Dennis Lucarelli

Decentralized synchronization protocols with nearest neighbor communication

A class of synchronization protocols for dense, large-scale sensor networks is presented. The protocols build on the recent work of Hong, Cheow, and Scaglione [5, 6] in which the synchronization update rules are modeled by a system of pulse-coupled oscillators. In the present work, we define a class of models that converge to a synchronized state based on the local communication topology of the sensor network only, thereby lifting the all-to-all communication requirement implicit in [5, 6]. Under some rather mild assumptions of the connectivity of the network over time, these protocols still converge to a synchronized state when the communication topology is time varying.

Forced and constrained consensus among cooperating agents

In this paper we consider the coordination of multiple agents via nearest-neighbor negotiations over a consensus variable. Existing results for single consensus variables are extended to include the cases of forced consensus, when one of the negotiating agents is driven by a setpoint, and of multiple consensus variables separated by hard constraints.

Localization in Multi-Modal Sensor Networks

We describe the design and implementation of solutions for localization problems in multi-modal wireless sensor networks. The problem of network self-localization, namely determining the positions of the nodes that comprise the network, is addressed optically using a set of pan-tilt-zoom (PTZ) cameras to search for a small light-source attached to each of the sensor nodes. Once the locations and headings of the networks nodes are estimated by the cameras, the network can be used to detect and estimate the location of objects traveling through it. Target localization is performed within the network, using information from magnetometers connected to the sensor nodes. We evaluate the performance of the proposed target localization algorithms through simulations and an implementation running on MicaZ motes. Simulation results show that the localization error for a 100-node network whose nodes are randomly deployed over an area of 100 x 100 m, can be less than 10 cm. Moreover, our initial implementation results show that the median of the localization error for magnetic targets in a 1m x 1m field is 7.1 cm.

Compressive sensing and stretch processing

We investigate the applicability of sparse signal recovery techniques featured in recent compressed sensing literature to stretch processing, a widely adopted high-range resolution pulse compression technique. We present simulation results suggesting that, under certain assumptions, incorporating sparse signal recovery algorithms results in up to a 30% improvement in range resolution. In addition, we empirically study the effects of grid mismatch and the sensitivity to phase difference between closely spaced scatterers. We also analyze worst-case coherence of the derived dictionary to better understand the expected performance of a class of representative scatterer configurations.

Multi-Modal Calibration of Surveillance Sensor Networks

Target detection and localization is one of the key research challenges in sensor networks. In this paper we propose a heterogeneous wireless sensor network integrating imaging and non-imaging sensors to accomplish the detection and localization task in complex urban environments. The low-cost non-imaging sensors provide early detection and partial localization of potential targets and direct imaging sensors to focus on them. Accurate target location estimated by the imaging sensors is subsequently used for in-situ calibration of the non-imaging sensors so that localization error is minimized over time. We evaluate our approach through simulation and our preliminary results reveal that coordination across different sensing modalities increases localization accuracy and can reduce the amount of imaging data that must be carried by the network.

Target localization in camera wireless networks

Target localization is an application of wireless sensor networks with wide applicability in homeland security scenarios including surveillance and asset protection. In this paper we present a novel sensor network that localizes with the help of two modalities: cameras and non-imaging sensors. A set of two cameras is initially used to localize the motes of a wireless sensor network. Motes subsequently collaborate to estimate the location of a target using their non-imaging sensors. Our results show that the combination of imaging and non-imaging modalities successfully achieves the dual goal of self- and target-localization. Specifically, we found through simulation and experimental validation that cameras can localize motes deployed in a 100 mx100 m area with a few cm error. Moreover, a network of motes equipped with magnetometers can, once localized, estimate the location of magnetic targets within a few cm.

Control aspects of holonomic quantum computation

A unifying framework for the control of quantum systems with non-Abelian holonomy is presented. It is shown that, from a control theoretic point of view, holonomic quantum computation can be treated as a control system evolving on a principal fiber bundle. An extension of methods developed for these classical systems may be applied to quantum holonomic systems to obtain insight into the control properties of such systems and to construct control algorithms for two established examples of the computing paradigm.

Model-based pose estimation by consensus

We present a system for determining a consensus estimate of the pose of an object, as seen from multiple cameras in a distributed network. The cameras are pointed towards a 3D object defined by a configuration of points, which are assumed to be visible and detected in all camera images. The cameras are given a model defining the 3D configuration of these object points, but do not know which image point corresponds to which object point. Each camera estimates the pose of the object, then iteratively exchanges information with its neighbors to arrive at a common decision of the pose over the network. We consider eight variations of the consensus algorithm, and find that each converges to a more accurate result than do the individual cameras alone on average. The method exchanging 3D world coordinates penalized to agree with the input model provides the most accurate results. If bandwidth is limited, performing consensus over rotations and translations requires cameras to exchange only the six values specifying the six degrees of freedom of the object pose, and performing consensus in SE(3) using the Karcher mean is generally the best choice. We show further that interleaving pose calculation with the consensus iterations improves the final result when the image noise is large.

Prototype design and experimental results for an Intruder Detection Swarming Network

The 2007 paper entitled ldquoSwarming Network for Intruder Detectionrdquo described the concept for an intruder detection and tracking sensor network that exhibits swarming behavior. The network is based on simple signals and cueing with no data protocols as a means to minimize cost and complexity. Further, a means to detect an intruder via measuring the blockage by the intruder of randomly placed illumination signals was also described. Preliminary calculations indicated the potential for high probability of detection and low probability of false alarm. This paper describes the results of further investigation, including preliminary design to better estimate affordability and scalability, and analysis and experiments to explore the assumptions of the tone-based signaling approach. Results to date build confidence that the concept is realizable.

Consensus Variable Approach to Decentralized Adaptive Scheduling

We present a new approach to solving adaptive scheduling problems in decentralized systems, based on the concept of nearest-neighbor negotiations and the idea of a consensus variable. Exploiting some recent extensions to existing results for single consensus variables, the adaptive scheduling problem is solved by choosing task timings as the consensus variables in the system. This application is illustrated via the example of a synchronized strike mission. The chapter concludes with a discussion of future research directions on this topic.