Time synchronization is a critical component in many wireless sensor network applications. Although several synchronization protocols have recently been developed, they tend to break down when implemented on networks of heterogeneous devices consisting of different hardware components and operating systems, and communicate over different network media. In this paper, we present a methodology for time synchronization in heterogeneous sensor networks (HSNs). This includes synchronization between mote and PC networks, a communication pathway that is often used in sensor networks, but has received little attention with respect to time synchronization. In addition, we evaluate clock skew compensation methods including linear regression, exponential averaging, and phase-locked loops. Our HSN synchronization methodology has been implemented as a network service and tested on an experimental testbed. We show that a 6-hop heterogeneous sensor network can be synchronized with an accuracy on the order of microseconds.

We address the problem of coordinating the activities of a team of agents in a dynamic, uncertain, nonlinear environment. Bounded rationality, bounded communication, subjectivity and distribution make it extremely challenging to find effective strategies. In these domains it is difficult to accurately predict whether potential policy modifications will lead to an increase in the value of the team reward. Our Predictability and Criticality Metrics (PCM) approach errs on the side of safety, and advocates considering policy modifications that are guaranteed to not harm the current policy, and uses simple metrics to choose from within that set a modification that increases the team reward. In the context of the DARPA Coordinators program, we show how the PCM approach yielded a system that significantly outperformed several competing approaches in an extensive independent evaluation.

The paper describes a target tracking system running on a Heterogeneous Sensor Network (HSN) and presents results gathered from a realistic deployment. The system fuses audio direction of arrival data from mote class devices and object detection measurements from embedded PCs equipped with cameras. The acoustic sensor nodes perform beamforming and measure the energy as a function of the angle. The camera nodes detect moving objects and estimate their angle. The sensor detections are sent to a centralized sensor fusion node via a combination of two wireless networks. The novelty of our system is the unique combination of target tracking methods customized for the application at hand and their implementation on an actual HSN platform.

Many wireless sensor network applications require knowledge of node placement in order to make sense of sensor data in a spatial context. Networks of mobile sensors require position updates for navigation through the sensing region. The global positioning system is able to provide localization information, however in many situations it cannot be relied on, and alternative localization methods are required. We propose a technique for the localization and navigation of a mobile robot that uses the Doppler-shift in frequency observed by stationary sensor nodes. Our experimental results show that, by using observed RF Doppler shifts, a robot is able to navigate through a sensing region with an average localization error of 1.68 meters.

The application of model-based diagnosis schemes to real systems introduces many significant challenges, such as building accurate system models for heterogeneous systems with complex behaviors, dealing with noisy measurements and disturbances during system operation, and producing valuable results in a timely manner with limited information and computational resources. The Advanced Diagnostics and Prognostics Testbed (ADAPT), deployed at NASA Ames Research Center, is a representative spacecraft electrical power distribution system that embodies a number of these challenges for developing realistic diagnosis and prognosis algorithms. ADAPT contains a large number of interconnected components, along with a number of circuit breakers and relays that enable a number of different power distribution configurations. The system includes electrical dc and ac loads, mechanical subsystems, such as motors, and fluid systems, such as pumps. The system components are susceptible to different types of faults that include unexpected changes in parameter values, discrete faults in switching elements, and sensor faults. This paper presents Hybrid TRANSCEND, a comprehensive model-based diagnosis scheme to address these challenges. The scheme uses the hybrid bond graph modeling language to systematically develop computational models and algorithms for hybrid state estimation, robust fault detection, and efficient fault isolation. The computational methods are implemented as a suite of software tools that enables analysis and testing through simulation, diagnosability studies, and deployment on the experimental testbed. Simulation and experimental results demonstrate the effectiveness of this methodology in efficient diagnosis of heterogeneous components for an embedded system.

We observe that the e-business systems development frameworks tradeoff performance at the expense of flexibility. In this paper, we present a performance comparison of JavaBeans application framework with a well-known framework, Struts. JavaBeans is a flexible and extensible CBD application framework. However the flexibility and extensibility are conflicting software qualities against the performance. Our experiment results show the significance of JavaBeans application framework over contemporary CBD application frameworks and how much its performance is affected by changing schemes of the framework for achieving flexibility and extensibility.

Effective resource management for distributed real-time embedded (DRE) systems is hard due to their unique characteristics, including (1) constraints in multiple resources and (2) highly fluctuating resource availability and input workload. DRE systems can benefit from a middleware framework that enables adaptive resource management algorithms to ensure application QoS requirements are met. This paper identifies key challenges in designing and extending resource allocation algorithms for DRE systems. We present an empirical study of bin-packing algorithms enhanced to meet these challenges. Our analysis identifies input application patterns that help generate appropriate heuristics for using these algorithms effectively in DRE systems.