Matthew Wolenetz

DFuse: a framework for distributed data fusion

Simple in-network data aggregation (or fusion) techniques for sensor networks have been the focus of several recent research efforts, but they are insufficient to support advanced fusion applications. We extend these techniques to future sensor networks and ask two related questions: (a) what is the appropriate set of data fusion techniques, and (b) how do we dynamically assign aggregation roles to the nodes of a sensor network. We have developed an architectural framework, DFuse, for answering these two questions. It consists of a data fusion API and a distributed algorithm for energy-aware role assignment. The fusion API enables an application to be specified as a coarse-grained dataflow graph, and eases application development and deployment. The role assignment algorithm maps the graph onto the network, and optimally adapts the mapping at run-time using role migration. Experiments on an iPAQ farm show that, the fusion API has low-overhead, and the role assignment algorithm with role migration significantly increases the network lifetime compared to any static assignment.

Dynamic data fusion for future sensor networks

DFuse is an architectural framework for dynamic application-specified data fusion in sensor networks. It bridges an important abstraction gap for developing advanced fusion applications that takes into account the dynamic nature of applications and sensor networks. Elements of the DFuse architecture include a fusion API, a distributed role assignment algorithm that dynamically adapts the placement of the application task graph on the network, and an abstraction migration facility that aids such dynamic role assignment. Experimental evaluations show that the API has low overhead, and simulation results show that the role assignment algorithm significantly increases the network lifetime over static placement.

MediaBroker: an architecture for pervasive computing

MediaBroker is a distributed framework designed to support pervasive computing applications. Specifically, the architecture consists of a transport engine and peripheral clients and addresses issues in scalability, data sharing, data transformation and platform heterogeneity. Key features of MediaBroker are a type-aware data transport that is capable of dynamically transforming data en route from source to sinks; an extensible system for describing types of streaming data; and the interaction between the transformation engine and the type system. Details of the MediaBroker architecture and implementation are presented in this paper. Through experimental study, we show reasonable performance for selected streaming media-intensive applications. For example, relative to baseline TCP performance, MediaBroker incurs under 11% latency overhead and achieves roughly 80% of the TCP throughput when streaming items larger than 100 KB across our infrastructure.

MediaBroker: A pervasive computing infrastructure for adaptive transformation and sharing of stream data

MediaBroker is a distributed framework designed to support pervasive computing applications. Key contributions of MediaBroker are efficient and scalable data transport, data stream registration and discovery, an extensible system for data type description, and type-aware data transport that is capable of dynamically transforming data en route from source to sinks. Specifically, the architecture consists of a transport engine and peripheral clients and addresses issues in scalability, data sharing, data transformation, and platform heterogeneity. Details of the MediaBroker architecture, implementation, and a concrete application example are presented in this article. Experimental study shows reasonable performance for selected streaming media-intensive applications. For example, relative to baseline TCP performance, MediaBroker incurs under 11% latency overhead and achieves roughly 80% of the TCP throughput when streaming items larger than 100 kB across our infrastructure. The EventWeb application demonstrates the utility and graceful scaling of MediaBroker for supporting pervasive computing applications.

A system architecture for distributed control loop applications (extended abstract)

In this position paper we motivate an important emerging class of applications that cooperate across a complex distributed computational fabric containing elements of widely varying capabilities, including physical and virtual sensors, actuators, and high-performance computational clusters and grids. We identify typical requirements of such applications and identify several novel research challenges that such applications pose. We sketch an evolving architecture developed as part of the Media Broker project at Georgia Tech that solves a subset of the problems presented.

An architecture for EventWeb

While the volume and diversity of multimedia permeating the world around us increases, our chances of making sense of the available information do the opposite. This environment poses a number of challenges which include achieving scalability while accessing all the available media, live and archived, inferring its context, and delivering media to all interested parties with its context attached. We envision a solution to this set of challenges in a novel system architecture. As a starting point, however, we select a previously described framework, EventWeb, suitable for annotating raw multimedia data with context meaningful to end users. We then map it onto a distributed architecture capable of correlating, analyzing, and transporting the volumes of data characteristic of the problem space. This paper first presents the requirements for our architecture, then discusses this architecture in detail, and outlines our current implementation efforts.