Heterogeneous sensor networks consisting of resource-constrained nodes as well as resource-intensive nodes equipped with high-bandwidth sensors offer significant advantages for developing large sensor networks for a diverse set of applications. Target tracking can benefit from such heterogeneous networks that support the use of sensors with different modalities. Such applications require tight time synchronization across the heterogeneous sensor network in order to improve both the estimation and real-time performance. In this paper we present a methodology for time synchronization in heterogeneous sensor networks. The synchronization methodology has been implemented as a network service and tested on an experimental testbed demonstrating an accuracy in the order of microseconds over a multi-hop network. In addition, we use the time synchronization method in a multi-modal tracking application for performing accurate sensor fusion of audio and video data collected from heterogeneous sensor nodes and we show that our method improves tracking performance.

In practical applications of graph transformation techniques to model transformations one often has the need for copying, deleting, or moving entire subgraphs that match a certain graph pattern. While this can be done using elementary node and edge operations, the transformation is rather cumbersome to write. To simplify the transformation, we have recently developed a novel approach that allows selecting subgraphs from the matched portion of the host graph, applying a filter condition to the selection, and performing a delete, move, or copy operation on the filtered result in the context of a transformation rule. The approach has been implemented in the GReAT language and tested on examples that show the practical efficacy of the technique. The paper describes the technique in detail and illustrates its use on a real-life example.

Wireless sensor networks consist of small, inexpensive devices which interact with the environment, communicate with each other, and perform distributed computations in order to monitor spatio-temporal phenomena. These devices are ideally suited for a variety of applications including object tracking, environmental monitoring, and homeland security. At present, sensor network technologies do not provide off-the-shelf solutions to users who lack low-level network programming experience. Because of limited resources, ad hoc deployments, and volatile wireless communication links, the development of distributed applications require the combination of both application and system-level logic. Programming frameworks and middleware for traditional distributed computing are not suitable for many of these problems due to the resource constraints and interactions with the physical world.To address these challenges we have developed OASiS, a programming framework which provides abstractions for objectcentric, ambient-aware, service-oriented sensor network applications. OASiS uses a well-defined model of computation based on globally asynchronous locally synchronous dataflow, and is complemented by a user-friendly modeling environment. Applications are realized as graphs of modular services and executed in response to the detection of physical phenomena. We have also implemented a suite of middleware services that support OASiS to provide a layer of abstraction shielding the low-level system complexities. A tracking application is used to illustrate the features of OASiS. Our results demonstrate the feasibility and the benefits of a service-oriented programming framework for composing and deploying applications in resource constrained sensor networks.

Service oriented architecture (SOA) design principles are increasingly being adopted to develop distributed real-time and embedded (DRE) systems, such as avionics mission computing, due to the availability of real-time component middleware platforms. Traditional approaches to fault tolerance that rely on replication and recovery of a single server or a single host do not work in this paradigm since the fault management schemes must now account for the timely and simultaneous failover of groups of entities while improving system availability by minimizing the risk of simultaneous failures of replicated entities. This paper describes MDDPro, a model-driven dependability provisioning tool for DRE systems. MDDPro provides intuitive modeling abstractions to specify failover requirements of DRE systems at different granularities. MDDPro enables plugging in different replica placement algorithms to improve system availability. Finally, its generative capabilities automate the deployment and configuration of the DRE system on the underlying platforms.

Remote sensing missions for Earth Science contribute greatly to the understanding of the dynamics of our planet. Conventional approaches however, impede the scientific community's ability to (1) generate and refine models of complex phenomena, such as, extended weather forecasting, (2) detect and rapidly respond to critical transient events (e.g., disasters, such as hurricanes and floods). This paper describes a more effective approach based on intelligent, networked sensor webs that incorporate seamless dynamic connectivity between spacecraft, aircraft, and in situ terrestrial sensors, employs reactive and proactive strategies for improved temporal, spectral, and spatial coverage of the earth and its atmosphere, and uses enhanced dynamic decision-making for rapid responses to changing situations. MACRO, an extension of our earlier work on a multi-agent framework for heterogeneous spacecraft constellations, will provide interoperability and autonomy to achieve the needs for smart sensing in NASA's proposed sensor web. The system capability will be demonstrated via a simulated but salient disaster management scenario on an existing hardware testbed at the Lockheed Martin Advanced Technology Center.