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 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.

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.

CCF (Collaborative Computing Frameworks) is a suite of software systems, communications protocols, and tools that enable collaborative, computer-based cooperative work. CCF constructs a virtual work environment on multiple computer systems connected over the Internet, to form a Collaboratory. In this setting, participants interact with each other, simultaneously access and operate computer applications, refer to global data repositories or archives, collectively create and manipulate documents or other artifacts, perform computational transformations, and conduct a number of other activities via telepresence. Research issues addressed in this project include problem solving environments and methodologies for laboratory and instrument-based scientific disciplines, and computer science issues in heterogeneous distributed systems. New approaches are being investigated and developed for fast multiway communication, robust geographically distributed data management methodologies, high-performance computational transforms inlined within collaboration sessions, and related auxiliary issues such as active documents, security, archival storage, and experiment management and control. In this paper, we discuss the design philosophy and systems rationale behind CCF, describe the major subsystems of the collaborative computing environment, and discuss the salient features of the system.

The Collaborative Computing Transport Layer (CCTL) is a communication substrate consisting of a suite of multiparty protocols, providing varying service qualities among process groups. CCTL explicitly supports distributed collaborative and multimedia applications. CCTL is based on a two-level group hierarchy. Logical interconnections among entities, called channels, define an efficient and light-weight group mechanism. Channels support a variety of service qualities such as reliability and message ordering. Related channels can also be combined to form sessions, heavy-weight groups which provide a default atomic multicast service. CCTL supports membership protocols tailored to the quality of service offered by a channel, including a relaxed form of virtual synchrony. In this paper, we present three membership protocols and compare and relate our implementations to alternatives. Our two-level architecture allows simple and efficient implementation of the membership protocols.

A simple owner protocol for implementing a causal distributed shared memory (DSM) is presented, and it is argued that this implementation is more efficient than comparable coherent DSM implementations. Moreover, it is shown that writing programs for causal memory is no more difficult than writing programs for atomic shared memory.>

The use of weakly consistent memories in distributed shared memory systems to combat unacceptable network delay and to allow such systems to scale is proposed. Proposed memory correctness conditions are surveyed, and how they are related by a weakness hierarchy is demonstrated. Multiversion and messaging interpretations of memory are introduced as means of systematically exploring the space of possible memories. Slow memory is presented as a memory that allows the effects of writes to propagate slowly through the system, eliminating the need for costly consistency maintenance protocols that limit concurrency. Slow memory processes a valuable locality property and supports a reduction from traditional atomic memory. Thus slow memory is as expressive as atomic memory. This expressiveness is demonstrated by two exclusion algorithms and a solution to M.J. Fischer and A. Michael's (1982) dictionary problem on slow memory.>