Graded quantization for multiple description coding of compressive measurements

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics

A secure communication framework for wireless sensor networks

Today, wireless sensor networks (WSNs) are no longer a nascent technology and future networks, especially Cyber-Physical Systems (CPS) will integrate more sensor-based systems into a variety of application scenarios. Typical application areas include medical, environmental, military, and commercial enterprises. Providing security to this diverse set of sensor-based applications is necessary for the healthy operations of the overall system because untrusted entities may target the proper functioning of applications and disturb the critical decision-making processes by injecting false information into the network. One way to address this issue is to employ en-route-filtering-based solutions utilizing keys generated by either static or dynamic key management schemes in the WSN literature. However, current schemes are complicated for resource-constrained sensors as they utilize many keys and more importantly as they transmit many keying messages in the network, which increases the energy consumption of WSNs that are already severely limited in the technical capabilities and resources (i.e., power, computational capacities, and memory) available to them. Nonetheless, further improvements without too much overhead are still possible by sharing a dynamically created cryptic credential. Building upon this idea, the purpose of this thesis is to introduce an efficient and secure communication framework for WSNs. Specifically, three protocols are suggested as contributions using virtual energies and local times onboard the sensors as dynamic cryptic credentials: (1) Virtual Energy-Based Encryption and Keying (VEBEK); (2) TIme-Based DynamiC Keying and En-Route Filtering (TICK); (3) Secure Source-Based Loose Time Synchronization (SOBAS) for WSNs.Ph.D.Committee Chair: Copeland, John; Committee Co-Chair: Beyah, Raheem; Committee Member: Li, Geoffrey; Committee Member: Owen, Henry; Committee Member: Zegura, Ellen; Committee Member: Zhang, Fumi

Reliable Inference from Unreliable Agents

Distributed inference using multiple sensors has been an active area of research since the emergence of wireless sensor networks (WSNs). Several researchers have addressed the design issues to ensure optimal inference performance in such networks. The central goal of this thesis is to analyze distributed inference systems with potentially unreliable components and design strategies to ensure reliable inference in such systems. The inference process can be that of detection or estimation or classification, and the components/agents in the system can be sensors and/or humans. The system components can be unreliable due to a variety of reasons: faulty sensors, security attacks causing sensors to send falsified information, or unskilled human workers sending imperfect information. This thesis first quantifies the effect of such unreliable agents on the inference performance of the network and then designs schemes that ensure a reliable overall inference. In the first part of this thesis, we study the case when only sensors are present in the system, referred to as sensor networks. For sensor networks, the presence of malicious sensors, referred to as Byzantines, are considered. Byzantines are sensors that inject false information into the system. In such systems, the effect of Byzantines on the overall inference performance is characterized in terms of the optimal attack strategies. Game-theoretic formulations are explored to analyze two-player interactions. Next, Byzantine mitigation schemes are designed that address the problem from the system\u27s perspective. These mitigation schemes are of two kinds: Byzantine identification schemes and Byzantine tolerant schemes. Using learning based techniques, Byzantine identification schemes are designed that learn the identity of Byzantines in the network and use this information to improve system performance. When such schemes are not possible, Byzantine tolerant schemes using error-correcting codes are developed that tolerate the effect of Byzantines and maintain good performance in the network. Error-correcting codes help in correcting the erroneous information from these Byzantines and thereby counter their attack. The second line of research in this thesis considers humans-only networks, referred to as human networks. A similar research strategy is adopted for human networks where, the effect of unskilled humans sharing beliefs with a central observer called \emph{CEO} is analyzed, and the loss in performance due to the presence of such unskilled humans is characterized. This problem falls under the family of problems in information theory literature referred to as the \emph{CEO Problem}, but for belief sharing. The asymptotic behavior of the minimum achievable mean squared error distortion at the CEO is studied in the limit when the number of agents $L$ and the sum rate $R$ tend to infinity. An intermediate regime of performance between the exponential behavior in discrete CEO problems and the $1/R$ behavior in Gaussian CEO problems is established. This result can be summarized as the fact that sharing beliefs (uniform) is fundamentally easier in terms of convergence rate than sharing measurements (Gaussian), but sharing decisions is even easier (discrete). Besides theoretical analysis, experimental results are reported for experiments designed in collaboration with cognitive psychologists to understand the behavior of humans in the network. The act of fusing decisions from multiple agents is observed for humans and the behavior is statistically modeled using hierarchical Bayesian models. The implications of such modeling on the design of large human-machine systems is discussed. Furthermore, an error-correcting codes based scheme is proposed to improve system performance in the presence of unreliable humans in the inference process. For a crowdsourcing system consisting of unskilled human workers providing unreliable responses, the scheme helps in designing easy-to-perform tasks and also mitigates the effect of erroneous data. The benefits of using the proposed approach in comparison to the majority voting based approach are highlighted using simulated and real datasets. In the final part of the thesis, a human-machine inference framework is developed where humans and machines interact to perform complex tasks in a faster and more efficient manner. A mathematical framework is built to understand the benefits of human-machine collaboration. Such a study is extremely important for current scenarios where humans and machines are constantly interacting with each other to perform even the simplest of tasks. While machines perform best in some tasks, humans still give better results in tasks such as identifying new patterns. By using humans and machines together, one can extract complete information about a phenomenon of interest. Such an architecture, referred to as Human-Machine Inference Networks (HuMaINs), provides promising results for the two cases of human-machine collaboration: \emph{machine as a coach} and \emph{machine as a colleague}. For simple systems, we demonstrate tangible performance gains by such a collaboration which provides design modules for larger, and more complex human-machine systems. However, the details of such larger systems needs to be further explored

Practical security scheme design for resource-constrained wireless networks

The implementation of ubiquitous computing (or pervasive computing) can leverage various types of resource-constrained wireless networks such as wireless sensor networks and wireless personal area networks. These resource-constrained wireless networks are vulnerable to many malicious attacks that often cause leakage, alteration and destruction of critical information due to the insecurity of wireless communication and the tampers of devices. Meanwhile, the constraints of resources, the lack of centralized management, and the demands of mobility of these networks often make traditional security mechanisms inefficient or infeasible. So, the resource-constrained wireless networks pose new challenges for information assurance and call for practical, efficient and effective solutions. In this research, we focus on wireless sensor networks and aim at enhancing confidentiality, authenticity, availability and integrity, for wireless sensor networks. Particularly, we identify three important problems as our research targets: (1) key management for wireless sensor networks (for confidentiality), (2) filtering false data injection and DoS attacks in wireless sensor networks (for authenticity and availability), and (3) secure network coding (for integrity). We investigate a diversity of malicious attacks against wireless sensor networks and design a number of practical schemes for establishing pairwise keys between sensor nodes, filtering false data injection and DoS attacks, and securing network coding against pollution attacks for wireless sensor networks. Our contributions from this research are fourfold: (1) We give a taxonomy of malicious attacks for wireless sensor networks. (2) We design a group-based key management scheme using deployment knowledge for wireless sensor networks to establish pair-wise keys between sensor nodes. (3) We propose an en-route scheme for filtering false data injection and DoS attacks in wireless sensor networks. (4) We present two efficient schemes for securing normal and XOR network coding against pollution attacks. Simulation and experimental results show that our solutions outperform existing ones and are suitable for resource-constrained wireless sensor networks in terms of computation overhead, communication cost, memory requirement, and so on

Graded quantization for multiple description coding of compressive measurements

Compressed sensing (CS) is an emerging paradigm for acquisition of compressed representations of a sparse signal. Its low complexity is appealing for resource-constrained scenarios like sensor networks. However, such scenarios are often coupled with unreliable communication channels and providing robust transmission of the acquired data to a receiver is an issue. Multiple description coding (MDC) effectively combats channel losses for systems without feedback, thus raising the interest in developing MDC methods explicitly designed for the CS framework, and exploiting its properties. We propose a method called Graded Quantization (CS-GQ) that leverages the democratic property of compressive measurements to effectively implement MDC, and we provide methods to optimize its performance. A novel decoding algorithm based on the alternating directions method of multipliers is derived to reconstruct signals from a limited number of received descriptions. Simulations are performed to assess the performance of CS-GQ against other methods in presence of packet losses. The proposed method is successful at providing robust coding of CS measurements and outperforms other schemes for the considered test metrics

Software-Defined Lighting.

For much of the past century, indoor lighting has been based on incandescent or gas-discharge technology. But, with LED lighting experiencing a 20x/decade increase in flux density, 10x/decade decrease in cost, and linear improvements in luminous efficiency, solid-state lighting is finally cost-competitive with the status quo. As a result, LED lighting is projected to reach over 70% market penetration by 2030. This dissertation claims that solid-state lighting’s real potential has been barely explored, that now is the time to explore it, and that new lighting platforms and applications can drive lighting far beyond its roots as an illumination technology. Scaling laws make solid-state lighting competitive with conventional lighting, but two key features make solid-state lighting an enabler for many new applications: the high switching speeds possible using LEDs and the color palettes realizable with Red-Green-Blue-White (RGBW) multi-chip assemblies. For this dissertation, we have explored the post-illumination potential of LED lighting in applications as diverse as visible light communications, indoor positioning, smart dust time synchronization, and embedded device configuration, with an eventual eye toward supporting all of them using a shared lighting infrastructure under a unified system architecture that provides software-control over lighting. To explore the space of software-defined lighting (SDL), we design a compact, flexible, and networked SDL platform to allow researchers to rapidly test new ideas. Using this platform, we demonstrate the viability of several applications, including multi-luminaire synchronized communication to a photodiode receiver, communication to mobile phone cameras, and indoor positioning using unmodified mobile phones. We show that all these applications and many other potential applications can be simultaneously supported by a single lighting infrastructure under software control.PhDElectrical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/111482/1/samkuo_1.pd