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Open Access Publications from the University of California

Scholarly Works (8 results)

  • Thesis
  • Peer Reviewed
UC Berkeley Electronic Theses and Dissertations (2016)

Emerging distributed cyber-physical systems (CPSs) integrate a wide range of heterogeneous components that need to be orchestrated in a dynamic environment. While model-based techniques are commonly used in CPS design, they be- come inadequate in capturing the complexity as systems become larger and extremely dynamic. The adaptive nature of the systems makes data-driven approaches highly desirable, if not necessary.

Traditionally, data-driven systems utilize large volumes of static data sets to extract models and predictions of physical processes. However, in emerging CPS, networked sensors provide continually streaming data, creating an essentially infinite source of information. Processing data in batches is no longer a viable option: streams are most valuable when processed on-line, allowing actionable information to be gathered just as the data becomes available. This fundamental shift from big data to infinite data, while having great potential to enable smarter systems, also poses unique challenges. Computation models that capture the integration of streaming data into CPS design become a key requirement for systems to learn, adapt, and evolve in real-time.

This thesis explores methodologies for developing data-driven CPSs that integrate model-based design and real-time stream analytics in a modular way. The key modeling framework to be introduced is the aspect-oriented modeling (AOM) paradigm, which leverages the principle of separation-of-concerns in actor-oriented de- sign. Aspects are useful for representing cross-cutting concerns in complex system architectures, as first introduced by the aspect-oriented programming paradigm in object-oriented design. AOM applies this idea to actor-oriented design, creating aspects that enable representation of modular concerns in a complex system model. In data-driven CPS, the introduced aspects can be leveraged to process streaming data, extract actionable information, and incorporate these into the system workflow in a way that preserves model semantics and modularity. To address information extraction from streaming data, we propose the use of aspects that implement Dynamic Bayesian Network based algorithms for machine learning and optimization. Specifically, we introduce an actor-oriented toolkit that enables dynamics and sensing models to be composed with inference, Bayesian learning, and optimization algorithms, and present comprehensive case studies on cooperative mobile robot control. We additionally study the use of streaming data for control of dynamic networked CPS in the context of home automation, and present an overview of the use cases of aspects in actor-oriented CPS development.

    Cover page: Data-Driven Cyber-Physical Systems via Real-Time Stream Analytics and Machine Learning
    • Article
    • Peer Reviewed
    UC Berkeley Previously Published Works (2016)
    Accuracy and responsiveness are two key properties of emerging cyber-physical energy systems that need to incorporate high throughput sensor streams for distributed monitoring and control applications. The electric power grid, which is a prominent example of such systems, is being integrated with high throughput sensors in order to support stable system dynamics that are provisioned to be utilized in real-time supervisory control applications. The end-to-end performance and overall scalability of cyber-physical energy applications depend on robust middleware services that are able to operate with variable resources and multi-source sensor data. This leads to uncertain behavior under highly variable sensor and middleware topologies. We present a parametric approach to modeling the middleware service architecture for distributed power applications and account for temporal satisfiability of system properties under network resource and data volume uncertainty. We present a heterogeneous modeling framework that combines Monte Carlo simulations of uncertainty parameters within an executable discrete-event middleware service model. By employing Monte Carlo simulations followed by regression analysis, we quantify system parameters that significantly affect behavior of middleware services and the achievability of temporal requirements.
      Cover page: Uncertainty Analysis of Middleware Services for Streaming Smart Grid Applications
      • Article
      • Peer Reviewed
      UC Berkeley Previously Published Works (2016)
      One of the biggest challenges in cyber-physical system (CPS) design is their intrinsic complexity, heterogeneity, and multidisciplinary nature. Emerging distributed CPSs integrate a wide range of heterogeneous aspects such as physical dynamics, control, machine learning, and error handling. Furthermore, system components are often distributed over multiple physical locations, hardware platforms, and communication networks. While model-based design (MBD) has tremendously improved the design process, CPS design remains a difficult task. Models are meant to improve understanding of a system, yet this quality is often lost when models become too complicated. In this paper, we show how to use aspect-oriented (AO) modeling techniques in MBD as a systematic way to segregate domains of expertise and cross-cutting concerns within the model. We demonstrate these concepts on actor-oriented models of an industrial robotic swarm application and illustrate the use of AO modeling techniques to manage the complexity. We also show how to use AO modeling for design-space exploration.
        Cover page: Systems Engineering for Industrial Cyber–Physical Systems Using Aspects
        • Article
        • Peer Reviewed
        UC Berkeley Previously Published Works (2013)
        The power grid is incorporating high throughput sensor devices into power distribution networks. The future power grid needs to guarantee accuracy and responsiveness of applications that consume data from multiple sensor streams. The end-to-end performance and overall scalability of cyber-physical energy applications depend on the middleware's ability to handle multi-source sensor data, which exhibits uncertain behavior under highly variable numbers of sensors and middleware topologies. In this paper, we present a parametric approach to model middleware uncertainty and to analyze its effect on distributed power applications. The models encapsulate the entire data flow paths from sensor devices, through network and middleware components to the power application nodes that utilize sensor data streams. Using the Ptolemy II framework for modeling and simulation, we generate Monte Carlo samples of uncertain parameters that are used to generate parameterized middleware models that are used in end-to-end Discrete-Event(DE) system simulation simulation. The simulation results are further analyzed using regression methods to reveal the parameters that are influential in the limiting middleware behavior to achieve temporal requirements of the power applications. © 2013 ACM.
          Cover page: Modeling uncertainty for middleware-based streaming power grid applications
          • Article
          • Peer Reviewed
          UC Berkeley Previously Published Works (2016)
          We consider the problem of generating randomized control sequences for complex networked systems typically actuated by human agents. Our approach leverages a concept known as control improvisation, which is based on a combination of data-driven learning and controller synthesis from formal specifications. We learn from existing data a generative model (for instance, an explicit-duration hidden Markov model, or EDHMM) and then supervise this model in order to guarantee that the generated sequences satisfy some desirable specifications given in Probabilistic Computation Tree Logic (PCTL). We present an implementation of our approach and apply it to the problem of mimicking the use of lighting appliances in a residential unit, with potential applications to home security and resource management. We present experimental results showing that our approach produces realistic control sequences, similar to recorded data based on human actuation, while satisfying suitable formal requirements.
            Cover page: Control Improvisation with Probabilistic Temporal Specifications
            • Article
            • Peer Reviewed
            UC Berkeley Previously Published Works (2016)
            We address the problem of mining musical specifications from a training set of songs and using these specifications in a machine improvisation system capable of generating improvisations imitating a given style of music. Our inspiration comes from control improvisation, which combines learning and synthesis from formal specifications. We mine specifications from symbolic musical data with musical and general usage patterns. We use the mined specifications to ensure that an improvised musical sequence satisfies desirable properties given a harmonic context and phrase structure. We present a specification mining strategy based on pattern graphs and apply it to the problem of supervising the improvisation of blues songs. We present an analysis of the mined specifications and compare the results of improvisations generated with and without specifications.
              Cover page: Specification Mining for Machine Improvisation with Formal Specifications
              • Article
              • Peer Reviewed
              UC Berkeley Previously Published Works (2016)
                Cover page: Control Improvisation with Probabilistic Temporal Specifications.
                • Article
                • Peer Reviewed
                UC Berkeley Previously Published Works (2015)
                System simulation is a valuable tool to unveil inefficiencies and to test new strategies when implementing and revising systems. Often, simulations are parameterized using offline data and heuristic knowledge. Operational data, i.e., data gained through experimentation and observation, can greatly improve the fidelity between the actual system and the simulation. In a traffic scenario, for example, different road conditions or vehicle types can impact the outcome of the simulation and have to be considered during the modeling stage. This paper proposes using machine learning techniques to generate high fidelity simulation models. A traffic simulation case study exemplifies this approach by generating a model for the SUMO traffic simulator from vehicular telemetry data.
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