Heuristic solutions to the target identifiability problem in directional sensor networks

Existing algorithms for orienting sensors in directional sensor networks have primarily concerned themselves with the problem of maximizing the number of covered targets, assuming that target identification is a non-issue. Such an assumption however, does not hold true in all situations. In this paper, heuristic algorithms for choosing active sensors and orienting them with the goal of balancing coverage and identifiability are presented. The performance of the algorithms are verified via extensive simulations, and shown to confer increased target identifiability compared to algorithms originally designed to simply maximize the number of targets covered

Noise-sensing energy-harvesting wireless sensor network nodes

Noise pollution is becoming an increasing concern in many urban regions all over the world. An important step in fighting and mitigating noise pollution is its quantification. Wireless sensor networks (WSNs) can potentially help with these efforts, as they enable the simultaneous and continuous gathering of data over wide geographic regions. The need to replace batteries however makes the maintenance of such physically very large networks impractical. As an alternative to batteries, noise-sensing WSNs could also be powered by energy harvesting. While energy-harvesting WSNs have been demonstrated before, utilizing energy harvesting for powering noise-sensing WSNs still pose a significant challenge because of application’s unique requirements, such as a high power consumption profile for extended periods of time. In this thesis, we address four key areas of research necessary on to make energy-harvesting noise-sensing WSNs possible and, more importantly, practical to use in large-scale settings. The first key area that we address is that of new and emerging energy storage technologies, and how current algorithms and infrastructures must be modified to take advantage of them. The second key area is that of currently-accepted technical requirements, and their assessment on whether they would indeed lead to the attainment of long-term goals. The third key area is that of test methodologies for energy-harvesting designs, and how they should be modified to facilitate validation of results between researchers. The final key area is that of techniques and algorithms for future capabilities that energy-harvesting noise-sending WSNs will or can have, and how we should prepare for them, even though they may not yet exist. We provide research to support all four key areas in this work and provide concrete examples for each

Contributions On Theory And Practice For Multi-Mission Wireless Systems

The field of wireless systems has long been an active research area with various applications. Recently much attention has been given to multi-mission wireless systems that combine capabilities including information sensing, data processing, energy harvesting as well as the traditional data communication. This dissertation describes our endeavor in addressing some of the research challenges in multi-mission wireless systems, including the development of fundamental limits of such multi-mission wireless systems and effective technologies for improved performance. The first challenge addressed in this dissertation is how to handle interference, which is encountered in almost all wireless systems involving multiple nodes, an attribute shared by most multi-mission systems. To deepen our understanding on the impact of interference, we study a class of Gaussian interference channels (GICs) with mixed interference. A simple coding scheme is proposed based on Sato\u27s non-naive frequency division. The achievable region is shown to be equivalent to that of Costa\u27s noiseberg region for the one-sided Gaussian interference channel. This allows for an indirect proof that this simple achievable rate region is indeed equivalent to the Han-Kobayashi (HK) region with Gaussian input and with time sharing for this class of Gaussian interference channels with mixed interference. Optimal power management strategies are then investigated for a remote estimation system with an energy harvesting sensor. We first establish the asymptotic optimality of uncoded transmission for such a system under Gaussian assumption. With the aim of minimizing the mean squared error (MSE) at the receiver, optimal power allocation policies are proposed under various assumptions with regard to the knowledge at the transmitter and the receiver as well as battery storage capacity. For the case where non-causal side information (SI) of future harvested energy is available and battery storage is unlimited, it is shown that the optimal power allocation amounts to a simple \u27staircase-climbing\u27 procedure, where the power level follows a non-decreasing staircase function. For the case where battery storage has a finite capacity, the optimal power allocation policy can also be obtained via standard convex optimization techniques. Dynamic programming is used to optimize the allocation policy when causal SI is available. The issue of unknown transmit power at the receiver is also addressed. Finally, to make the proposed solutions practically more meaningful, two heuristic schemes are proposed to reduce computational complexity. Related to the above remote sensing problem, we provide an information theoretic formulation of a multi-functioning radio where communication between nodes involves transmission of both messages and source sequences. The objective is to study the optimal coding trade-off between the rate for message transmission and the distortion for source sequence estimation. For point-to-point systems, it is optimal to simply split total capacity into two components, one for message transmission and one for source transmission. For the multi-user case, we show that such separation-based scheme leads to a strictly suboptimal rate-distortion trade-off by examining the simple problem of sending a common source sequence and two independent messages through a Gaussian broadcast channel. Finally we study the design of a practical multi-mission wireless system - the dual-use of airborne radio frequency (RF) systems. Specifically, airborne multiple-input-multiple-output (MIMO) communication systems are leveraged for the detection of moving targets in a typical airborne environment that is characterized by the lack of scatterers. With uniform linear arrays (ULAs), angular domain decomposition of channel matrices is utilized and target detection can be accomplished by detection of change in the resolvable paths in the angular domain. For both linear and nonlinear arrays, Doppler frequency analysis can also be applied and the change in frequency components indicates the presence of potential airborne targets. Nonparametric detection of distribution changes is utilized in both approaches

Compromising Anonymous Communication Systems Using Blind Source Separation

We propose a class of anonymity attacks to both wired and wireless anonymity networks. These attacks are based on the blind source separation algorithms widely used to recover individual signals from mixtures of signals in statistical signal processing. Since the philosophy behind the design of current anonymity networks is to mix traffic or to hide in crowds, the proposed anonymity attacks are very effective. The flow separation attack proposed for wired anonymity networks can separate the traffic in a mix network. Our experiments show that this attack is effective and scalable. By combining the flow separation method with frequency spectrum matching, a passive attacker can derive the traffic map of the mix network. We use a nontrivial network to show that the combined attack works. The proposed anonymity attacks for wireless networks can identify nodes in fully anonymized wireless networks using collections of very simple sensors. Based on a time series of counts of anonymous packets provided by the sensors, we estimate the number of nodes with the use of principal component analysis. We then proceed to separate the collected packet data into traffic flows that, with help of the spatial diversity in the available sensors, can be used to estimate the location of the wireless nodes. Our simulation experiments indicate that the estimators show high accuracy and high confidence for anonymized TCP traffic. Additional experiments indicate that the estimators perform very well in anonymous wireless networks that use traffic padding

Parameter Estimation of Complex Systems from Sparse and Noisy Data

Mathematical modeling is a key component of various disciplines in science and engineering. A mathematical model which represents important behavior of a real system can be used as a substitute for the real process for many analysis and synthesis tasks. The performance of model based techniques, e.g. system analysis, computer simulation, controller design, sensor development, state filtering, product monitoring, and process optimization, is highly dependent on the quality of the model used. Therefore, it is very important to be able to develop an accurate model from available experimental data. Parameter estimation is usually formulated as an optimization problem where the parameter estimate is computed by minimizing the discrepancy between the model prediction and the experimental data. If a simple model and a large amount of data are available then the estimation problem is frequently well-posed and a small error in data fitting automatically results in an accurate model. However, this is not always the case. If the model is complex and only sparse and noisy data are available, then the estimation problem is often ill-conditioned and good data fitting does not ensure accurate model predictions. Many challenges that can often be neglected for estimation involving simple models need to be carefully considered for estimation problems involving complex models. To obtain a reliable and accurate estimate from sparse and noisy data, a set of techniques is developed by addressing the challenges encountered in estimation of complex models, including (1) model analysis and simplification which identifies the important sources of uncertainty and reduces the model complexity; (2) experimental design for collecting information-rich data by setting optimal experimental conditions; (3) regularization of estimation problem which solves the ill-conditioned large-scale optimization problem by reducing the number of parameters; (4) nonlinear estimation and filtering which fits the data by various estimation and filtering algorithms; (5) model verification by applying statistical hypothesis test to the prediction error. The developed methods are applied to different types of models ranging from models found in the process industries to biochemical networks, some of which are described by ordinary differential equations with dozens of state variables and more than a hundred parameters