Scalable Approach to Uncertainty Quantification and Robust Design of Interconnected Dynamical Systems

Development of robust dynamical systems and networks such as autonomous aircraft systems capable of accomplishing complex missions faces challenges due to the dynamically evolving uncertainties coming from model uncertainties, necessity to operate in a hostile cluttered urban environment, and the distributed and dynamic nature of the communication and computation resources. Model-based robust design is difficult because of the complexity of the hybrid dynamic models including continuous vehicle dynamics, the discrete models of computations and communications, and the size of the problem. We will overview recent advances in methodology and tools to model, analyze, and design robust autonomous aerospace systems operating in uncertain environment, with stress on efficient uncertainty quantification and robust design using the case studies of the mission including model-based target tracking and search, and trajectory planning in uncertain urban environment. To show that the methodology is generally applicable to uncertain dynamical systems, we will also show examples of application of the new methods to efficient uncertainty quantification of energy usage in buildings, and stability assessment of interconnected power networks

Recent Advances in Graph Partitioning

We survey recent trends in practical algorithms for balanced graph partitioning together with applications and future research directions

COMPUTING APPROXIMATE CUSTOMIZED RANKING

As the amount of information grows and as users become more sophisticated, ranking techniques become important building blocks to meet user needs when answering queries. PageRank is one of the most successful link-based ranking methods, which iteratively computes the importance scores for web pages based on the importance scores of incoming pages. Due to its success, PageRank has been applied in a number of applications that require customization. We address the scalability challenges for two types of customized ranking. The first challenge is to compute the ranking of a subgraph. Various Web applications focus on identifying a subgraph, such as focused crawlers and localized search engines. The second challenge is to compute online personalized ranking. Personalized search improves the quality of search results for each user. The user needs are represented by a personalized set of pages or personalized link importance in an entity relationship graph. This requires an efficient online computation. To solve the subgraph ranking problem efficiently, we estimate the ranking scores for a subgraph. We propose a framework of an exact solution (IdealRank) and an approximate solution (ApproxRank) for computing ranking on a subgraph. Both IdealRank and ApproxRank represent the set of external pages with an external node $\Lambda$ and modify the PageRank-style transition matrix with respect to $\Lambda$. The IdealRank algorithm assumes that the scores of external pages are known. We prove that the IdealRank scores for pages in the subgraph converge to the true PageRank scores. Since the PageRank-style scores of external pages may not typically be available, we propose the ApproxRank algorithm to estimate scores for the subgraph. We analyze the $L_1$ distance between IdealRank scores and ApproxRank scores of the subgraph and show that it is within a constant factor of the $L_1$ distance of the external pages. We demonstrate with real and synthetic data that ApproxRank provides a good approximation to PageRank for a variety of subgraphs. We consider online personalization using ObjectRank; it is an authority flow based ranking for entity relationship graphs. We formalize the concept of an aggregate surfer on a data graph; the surfer's behavior is controlled by multiple personalized rankings. We prove a linearity theorem over these rankings which can be used as a tool to scale this type of personalization. DataApprox uses a repository of precomputed rankings for a given set of link weights assignments. We define DataApprox as an optimization problem; it selects a subset of the precomputed rankings from the repository and produce a weighted combination of these rankings. We analyze the $L_1$ distance between the DataApprox scores and the real authority flow ranking scores and show that DataApprox has a theoretical bound. Our experiments on the DBLP data graph show that DataApprox performs well in practice and allows fast and accurate personalized authority flow ranking

Vehicular Networks and Outdoor Pedestrian Localization

This thesis focuses on vehicular networks and outdoor pedestrian localization. In particular, it targets secure positioning in vehicular networks and pedestrian localization for safety services in outdoor environments. The former research topic must cope with three major challenges, concerning users’ privacy, computational costs of security and the system trust on user correctness. This thesis addresses those issues by proposing a new lightweight privacy-preserving framework for continuous tracking of vehicles. The proposed solution is evaluated in both dense and sparse vehicular settings through simulation and experiments in real-world testbeds. In addition, this thesis explores the benefit given by the use of low frequency bands for the transmission of control messages in vehicular networks. The latter topic is motivated by a significant number of traffic accidents with pedestrians distracted by their smartphones. This thesis proposes two different localization solutions specifically for pedestrian safety: a GPS-based approach and a shoe-mounted inertial sensor method. The GPS-based solution is more suitable for rural and suburban areas while it is not applicable in dense urban environments, due to large positioning errors. Instead the inertial sensor approach overcomes the limitations of previous technique in urban environments. Indeed, by exploiting accelerometer data, this architecture is able to precisely detect the transitions from safe to potentially unsafe walking locations without the need of any absolute positioning systems

A privacy preserving framework for cyber-physical systems and its integration in real world applications

A cyber-physical system (CPS) comprises of a network of processing and communication capable sensors and actuators that are pervasively embedded in the physical world. These intelligent computing elements achieve the tight combination and coordination between the logic processing and physical resources. It is envisioned that CPS will have great economic and societal impact, and alter the qualify of life like what Internet has done. This dissertation focuses on the privacy issues in current and future CPS applications. as thousands of the intelligent devices are deeply embedded in human societies, the system operations may potentially disclose the sensitive information if no privacy preserving mechanism is designed. This dissertation identifies data privacy and location privacy as the representatives to investigate the privacy problems in CPS. The data content privacy infringement occurs if the adversary can determine or partially determine the meaning of the transmitted data or the data stored in the storage. The location privacy, on the other hand, is the secrecy that a certain sensed object is associated to a specific location, the disclosure of which may endanger the sensed object. The location privacy may be compromised by the adversary through hop-by-hop traceback along the reverse direction of the message routing path. This dissertation proposes a public key based access control scheme to protect the data content privacy. Recent advances in efficient public key schemes, such as ECC, have already shown the feasibility to use public key schemes on low power devices including sensor motes. In this dissertation, an efficient public key security primitives, WM-ECC, has been implemented for TelosB and MICAz, the two major hardware platform in current sensor networks. WM-ECC achieves the best performance among the academic implementations. Based on WM-ECC, this dissertation has designed various security schemes, including pairwise key establishment, user access control and false data filtering mechanism, to protect the data content privacy. The experiments presented in this dissertation have shown that the proposed schemes are practical for real world applications. to protect the location privacy, this dissertation has considered two adversary models. For the first model in which an adversary has limited radio detection capability, the privacy-aware routing schemes are designed to slow down the adversary\u27s traceback progress. Through theoretical analysis, this dissertation shows how to maximize the adversary\u27s traceback time given a power consumption budget for message routing. Based on the theoretical results, this dissertation also proposes a simple and practical weighted random stride (WRS) routing scheme. The second model assumes a more powerful adversary that is able to monitor all radio communications in the network. This dissertation proposes a random schedule scheme in which each node transmits at a certain time slot in a period so that the adversary would not be able to profile the difference in communication patterns among all the nodes. Finally, this dissertation integrates the proposed privacy preserving framework into Snoogle, a sensor nodes based search engine for the physical world. Snoogle allows people to search for the physical objects in their vicinity. The previously proposed privacy preserving schemes are applied in the application to achieve the flexible and resilient privacy preserving capabilities. In addition to security and privacy, Snoogle also incorporates a number of energy saving and communication compression techniques that are carefully designed for systems composed of low-cost, low-power embedded devices. The evaluation study comprises of the real world experiments on a prototype Snoogle system and the scalability simulations

Methods for improving resilience in communication networks and P2P overlays

Resilience to failures and deliberate attacks is becoming an essential requirement in most communication networks today. This also applies to P2P Overlays which on the one hand are created on top of communication infrastructures, and therefore are equally affected by failures of the underlying infrastructure, but which on the other hand introduce new possibilities like the creation of arbitrary links within the overlay. In this article, we present a survey of strategies to improve resilience in communication networks as well as in P2P overlay networks. Furthermore, our intention is to point out differences and similarities in the resilience-enhancing measures for both types of networks. By revising some basic concepts from graph theory, we show that many concepts for communication networks are based on well-known graph-theoretical problems. Especially, some methods for the construction of protection paths in advance of a failure are based on very hard problems, indeed many of them are in NP and can only be solved heuristically or on certain topologies. P2P overlay networks evidently benefit from resilience-enhancing strategies in the underlying communication infrastructure, but beyond that, their specific properties pose the need for more sophisticated mechanisms. The dynamic nature of peers requires to take some precautions, like estimating the reliability of peers, redundantly storing information, and provisioning a reliable routing

Hardware Acceleration for Unstructured Big Data and Natural Language Processing.

The confluence of the rapid growth in electronic data in recent years, and the renewed interest in domain-specific hardware accelerators presents exciting technical opportunities. Traditional scale-out solutions for processing the vast amounts of text data have been shown to be energy- and cost-inefficient. In contrast, custom hardware accelerators can provide higher throughputs, lower latencies, and significant energy savings. In this thesis, I present a set of hardware accelerators for unstructured big-data processing and natural language processing. The first accelerator, called HAWK, aims to speed up the processing of ad hoc queries against large in-memory logs. HAWK is motivated by the observation that traditional software-based tools for processing large text corpora use memory bandwidth inefficiently due to software overheads, and, thus, fall far short of peak scan rates possible on modern memory systems. HAWK is designed to process data at a constant rate of 32 GB/s—faster than most extant memory systems. I demonstrate that HAWK outperforms state-of-the-art software solutions for text processing, almost by an order of magnitude in many cases. HAWK occupies an area of 45 sq-mm in its pareto-optimal configuration and consumes 22 W of power, well within the area and power envelopes of modern CPU chips. The second accelerator I propose aims to speed up similarity measurement calculations for semantic search in the natural language processing space. By leveraging the latency hiding concepts of multi-threading and simple scheduling mechanisms, my design maximizes functional unit utilization. This similarity measurement accelerator provides speedups of 36x-42x over optimized software running on server-class cores, while requiring 56x-58x lower energy, and only 1.3% of the area.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/116712/1/prateekt_1.pd