SENSOR MANAGEMENT FOR LOCALIZATION AND TRACKING IN WIRELESS SENSOR NETWORKS

Wireless sensor networks (WSNs) are very useful in many application areas including battlefield surveillance, environment monitoring and target tracking, industrial processes and health monitoring and control. The classical WSNs are composed of large number of densely deployed sensors, where sensors are battery-powered devices with limited signal processing capabilities. In the crowdsourcing based WSNs, users who carry devices with built-in sensors are recruited as sensors. In both WSNs, the sensors send their observations regarding the target to a central node called the fusion center for final inference. With limited resources, such as limited communication bandwidth among the WSNs and limited sensor battery power, it is important to investigate algorithms which consider the trade-off between system performance and energy cost in the WSNs. The goal of this thesis is to study the sensor management problems in resource limited WSNs while performing target localization or tracking tasks. Most research on sensor management problems in classical WSNs assumes that the number of sensors to be selected is given a priori, which is often not true in practice. Moreover, sensor network design usually involves consideration of multiple conflicting objectives, such as maximization of the lifetime of the network or the inference performance, while minimizing the cost of resources such as energy, communication or deployment costs. Thus, in this thesis, we formulate the sensor management problem in a classical resource limited WSN as a multi-objective optimization problem (MOP), whose goal is to find a set of sensor selection strategies which re- veal the trade-off between the target tracking performance and the number of selected sensors to perform the task. In this part of the thesis, we propose a novel mutual information upper bound (MIUB) based sensor selection scheme, which has low computational complexity, same as the Fisher information (FI) based sensor selection scheme, and gives estimation performance similar to the mutual information (MI) based sensor selection scheme. Without knowing the number of sensors to be selected a priori, the MOP gives a set of sensor selection strategies that reveal different trade-offs between two conflicting objectives: minimization of the number of selected sensors and minimization of the gap between the performance metric (MIUB and FI) when all the sensors transmit measurements and when only the selected sensors transmit their measurements based on the sensor selection strategy. Crowdsourcing has been applied to sensing applications recently where users carrying devices with built-in sensors are allowed or even encouraged to contribute toward the inference tasks. Crowdsourcing based WSNs provide cost effectiveness since a dedicated sensing infrastructure is no longer needed for different inference tasks, also, such architectures allow ubiquitous coverage. Most sensing applications and systems assume voluntary participation of users. However, users consume their resources while participating in a sensing task, and they may also have concerns regarding their privacy. At the same time, the limitation on communication bandwidth requires proper management of the participating users. Thus, there is a need to design optimal mechanisms which perform selection of the sensors in an efficient manner as well as providing appropriate incentives to the users to motivate their participation. In this thesis, optimal mechanisms are designed for sensor management problems in crowdsourcing based WSNs where the fusion center (FC) con- ducts auctions by soliciting bids from the selfish sensors, which reflect how much they value their energy cost. Furthermore, the rationality and truthfulness of the sensors are guaranteed in our model. Moreover, different considerations are included in the mechanism design approaches: 1) the sensors send analog bids to the FC, 2) the sensors are only allowed to send quantized bids to the FC because of communication limitations or some privacy issues, 3) the state of charge (SOC) of the sensors affects the energy consumption of the sensors in the mechanism, and, 4) the FC and the sensors communicate in a two-sided market

Augmented Human Machine Intelligence for Distributed Inference

With the advent of the internet of things (IoT) era and the extensive deployment of smart devices and wireless sensor networks (WSNs), interactions of humans and machine data are everywhere. In numerous applications, humans are essential parts in the decision making process, where they may either serve as information sources or act as the final decision makers. For various tasks including detection and classification of targets, detection of outliers, generation of surveillance patterns and interactions between entities, seamless integration of the human and the machine expertise is required where they simultaneously work within the same modeling environment to understand and solve problems. Efficient fusion of information from both human and sensor sources is expected to improve system performance and enhance situational awareness. Such human-machine inference networks seek to build an interactive human-machine symbiosis by merging the best of the human with the best of the machine and to achieve higher performance than either humans or machines by themselves. In this dissertation, we consider that people often have a number of biases and rely on heuristics when exposed to different kinds of uncertainties, e.g., limited information versus unreliable information. We develop novel theoretical frameworks for collaborative decision making in complex environments when the observers may include both humans and physics-based sensors. We address fundamental concerns such as uncertainties, cognitive biases in human decision making and derive human decision rules in binary decision making. We model the decision-making by generic humans working in complex networked environments that feature uncertainties, and develop new approaches and frameworks facilitating collaborative human decision making and cognitive multi-modal fusion. The first part of this dissertation exploits the behavioral economics concept Prospect Theory to study the behavior of human binary decision making under cognitive biases. Several decision making systems involving humans\u27 participation are discussed, and we show the impact of human cognitive biases on the decision making performance. We analyze how heterogeneity could affect the performance of collaborative human decision making in the presence of complex correlation relationships among the behavior of humans and design the human selection strategy at the population level. Next, we employ Prospect Theory to model the rationality of humans and accurately characterize their behaviors in answering binary questions. We design a weighted majority voting rule to solve classification problems via crowdsourcing while considering that the crowd may include some spammers. We also propose a novel sequential task ordering algorithm to improve system performance for classification in crowdsourcing composed of unreliable human workers. In the second part of the dissertation, we study the behavior of cognitive memory limited humans in binary decision making and develop efficient approaches to help memory constrained humans make better decisions. We show that the order in which information is presented to the humans impacts their decision making performance. Next, we consider the selfish behavior of humans and construct a unified incentive mechanism for IoT based inference systems while addressing the selfish concerns of the participants. We derive the optimal amount of energy that a selfish sensor involved in the signal detection task must spend in order to maximize a certain utility function, in the presence of buyers who value the result of signal detection carried out by the sensor. Finally, we design a human-machine collaboration framework that blends both machine observations and human expertise to solve binary hypothesis testing problems semi-autonomously. In networks featuring human-machine teaming/collaboration, it is critical to coordinate and synthesize the operations of the humans and machines (e.g., robots and physical sensors). Machine measurements affect human behaviors, actions, and decisions. Human behavior defines the optimal decision-making algorithm for human-machine networks. In today\u27s era of artificial intelligence, we not only aim to exploit augmented human-machine intelligence to ensure accurate decision making; but also expand intelligent systems so as to assist and improve such intelligence

Solving Complex Data-Streaming Problems by Applying Economic-Based Principles to Mobile and Wireless Resource Constraint Networks

The applications that employ mobile networks depend on the continuous input of reliable data collected by sensing devices. A common application is in military systems, where as an example, drones that are sent on a mission can communicate with each other, exchange sensed data, and autonomously make decisions. Although the mobility of nodes enhances the network coverage, connectivity, and scalability, it introduces pressing issues in data reliability compounded by restrictions in sensor energy resources, as well as limitations in available memory, and computational capacity. This dissertation investigates the issues that mobile networks encounter in providing reliable data. Our research goal is to develop a diverse set of novel data handling solutions for mobile sensor systems providing reliable data by considering the dynamic trajectory behavior relationships among nodes, and the constraints inherent to mobile nodes. We study the applicability of economic models, which are simplified versions of real-world situations that let us observe and make predictions about economic behavior, to our domain. First, we develop a data cleaning method by introducing the notion of “beta,” a measure that quantifies the risk associated with trusting the accuracy of the data provided by a node based on trajectory behavior similarity. Next, we study the reconstruction of highly incomplete data streams. Our method determines the level of trust in data accuracy by assigning variable “weights” considering the quality and the origin of data. Thirdly, we design a behavior-based data reduction and trend prediction technique using Japanese candlesticks. This method reduces the dataset to 5% of its original size while preserving the behavioral patterns. Finally, we develop a data cleaning distribution method for energy-harvesting networks. Based on the Leontief Input-Output model, this method increases the data that is run through cleaning and the network uptime

Improving the Unreliability of Competence Information:an Argumentation to Apply Information Fusion in Learning Networks

Miao, Y., Sloep, P. B., Hummel, H., & Koper, R. (2009). Improving the Unreliability of Competence Information: an Argumentation to Apply Information Fusion in Learning Networks [Special issue]. International Journal of Continuing Engineering Education and Life-Long Learning (IJCEELL), 19(4/5/6), 366-380.Automated competence tracking and management is crucial for an effective and efficient lifelong competence development in learning networks. In this paper, we systematically analyze the problem of unreliability of competence information in learning networks. In tracking the development of competences in learning networks, a large amount of competence information can be gathered from diverse sources and diverse types of sources. Individual information is more or less credible. This paper investigates information fusion technologies that may be applied to address the problem and that show promise as candidate solutions for achieving an improved estimate of competences by fusing information coming from multiple sources and diverse types of sources.The work on this publication has been sponsored by the TENCompetence Integrated Project that is funded by the European Commission's 6th Framework Programme, priority IST/Technology Enhanced Learning. Contract 027087 [http://www.tencompetence.org

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

Smart device user behavior classification and learning with Hidden Markov model

Today, millions of people use smart phones because smart phones are convenient to access various online services, such as online social networks, online banking and emails. People usually use passwords for authentication, but passwords are also the major source of security vulnerabilities, which are easily to be guessed and re-used. So, protecting a user’s privacy on mobile is never trivial. Another issue with passwords is the usability in that users have to repeatedly enter passwords or pins. Therefore, it will be tremendously beneficial if a non-password authentication can be offered. In this thesis project, we propose a stochastic non-password authentication solution that models users’ finger gestures and handholding patterns as users’ profiles using Hidden Markov Model (HMM). The system leverages the data collected by three sensors on mobile devices: accelerometer, orientation and touch screen input measurements, and then trains an HMM user profile model. This solution is a software-only approach that does not require any authentication hardware such as fingerprint sensor. The solution employs machine learning algorithms to passively re-use the data provided by existing sensors equipped on devices. This project is implemented and evaluated on Android devices for performance assessment. In the experiment, we verify the accuracy of authentication. The result shows high accuracy by using more sensors since HMM has been reported of 89% accuracy in image-based gesture recognitions.Thesis (M.S.)Department of Computer Scienc

A Mechanism Design Approach to Bandwidth Allocation in Tactical Data Networks

The defense sector is undergoing a phase of rapid technological advancement, in the pursuit of its goal of information superiority. This goal depends on a large network of complex interconnected systems - sensors, weapons, soldiers - linked through a maze of heterogeneous networks. The sheer scale and size of these networks prompt behaviors that go beyond conglomerations of systems or `system-of-systems\u27. The lack of a central locus and disjointed, competing interests among large clusters of systems makes this characteristic of an Ultra Large Scale (ULS) system. These traits of ULS systems challenge and undermine the fundamental assumptions of today\u27s software and system engineering approaches. In the absence of a centralized controller it is likely that system users may behave opportunistically to meet their local mission requirements, rather than the objectives of the system as a whole. In these settings, methods and tools based on economics and game theory (like Mechanism Design) are likely to play an important role in achieving globally optimal behavior, when the participants behave selfishly. Against this background, this thesis explores the potential of using computational mechanisms to govern the behavior of ultra-large-scale systems and achieve an optimal allocation of constrained computational resources Our research focusses on improving the quality and accuracy of the common operating picture through the efficient allocation of bandwidth in tactical data networks among self-interested actors, who may resort to strategic behavior dictated by self-interest. This research problem presents the kind of challenges we anticipate when we have to deal with ULS systems and, by addressing this problem, we hope to develop a methodology which will be applicable for ULS system of the future. We build upon the previous works which investigate the application of auction-based mechanism design to dynamic, performance-critical and resource-constrained systems of interest to the defense community. In this thesis, we consider a scenario where a number of military platforms have been tasked with the goal of detecting and tracking targets. The sensors onboard a military platform have a partial and inaccurate view of the operating picture and need to make use of data transmitted from neighboring sensors in order to improve the accuracy of their own measurements. The communication takes place over tactical data networks with scarce bandwidth. The problem is compounded by the possibility that the local goals of military platforms might not be aligned with the global system goal. Such a scenario might occur in multi-flag, multi-platform military exercises, where the military commanders of each platform are more concerned with the well-being of their own platform over others. Therefore there is a need to design a mechanism that efficiently allocates the flow of data within the network to ensure that the resulting global performance maximizes the information gain of the entire system, despite the self-interested actions of the individual actors. We propose a two-stage mechanism based on modified strictly-proper scoring rules, with unknown costs, whereby multiple sensor platforms can provide estimates of limited precisions and the center does not have to rely on knowledge of the actual outcome when calculating payments. In particular, our work emphasizes the importance of applying robust optimization techniques to deal with the uncertainty in the operating environment. We apply our robust optimization - based scoring rules algorithm to an agent-based model framework of the combat tactical data network, and analyze the results obtained. Through the work we hope to demonstrate how mechanism design, perched at the intersection of game theory and microeconomics, is aptly suited to address one set of challenges of the ULS system paradigm - challenges not amenable to traditional system engineering approaches

On Classification in Human-driven and Data-driven Systems

Classification systems are ubiquitous, and the design of effective classification algorithms has been an even more active area of research since the emergence of machine learning techniques. Despite the significant efforts devoted to training and feature selection in classification systems, misclassifications do occur and their effects can be critical in various applications. The central goal of this thesis is to analyze classification problems in human-driven and data-driven systems, with potentially unreliable components and design effective strategies to ensure reliable and effective classification algorithms in such systems. The components/agents in the system can be machines and/or humans. The system components can be unreliable due to a variety of reasons such as faulty machines, security attacks causing machines to send falsified information, unskilled human workers sending imperfect information, or human workers providing random responses. This thesis first quantifies the effect of such unreliable agents on the classification performance of the systems and then designs schemes that mitigate misclassifications and their effects by adapting the behavior of the classifier on samples from machines and/or humans and ensure an effective and reliable overall classification. In the first part of this thesis, we study the case when only humans are present in the systems, and consider crowdsourcing systems. Human workers in crowdsourcing systems observe the data and respond individually by providing label related information to a fusion center in a distributed manner. In such systems, we consider the presence of unskilled human workers where they have a reject option so that they may choose not to provide information regarding the label of the data. To maximize the classification performance at the fusion center, an optimal aggregation rule is proposed to fuse the human workers\u27 responses in a weighted majority voting manner. Next, the presence of unreliable human workers, referred to as spammers, is considered. Spammers are human workers that provide random guesses regarding the data label information to the fusion center in crowdsourcing systems. The effect of spammers on the overall classification performance is characterized when the spammers can strategically respond to maximize their reward in reward-based crowdsourcing systems. For such systems, an optimal aggregation rule is proposed by adapting the classifier based on the responses from the workers. The next line of human-driven classification is considered in the context of social networks. The classification problem is studied to classify a human whether he/she is influential or not in propagating information in social networks. Since the knowledge of social network structures is not always available, the influential agent classification problem without knowing the social network structure is studied. A multi-task low rank linear influence model is proposed to exploit the relationships between different information topics. The proposed approach can simultaneously predict the volume of information diffusion for each topic and automatically classify the influential nodes for each topic. In the third part of the thesis, a data-driven decentralized classification framework is developed where machines interact with each other to perform complex classification tasks. However, the machines in the system can be unreliable due to a variety of reasons such as noise, faults and attacks. Providing erroneous updates leads the classification process in a wrong direction, and degrades the performance of decentralized classification algorithms. First, the effect of erroneous updates on the convergence of the classification algorithm is analyzed, and it is shown that the algorithm linearly converges to a neighborhood of the optimal classification solution. Next, guidelines are provided for network design to achieve faster convergence. Finally, to mitigate the impact of unreliable machines, a robust variant of ADMM is proposed, and its resilience to unreliable machines is shown with an exact convergence to the optimal classification result. The final part of research in this thesis considers machine-only data-driven classification problems. First, the fundamentals of classification are studied in an information theoretic framework. We investigate the nonparametric classification problem for arbitrary unknown composite distributions in the asymptotic regime where both the sample size and the number of classes grow exponentially large. The notion of discrimination capacity is introduced, which captures the largest exponential growth rate of the number of classes relative to the samples size so that there exists a test with asymptotically vanishing probability of error. Error exponent analysis using the maximum mean discrepancy is provided and the discrimination rate, i.e., lower bound on the discrimination capacity is characterized. Furthermore, an upper bound on the discrimination capacity based on Fano\u27s inequality is developed

Security and Energy Efficiency in Resource-Constrained Wireless Multi-hop Networks

In recent decades, there has been a huge improvement and interest from the research community in wireless multi-hop networks. Such networks have widespread applications in civil, commercial and military applications. Paradigms of this type of networks that are critical for many aspects of human lives are mobile ad-hoc networks, sensor networks, which are used for monitoring buildings and large agricultural areas, and vehicular networks with applications in traffic monitoring and regulation. Internet of Things (IoT) is also envisioned as a multi-hop network consisting of small interconnected devices, called ``things", such as smart meters, smart traffic lights, thermostats etc. Wireless multi-hop networks suffer from resource constraints, because all the devices have limited battery, computational power and memory. Battery level of these devices should be preserved in order to ensure reliability and communication across the network. In addition, these devices are not a priori designed to defend against sophisticated adversaries, which may be deployed across the network in order to disrupt network operation. In addition, the distributed nature of this type of networks introduces another limitation to protocol performance in the presence of adversaries. Hence, the inherit nature of this type of networks poses severe limitations on designing and optimizing protocols and network operations. In this dissertation, we focus on proposing novel techniques for designing more resilient protocols to attackers and more energy efficient protocols. In the first part of the dissertation, we investigate the scenario of multiple adversaries deployed across the network, which reduce significantly the network performance. We adopt a component-based and a cross-layer view of network protocols to make protocols secure and resilient to attacks and to utilize our techniques across existing network protocols. We use the notion of trust between network entities to propose lightweight defense mechanisms, which also satisfy performance requirements. Using cryptographic primitives in our network scenario can introduce significant computational overhead. In addition, behavioral aspects of entities are not captured by cryptographic primitives. Hence, trust metrics provide an efficient security metric in these scenarios, which can be utilized to introduce lightweight defense mechanisms applicable to deployed network protocols. In the second part of the dissertation, we focus on energy efficiency considerations in this type of networks. Our motivation for this work is to extend network lifetime, but at the same time maintain critical performance requirements. We propose a distributed sleep management framework for heterogeneous machine-to-machine networks and two novel energy efficient metrics. This framework and the routing metrics are integrated into existing routing protocols for machine-to-machine networks. We demonstrate the efficiency of our approach in terms of increasing network lifetime and maintaining packet delivery ratio. Furthermore, we propose a novel multi-metric energy efficient routing protocol for dynamic networks (i.e. mobile ad-hoc networks) and illustrate its performance in terms of network lifetime. Finally, we investigate the energy-aware sensor coverage problem and we propose a novel game theoretic approach to capture the tradeoff between sensor coverage efficiency and energy consumption