Advances in data acquisition and sensor technologies are leading towards the development of “high fan-in ” architectures: widely distributed systems whose edges consist of numerous receptors such as sensor networks, RFID readers, or probes, and whose interior nodes are traditional host computers organized using the principles of cascading streams and successive aggregation. Examples include RFID-enabled supply chain management, largescale environmental monitoring, and various types of network and computing infrastructure monitoring. In this paper, we identify the key characteristics and data management challenges presented by high fan-in systems, and argue for a uniform, query-based approach towards addressing them. We then present our initial design concepts behind HiFi, the system we are building to embody these ideas, and describe a proof-of-concept prototype. 1.

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 coarsegrained 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.

model for outdoor distributed embedded systems. Central to SP are the concepts of space and spatial reference, which provide applications with a virtual resource naming in networks of embedded systems. A network resource is referenced using its expected physical location and properties. Together with other SP features, such as reference consistency and access timeout, they help programmers cope with highly dynamic network configurations in a network-transparent fashion.

In this paper, we present the design and implementation of Smart Messages, a distributed computing platform for networks of embedded systems based on execution migration. A Smart Message (SM) is a user-defined distributed program which executes on nodes of interest, named by their properties, and uses an explicit lightweight migration to reach these nodes. During migrations, an SM carries its code and execution state, and it self-routes at each intermediate node between two nodes of interest. The nodes in the network cooperate to support the SM execution by providing a virtual machine and a shared memory region addressable by names (tag space). To illustrate the flexibility of SMs to program real world applications, we describe EZCab, an application for booking cabs in densely populated urban areas. We also present experimental results to quantify the performance achieved by the SM prototype. 1.

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.

This paper surveys a variety of subsystems designed to be the building blocks from which sophisticated infrastructures for ubiquitous computing are assembled. Our experience shows that many of these building blocks fit neatly into one of five categories, each containing functionally-equivalent components. Effectively identifying the best-fit “lego pieces”, which in turn determines the composite functionality of the resulting infrastructure, is critical. The selection process, however, is impeded by the lack of convention for labeling these classes of building blocks. The lack of clarity with respect to what ready-made subsystems are available within each class often results in naive re-implementation of ready-made components, monolithic and clumsy implementations, and implementations that impose non-standard interfaces onto the applications above. This paper explores each class of subsystems in light of the experience gained over two years of active development of both ubiquitous computing applications and software infrastructures for their deployment.

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. 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 Media-Broker 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.

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. 1

Future ubiquitous computing environments will consist of massive, ad hoc networks of embedded systems deployed in the physical space. Programming such environments requires new abstractions and computing models. This paper presents Spatial Programming (SP), a novel paradigm for programming ubiquitous computing environments. SP o#ers access to data and services distributed on nodes spread across the physical space in a similar fashion to access to memory using references. SP programs access network resources using a high level abstraction, termed spatial reference, which names these resources using their spatial properties and content-based names. An underlying system takes care of mapping spatial references onto target nodes in the network. Thus, SP hides the complexity of the underlying network from the programmer, while o#ering a simple and intuitive programming model.