Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv

Belief Space Scheduling

This thesis develops the belief space scheduling framework for scheduling under uncertainty in Stochastic Collection and Replenishment (SCAR) scenarios. SCAR scenarios involve the transportation of a resource such as fuel to agents operating in the field. Key characteristics of this scenario are persistent operation of the agents, and consideration of uncertainty. Belief space scheduling performs optimisation on probability distributions describing the state of the system. It consists of three major components---estimation of the current system state given uncertain sensor readings, prediction of the future state given a schedule of tasks, and optimisation of the schedule of the replenishing agents. The state estimation problem is complicated by a number of constraints that act on the state. A novel extension of the truncated Kalman Filter is developed for soft constraints that have uncertainty described by a Gaussian distribution. This is shown to outperform existing estimation methods, striking a balance between the high uncertainty of methods that ignore the constraints and the overconfidence of methods that ignore the uncertainty of the constraints. To predict the future state of the system, a novel analytical, continuous-time framework is proposed. This framework uses multiple Gaussian approximations to propagate the probability distributions describing the system state into the future. It is compared with a Monte Carlo framework and is shown to provide similar discrimination performance while computing, in most cases, orders of magnitude faster. Finally, several branch and bound tree search methods are developed for the optimisation problem. These methods focus optimisation efforts on earlier tasks within a model predictive control-like framework. Combined with the estimation and prediction methods, these are shown to outperform existing approaches

Real-Time Localization Using Software Defined Radio

Service providers make use of cost-effective wireless solutions to identify, localize, and possibly track users using their carried MDs to support added services, such as geo-advertisement, security, and management. Indoor and outdoor hotspot areas play a significant role for such services. However, GPS does not work in many of these areas. To solve this problem, service providers leverage available indoor radio technologies, such as WiFi, GSM, and LTE, to identify and localize users. We focus our research on passive services provided by third parties, which are responsible for (i) data acquisition and (ii) processing, and network-based services, where (i) and (ii) are done inside the serving network. For better understanding of parameters that affect indoor localization, we investigate several factors that affect indoor signal propagation for both Bluetooth and WiFi technologies. For GSM-based passive services, we developed first a data acquisition module: a GSM receiver that can overhear GSM uplink messages transmitted by MDs while being invisible. A set of optimizations were made for the receiver components to support wideband capturing of the GSM spectrum while operating in real-time. Processing the wide-spectrum of the GSM is possible using a proposed distributed processing approach over an IP network. Then, to overcome the lack of information about tracked devices’ radio settings, we developed two novel localization algorithms that rely on proximity-based solutions to estimate in real environments devices’ locations. Given the challenging indoor environment on radio signals, such as NLOS reception and multipath propagation, we developed an original algorithm to detect and remove contaminated radio signals before being fed to the localization algorithm. To improve the localization algorithm, we extended our work with a hybrid based approach that uses both WiFi and GSM interfaces to localize users. For network-based services, we used a software implementation of a LTE base station to develop our algorithms, which characterize the indoor environment before applying the localization algorithm. Experiments were conducted without any special hardware, any prior knowledge of the indoor layout or any offline calibration of the system

Computer Control: An Overview

Computer control is entering all facets of life from home electronics to production of different products and material. Many of the computers are embedded and thus ``hidden'' for the user. In many situations it is not necessary to know anything about computer control or real-time systems to implement a simple controller. There are, however, many situations where the result will be much better when the sampled-data aspects of the system are taken into consideration when the controller is designed. Also, it is very important that the real-time aspects are regarded. The real-time system influences the timing in the computer and can thus minimize latency and delays in the feedback controller. The paper introduces different aspects of computer-controlled systems from simple approximation of continuous time controllers to design aspects of optimal sampled-data controllers. We also point out some of the pitfalls of computer control and discusses the practical aspects as well as the implementation issues of computer control. Published as a Professional Briefs by IFAC

CROSS-STACK PREDICTIVE CONTROL FRAMEWORK FOR MULTICORE REAL-TIME APPLICATIONS

Many of the next generation applications in entertainment, human computer interaction, infrastructure, security and medical systems are computationally intensive, always-on, and have soft real time (SRT) requirements. While failure to meet deadlines is not catastrophic in SRT systems, missing deadlines can result in an unacceptable degradation in the quality of service (QoS). To ensure acceptable QoS under dynamically changing operating conditions such as changes in the workload, energy availability, and thermal constraints, systems are typically designed for worst case conditions. Unfortunately, such over-designing of systems increases costs and overall power consumption. In this dissertation we formulate the real-time task execution as a Multiple-Input, Single- Output (MISO) optimal control problem involving tracking a desired system utilization set point with control inputs derived from across the computing stack. We assume that an arbitrary number of SRT tasks may join and leave the system at arbitrary times. The tasks are scheduled on multiple cores by a dynamic priority multiprocessor scheduling algorithm. We use a model predictive controller (MPC) to realize optimal control. MPCs are easy to tune, can handle multiple control variables, and constraints on both the dependent and independent variables. We experimentally demonstrate the operation of our controller on a video encoder application and a computer vision application executing on a dual socket quadcore Xeon processor with a total of 8 processing cores. We establish that the use of DVFS and application quality as control variables enables operation at a lower power op- erating point while meeting real-time constraints as compared to non cross-stack control approaches. We also evaluate the role of scheduling algorithms in the control of homo- geneous and heterogeneous workloads. Additionally, we propose a novel adaptive control technique for time-varying workloads

Measurements, Models, Systems and Design

531 s.

Automated camera ranking and selection using video content and scene context

PhDWhen observing a scene with multiple cameras, an important problem to solve is to automatically identify “what camera feed should be shown and when?” The answer to this question is of interest for a number of applications and scenarios ranging from sports to surveillance. In this thesis we present a framework for the ranking of each video frame and camera across time and the camera network, respectively. This ranking is then used for automated video production. In the first stage information from each camera view and from the objects in it is extracted and represented in a way that allows for object- and frame-ranking. First objects are detected and ranked within and across camera views. This ranking takes into account both visible and contextual information related to the object. Then content ranking is performed based on the objects in the view and camera-network level information. We propose two novel techniques for content ranking namely: Routing Based Ranking (RBR) and Multivariate Gaussian based Ranking (MVG). In RBR we use a rule based framework where weighted fusion of object and frame level information takes place while in MVG the rank is estimated as a multivariate Gaussian distribution. Through experimental and subjective validation we demonstrate that the proposed content ranking strategies allows the identification of the best-camera at each time. The second part of the thesis focuses on the automatic generation of N-to-1 videos based on the ranked content. We demonstrate that in such production settings it is undesirable to have frequent inter-camera switching. Thus motivating the need for a compromise, between selecting the best camera most of the time and minimising the frequent inter-camera switching, we demonstrate that state-of-the-art techniques for this task are inadequate and fail in dynamic scenes. We propose three novel methods for automated camera selection. The first method (¡go f ) performs a joint optimization of a cost function that depends on both the view quality and inter-camera switching so that a i Abstract ii pleasing best-view video sequence can be composed. The other two methods (¡dbn and ¡util) include the selection decision into the ranking-strategy. In ¡dbn we model the best-camera selection as a state sequence via Directed Acyclic Graphs (DAG) designed as a Dynamic Bayesian Network (DBN), which encodes the contextual knowledge about the camera network and employs the past information to minimize the inter camera switches. In comparison ¡util utilizes the past as well as the future information in a Partially Observable Markov Decision Process (POMDP) where the camera-selection at a certain time is influenced by the past information and its repercussions in the future. The performance of the proposed approach is demonstrated on multiple real and synthetic multi-camera setups. We compare the proposed architectures with various baseline methods with encouraging results. The performance of the proposed approaches is also validated through extensive subjective testing

Cooperative Navigation for Low-bandwidth Mobile Acoustic Networks.

This thesis reports on the design and validation of estimation and planning algorithms for underwater vehicle cooperative localization. While attitude and depth are easily instrumented with bounded-error, autonomous underwater vehicles (AUVs) have no internal sensor that directly observes XY position. The global positioning system (GPS) and other radio-based navigation techniques are not available because of the strong attenuation of electromagnetic signals in seawater. The navigation algorithms presented herein fuse local body-frame rate and attitude measurements with range observations between vehicles within a decentralized architecture. The acoustic communication channel is both unreliable and low bandwidth, precluding many state-of-the-art terrestrial cooperative navigation algorithms. We exploit the underlying structure of a post-process centralized estimator in order to derive two real-time decentralized estimation frameworks. First, the origin state method enables a client vehicle to exactly reproduce the corresponding centralized estimate within a server-to-client vehicle network. Second, a graph-based navigation framework produces an approximate reconstruction of the centralized estimate onboard each vehicle. Finally, we present a method to plan a locally optimal server path to localize a client vehicle along a desired nominal trajectory. The planning algorithm introduces a probabilistic channel model into prior Gaussian belief space planning frameworks. In summary, cooperative localization reduces XY position error growth within underwater vehicle networks. Moreover, these methods remove the reliance on static beacon networks, which do not scale to large vehicle networks and limit the range of operations. Each proposed localization algorithm was validated in full-scale AUV field trials. The planning framework was evaluated through numerical simulation.PhDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/113428/1/jmwalls_1.pd