Collaborative Information Processing in Wireless Sensor Networks for Diffusive Source Estimation

In this dissertation, we address the issue of collaborative information processing for diffusive source parameter estimation using wireless sensor networks (WSNs) capable of sensing in dispersive medium/environment, from signal processing perspective. We begin the dissertation by focusing on the mathematical formulation of a special diffusion phenomenon, i.e., an underwater oil spill, along with statistical algorithms for meaningful analysis of sensor data leading to efficient estimation of desired parameters of interest. The objective is to obtain an analytical solution to the problem, rather than using non-model based sophisticated numerical techniques. We tried to make the physical diffusion model as much appropriate as possible, while maintaining some pragmatic and reasonable assumptions for the simplicity of exposition and analytical derivation. The dissertation studies both source localization and tracking for static and moving diffusive sources respectively. For static diffusive source localization, we investigate two parametric estimation techniques based on the maximum-likelihood (ML) and the best linear unbiased estimator (BLUE) for a special case of our obtained physical dispersion model. We prove the consistency and asymptotic normality of the obtained ML solution when the number of sensor nodes and samples approach infinity, and derive the Cramer-Rao lower bound (CRLB) on its performance. In case of a moving diffusive source, we propose a particle filter (PF) based target tracking scheme for moving diffusive source, and analytically derive the posterior Cramer-Rao lower bound (PCRLB) for the moving source state estimates as a theoretical performance bound. Further, we explore nonparametric, machine learning based estimation technique for diffusive source parameter estimation using Dirichlet process mixture model (DPMM). Since real data are often complicated, no parametric model is suitable. As an alternative, we exploit the rich tools of nonparametric Bayesian methods, in particular the DPMM, which provides us with a flexible and data-driven estimation process. We propose DPMM based static diffusive source localization algorithm and provide analytical proof of convergence. The proposed algorithm is also extended to the scenario when multiple diffusive sources of same kind are present in the diffusive field of interest. Efficient power allocation can play an important role in extending the lifetime of a resource constrained WSN. Resource-constrained WSNs rely on collaborative signal and information processing for efficient handling of large volumes of data collected by the sensor nodes. In this dissertation, the problem of collaborative information processing for sequential parameter estimation in a WSN is formulated in a cooperative game-theoretic framework, which addresses the issue of fair resource allocation for estimation task at the Fusion center (FC). The framework allows addressing either resource allocation or commitment for information processing as solutions of cooperative games with underlying theoretical justifications. Different solution concepts found in cooperative games, namely, the Shapley function and Nash bargaining are used to enforce certain kinds of fairness among the nodes in a WSN

Distributed detection, localization, and estimation in time-critical wireless sensor networks

In this thesis the problem of distributed detection, localization, and estimation (DDLE) of a stationary target in a fusion center (FC) based wireless sensor network (WSN) is considered. The communication process is subject to time-critical operation, restricted power and bandwidth (BW) resources operating over a shared communication channel Buffering from Rayleigh fading and phase noise. A novel algorithm is proposed to solve the DDLE problem consisting of two dependent stages: distributed detection and distributed estimation. The WSN performs distributed detection first and based on the global detection decision the distributed estimation stage is performed. The communication between the SNs and the FC occurs over a shared channel via a slotted Aloha MAC protocol to conserve BW. In distributed detection, hard decision fusion is adopted, using the counting rule (CR), and sensor censoring in order to save power and BW. The effect of Rayleigh fading on distributed detection is also considered and accounted for by using distributed diversity combining techniques where the diversity combining is among the sensor nodes (SNs) in lieu of having the processing done at the FC. Two distributed techniques are proposed: the distributed maximum ratio combining (dMRC) and the distributed Equal Gain Combining (dEGC). Both techniques show superior detection performance when compared to conventional diversity combining procedures that take place at the FC. In distributed estimation, the segmented distributed localization and estimation (SDLE) framework is proposed. The SDLE enables efficient power and BW processing. The SOLE hinges on the idea of introducing intermediate parameters that are estimated locally by the SNs and transmitted to the FC instead of the actual measurements. This concept decouples the main problem into a simpler set of local estimation problems solved at the SNs and a global estimation problem solved at the FC. Two algorithms are proposed for solving the local problem: a nonlinear least squares (NLS) algorithm using the variable projection (VP) method and a simpler gird search (GS) method. Also, Four algorithms are proposed to solve the global problem: NLS, GS, hyperspherical intersection method (HSI), and robust hyperspherical intersection (RHSI) method. Thus, the SDLE can be solved through local and global algorithm combinations. Five combinations are tied: NLS2 (NLS-NLS), NLS-HSI, NLS-RHSI, GS2, and GS-N LS. It turns out that the last algorithm combination delivers the best localization and estimation performance. In fact , the target can be localized with less than one meter error. The SNs send their local estimates to the FC over a shared channel using the slotted-Aloha MAC protocol, which suits WSNs since it requires only one channel. However, Aloha is known for its relatively high medium access or contention delay given the medium access probability is poorly chosen. This fact significantly hinders the time-critical operation of the system. Hence, multi-packet reception (MPR) is used with slotted Aloha protocol, in which several channels are used for contention. The contention delay is analyzed for slotted Aloha with and without MPR. More specifically, the mean and variance have been analytically computed and the contention delay distribution is approximated. Having theoretical expressions for the contention delay statistics enables optimizing both the medium access probability and the number of MPR channels in order to strike a trade-off between delay performance and complexity

Estimation of the Concentration from a Moving Gaseous Source in the Atmosphere Using a Guided Sensing Aerial Vehicle

The estimation of the gas concentration (process-state) associated with a stationary or moving source using a sensing aerial vehicle (SAV) is considered. The dispersion from such a gaseous source into the ambient atmosphere is representative of an accidental or deliberate release of chemicals, or a release of gases from biological systems. Estimation of the concentration field provides a superior ability for source localization, assessment of possible adverse impacts, and eventual containment. The abstract and finite-dimensional approximation framework presented couples theoretical estimation and control with computational fluid dynamics methods. The gas dispersion (process) model is based on the advection-diffusion equation with variable eddy diffusivities and ambient winds. Cases are considered for a 2D and 3D domain. The state estimator is a modified Luenberger observer with a €�collocated€� filter gain that is parameterized by the position of the SAV. The process-state (concentration) estimator is based on a 2D and 3D adaptive, multigrid, multi-step finite-volume method. The grid is adapted with local refinement and coarsening during the process-state estimation in order to improve accuracy and efficiency. The motion dynamics of the SAV are incorporated into the spatial process and the SAV€™s guidance is directly linked to the performance of the state estimator. The computational model and the state estimator are coupled in the sense that grid-refinement is affected by the SAV repositioning, and the guidance laws of the SAV are affected by grid-refinement. Extensive numerical experiments serve to demonstrate the effectiveness of the coupled approach

Greedy Methods in Plume Detection, Localization and Tracking

Greedy method, as an efficient computing tool, can be applied to various combinatorial or nonlinear optimization problems where finding the global optimum is difficult, if not computationally infeasible. A greedy algorithm has the nature of making the locally optimal choice at each stage and then solving the subproblems that arise later. It iteratively make

Optical Nanoimpacts of Dielectric and Metallic Nanoparticles on Gold Surface by Reflectance Microscopy: Adsorption or Bouncing?

International audienceOptical modeling coupled to experiments show that a microscope operating in reflection mode allows imaging, through solutions or even a microfluidic cover, various kinds of nanoparticles, NPs, over a (reflecting) sensing surface, here a gold (Au) surface. Optical modeling suggests that this configuration enables the interferometric imaging of single NPs which can be characterized individually from local change in the surface reflectivity. The interferometric detection improves the optical limit of detection compared to classical configurations exploiting only the light scattered by the NPs. The method is then tested experimentally, to monitor in situ and in real time, the collision of single Brownian NPs, or optical nanoimpacts, with an Au-sensing surface. First, mimicking a microfluidic biosensor platform, the capture of 300 nm FeOx maghemite NPs from a convective flow by a surface-functionalized Au surface is dynamically monitored. Then, the adsorption or bouncing of individual dielectric (100 nm polystyrene) or metallic (40 and 60 nm silver) NPs is observed directly through the solution. The influence of the electrolyte on the ability of NPs to repetitively bounce or irreversibly adsorb onto the Au surface is evidenced. Exploiting such visualization mode of single-NP optical nanoimpacts is insightful for comprehending single-NP electrochemical studies relying on NP collision on an electrode (electrochemical nanoimpacts)

Measuring Groundwater Flow Velocities near Drinking Water Extraction Wells in Unconsolidated Sediments

Groundwater is an important source of drinking water in coastal regions with predominantly unconsolidated sediments. To protect and manage drinking water extraction wells in these regions, reliable estimates of groundwater flow velocities around well fields are of paramount importance. Such measurements help to identify the dynamics of the groundwater flow and its response to stresses, to optimize water resources management, and to calibrate groundwater flow models. In this article, we review approaches for measuring the relatively high groundwater flow velocity measurements near these wells. We discuss and review their potential and limitations for use in this environment. Environmental tracer measurements are found to be useful for regional scale estimates of groundwater flow velocities and directions, but their use is limited near drinking water extraction wells. Surface-based hydrogeophysical measurements can potentially provide insight into groundwater flow velocity patterns, although the depth is limited in large-scale measurement setups. Active-heating distributed temperature sensing (AH-DTS) provides direct measurements of in situ groundwater flow velocities and can monitor fluctuations in the high groundwater flow velocities near drinking water extraction wells. Combining geoelectrical measurements with AH-DTS shows the potential to estimate a 3D groundwater flow velocity distribution to fully identify groundwater flow towards drinking water extraction wells

Digging Deeper with Diffuse Correlation Spectroscopy

Patients with neurological diseases are vulnerable to cerebral ischemia, which can lead to brain injury. In the intensive care unit (ICU), neuromonitoring techniques that can detect flow reductions would enable timely administration of therapies aimed at restoring adequate cerebral perfusion, thereby avoiding damage to the brain. However, suitable bedside neuromonitoring methods sensitive to changes of blood flow and/or oxygen metabolism have yet to be established. Near-infrared spectroscopy (NIRS) is a promising technique capable of non-invasively monitoring flow and oxygenation. Specifically, diffuse correlation spectroscopy (DCS) and time-resolved (TR) NIRS can be used to monitor blood flow and tissue oxygenation, respectively, and combined to measuring oxidative metabolism. The work presented in this thesis focused on advancing a DCS/TR-NIRS hybrid system for acquiring these physiological measurements at the bedside. The application of NIRS for neuromonitoring is favourable in the neonatal ICU since the relatively thin scalp and skull of infants has minimal effect on the detected optical signal. Considering this application, the validation of a combined DCS/NIRS method for measuring the cerebral metabolic rate of oxygen (CMRO2) was investigated in Chapter 2. Although perfusion changes measured by DCS have been confirmed by various flow modalities, characterization of photon scattering in the brain is not clearly understood. Chapter 3 presents the first DCS study conducted directly on exposed cortex to confirm that the Brownian motion model is the best flow model for characterizing the DCS signal. Furthermore, a primary limitation of DCS is signal contamination from extracerebral tissues in the adult head, causing CBF to be underestimated. In Chapter 4, a multi-layered model was implemented to separate signal contributions from scalp and brain; derived CBF changes were compared to computed tomography perfusion. Overall, this thesis advances DCS techniques by (i) quantifying cerebral oxygen metabolism, (ii) confirming the more appropriate flow model for analyzing DCS data and (iii) demonstrating the ability of DCS to measure CBF accurately despite the presence of a thick (1-cm) extracerebral layer. Ultimately, the work completed in this thesis should help with the development of a hybrid DCS/NIRS system suitable for monitoring cerebral hemodynamics and energy metabolism in critical-ill patients