component, a change due to an incident, or the deletion of an existing component from the architecture, it also supports business-IT alignment processes like chargeback, capacity management and disaster recovery.