Target localization in wireless sensor networks for industrial control with selected sensors

This paper presents a novel energy-based target localization method in wireless sensor networks with selected sensors. In this method, sensors use Turbo Product Code (TPC) to transmit decisions to the fusion center. TPC can reduce bit error probability if communication channel errors exist. Moreover, in this method, thresholds for the energy-based target localization are designed using a heuristic method. This design method to find thresholds is suitable for uniformly distributed sensors and normally distributed targets. Furthermore, to save sensor energy, a sensor selection method is also presented. Simulation results showed that if sensors used TPC instead of Hamming code to transmit decisions to the fusion center, localization performance could be improved. Furthermore, the sensor selection method used can substantially reduce energy consumption for our target localization method. At the same time, this target localization method with selected sensors also provides satisfactory localization performance

Tracking with Sparse and Correlated Measurements via a Shrinkage-based Particle Filter

This paper presents a shrinkage-based particle filter method for tracking a mobile user in wireless networks. The proposed method estimates the shadowing noise covariance matrix using the shrinkage technique. The particle filter is designed with the estimated covariance matrix to improve the tracking performance. The shrinkage-based particle filter can be applied in a number of applications for navigation, tracking and localization when the available sensor measurements are correlated and sparse. The performance of the shrinkage-based particle filter is compared with the posterior Cramer-Rao lower bound, which is also derived in the paper. The advantages of the proposed shrinkage-based particle filter approach are demonstrated via simulation and experimental results

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

Resource aware distributed detection and estimation of random events in wireless sensor networks

In this dissertation, we develop several resource aware approaches for detection and estimation in wireless sensor networks (WSNs). Tolerating an acceptable degradation from the best achievable performance, we seek more resource efficient solutions than the state-of-the-art methods. We first define a multi-objective optimization problem and find the trade-off solutions between two conflicting objectives for the distributed detection problem in WSNs: minimizing the probability of error and minimizing the total energy consumption. Simulation results show that Pareto-optimal solutions can provide significant energy savings at the cost a slight increase in the probability of error from its minimum achievable value. Having detected the presence of the source, accurate source localization is another important task to be performed by a WSN. The state-of-the-art one-shot location estimation scheme requires simultaneous transmission of all sensor data to the fusion center. We propose an iterative source localization algorithm where a small set of anchor sensors first detect the presence of the source and arrive at a coarse location estimate. Then a number of non-anchor sensors are selected in an iterative manner to refine the location estimate. The iterative localization scheme reduces the communication requirements as compared to the one-shot location estimation while introducing some estimation latency. For sensor selection at each iteration, two metrics are proposed which are derived based on the mutual information (MI) and the posterior Cramer-Rao lower bound (PCRLB) of the location estimate. In terms of computational complexity, the PCRLB-based sensor selection metric is more efficient as compared to the MI-based sensor selection metric, and under the assumption of perfect communication channels between sensors and the fusion center, both sensor selection schemes achieve the similar estimation performance that is the mean squared error of the source location gets very close to the PCRLB of one-shot location estimator within a few iterations. The proposed iterative method is further extended to the case which considers fading on the channels between sensors and the fusion center. Simulation results are presented for the cases when partial or complete channel knowledge are available at the fusion center. We finally consider a heterogenous sensing field and define a distributed parameter estimation problem where the quantization data rate of a sensor is determined as a function of its observation SNR. The inverse of the average Fisher information is then defined as a lower bound on the average PCRLB which is hard to compute. The inverse of the average Fisher information is minimized subject to the total bandwidth and bandwidth utilization constraints and we find the optimal transmission probability of each possible quantization rate. Under stringent bandwidth availability, the proposed scheme outperforms the scheme where the total bandwidth is equally distributed among sensors

Shrinkage Based Particle Filters for Tracking in Wireless Sensor Networks with Correlated Sparse Measurements

This thesis focuses on the development of mobile tracking approaches in wireless sensor networks (WSNs) with correlated and sparse measurements. In wireless networks, devices have the ability to transfer information over the network nodes via wireless signals. The strength of a wireless signal at a receiver is referred as the received signal strength (RSS) and many wireless technologies such as Wi-Fi, ZigBee, the Global Positioning Systems (GPS), and other Satellite systems provide the RSS measurements for signal transmission. Due to the availability of RSS measurements, various tracking approaches in WSNs were developed based on the RSS measurements. Unfortunately, the feasibility of tracking using the RSS measurements is highly dependent on the connectivity of the wireless signals. The existing connectivity may be intermittently disrupted due to the low-battery status on the sensor node or temporarily sensor malfunction. In ad-hoc networks, the number of observation of the RSS measurements rapidly changing due to the movements of network nodes and mobile user. As a result, the tracking algorithms have limited data to perform state inference and this prevents accurate tracking. Furthermore, consecutive RSS measurements obtained from nearby sensor nodes exhibit spatio-temporal correlation, which provides extra information to be exploited. Exploiting the statistical information on the measurements noise covariance matrix increases the tracking accuracy. When the number of observations is relatively large, estimating the measurement noise covariance matrix is feasible. However, when they are relatively small, the covariance matrix estimation becomes ill-conditioned and non-invertible. In situations where the RSS measurements are corrupted by outliers, state inference can be misleading. Outliers can come from the sudden environmental disturbances, temporary sensor failures or even from the intrinsic noise of the sensor device. The outliers existence should be considered accordingly to avoid false and poor estimates. This thesis proposes first a shrinkage-based particle filter for mobile tracking in WSNs. It estimates the correlation in the RSS measurement using the shrinkage estimator. The shrinkage estimator overcomes the problems of ill-conditioned and non-invertibility of the measurement noise covariance matrix. The estimated covariance matrix is then applied to the particle filter. Secondly, it develops a robust shrinkage based particle filter for the problem of outliers in the RSS measurements. The proposed algorithm provides a non-parametric shrinkage estimate and represents a multiple model particle filter. The performances of both proposed filters are demonstrated over challenging scenarios for mobile tracking

Efficient and Adaptive Node Selection for Target Tracking in Wireless Sensor Network

In target tracking wireless sensor network, choosing the proper working nodes can not only minimize the number of active nodes, but also satisfy the tracking reliability requirement. However, most existing works focus on selecting sensor nodes which are the nearest to the target for tracking missions and they did not consider the correlation of the location of the sensor nodes so that these approaches can not meet all the goals of the network. This work proposes an efficient and adaptive node selection approach for tracking a target in a distributed wireless sensor network. The proposed approach combines the distance-based node selection strategy and particle filter prediction considering the spatial correlation of the different sensing nodes. Moreover, a joint distance weighted measurement is proposed to estimate the information utility of sensing nodes. Experimental results show that EANS outperformed the state-of-the-art approaches by reducing the energy cost and computational complexity as well as guaranteeing the tracking accuracy

A survey on gas leakage source detection and boundary tracking with wireless sensor networks

Gas leakage source detection and boundary tracking of continuous objects have received a significant research attention in the academic as well as the industries due to the loss and damage caused by toxic gas leakage in large-scale petrochemical plants. With the advance and rapid adoption of wireless sensor networks (WSNs) in the last decades, source localization and boundary estimation have became the priority of research works. In addition, an accurate boundary estimation is a critical issue due to the fast movement, changing shape, and invisibility of the gas leakage compared with the other single object detections. We present various gas diffusion models used in the literature that offer the effective computational approaches to measure the gas concentrations in the large area. In this paper, we compare the continuous object localization and boundary detection schemes with respect to complexity, energy consumption, and estimation accuracy. Moreover, this paper presents the research directions for existing and future gas leakage source localization and boundary estimation schemes with WSNs

Estimation And Tracking Algorithm For Autonomous Vehicles And Humans

Autonomous driving systems have experienced impressive growth in recent years. The present research community is working on several challenging aspects, such as, tracking, localization, path planning and control. In this thesis, first, we focus on tracking system and present a method to accurately track a moving vehicle. In the vehicle tracking, considering the proximity of surrounding vehicles, it is critical to detect their unusual maneuvers as quickly as possible, especially when autonomous vehicles operate among human-operated traffic. In this work, we present an approach to quickly detect lane-changing maneuvers of the nearby vehicles. The proposed algorithm is based on the optimal likelihood ratio test, known as Page test. Second, we consider another form of tracking: tracking the movements of humans in indoor settings. Indoor localization of staff and patients based on radio frequency identification (RFID) technology has promising potential application in the healthcare sector. The use of an active RFID in real-time indoor positioning system without any sacrifice of localization accuracy is intended to provide security, guidance and support service to patients. In this paper maximum likelihood estimation along with its Cramer-Rao lower bound of the locations of active RFID tags are presented by exploring the received signal strength indicator which is collected at the readers. The performance of real-time localization system is implemented by using an extended Kalman filter (EKF)

Sensing and Signal Processing in Smart Healthcare

In the last decade, we have witnessed the rapid development of electronic technologies that are transforming our daily lives. Such technologies are often integrated with various sensors that facilitate the collection of human motion and physiological data and are equipped with wireless communication modules such as Bluetooth, radio frequency identification, and near-field communication. In smart healthcare applications, designing ergonomic and intuitive human–computer interfaces is crucial because a system that is not easy to use will create a huge obstacle to adoption and may significantly reduce the efficacy of the solution. Signal and data processing is another important consideration in smart healthcare applications because it must ensure high accuracy with a high level of confidence in order for the applications to be useful for clinicians in making diagnosis and treatment decisions. This Special Issue is a collection of 10 articles selected from a total of 26 contributions. These contributions span the areas of signal processing and smart healthcare systems mostly contributed by authors from Europe, including Italy, Spain, France, Portugal, Romania, Sweden, and Netherlands. Authors from China, Korea, Taiwan, Indonesia, and Ecuador are also included