Ilge Akkaya

Systems Engineering for Industrial Cyber–Physical Systems Using Aspects

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.

Coordinated Actors for Reliable Self-adaptive Systems

Self-adaptive systems are systems that automatically adapt in response to environmental and internal changes, such as possible failures and variations in resource availability. Such systems are often realized by a MAPE-K feedback loop, where Monitor, Analyze, Plan and Execute components have access to a runtime model of the system and environment which is kept in the Knowledge component. In order to provide guarantees on the correctness of a self-adaptive system at runtime, the MAPE-K feedback loop needs to be extended with assurance techniques. To address this issue, we propose a coordinated actor-based approach to build a reusable and scalable model@runtime for self-adaptive systems in the domain of track-based traffic control systems. We demonstrate the approach by implementing an automated Air Traffic Control system (ATC) using Ptolemy tool. We compare different adaptation policies on the ATC model based on performance metrics and analyze combination of policies in different configurations of the model. We enriched our framework with runtime performance analysis such that for any unexpected change, subsequent behavior of the model is predicted and results are used for adaptation at the change-point. Moreover, the developed framework enables checking safety properties at runtime.

Model-based evaluation of GPS spoofing attacks on power grid sensors

Emerging cyber-physical system (CPS) applications require reliable time synchronization to enable distributed control and sensing applications. However, time reference signals are vulnerable to attacks that could remain undetected for a long time. Sensor-rich distributed CPS such as the “smart grid” highly rely on GPS and similar time references for sub-station clock synchronization. The vulnerability of time synchronization protocols to spoofing attacks is a potential risk factor that may lead to falsified sensor readings and, at a larger scale, may become hazardous for system safety. This paper describes a simulation-based assessment of the effect of time accuracy on time-centric power system applications. In particular, the vulnerability of power grid sensors to erroneous time references and the potential risks of time-base spoofing on power grid health are studied, using the Ptolemy modeling and simulation tool. Both the false alarm and the missed generation scenarios are considered, where the GPS spoofer may lead the substation to declare an erroneous out-of-phase situation, or the substation may be disabled to detect anomalies that are present in the incoming phase data.

A particle filter framework for the estimation of heart rate from ECG signals corrupted by motion artifacts.

In this work, we describe a methodology to probabilistically estimate the R-peak locations of an electrocardiogram (ECG) signal using a particle filter. This is useful for heart rate estimation, which is an important metric for medical diagnostics. Some scenarios require constant in-home monitoring using a wearable device. This poses a particularly challenging environment for heart rate detection, due to the susceptibility of ECG signals to motion artifacts. In this work, we show how the particle filter can effectively track the true R-peak locations amidst the motion artifacts, given appropriate heart rate and R-peak observation models. A particle filter based framework has several advantages due to its freedom from strict assumptions on signal and noise models, as well as its ability to simultaneously track multiple possible heart rate hypotheses. Moreover, the proposed framework is not exclusive to ECG signals and could easily be leveraged for tracking other physiological parameters. We describe the implementation of the particle filter and validate our approach on real ECG data affected by motion artifacts from the MIT-BIH noise stress test database. The average heart rate estimation error is about 5 beats per minute for signal streams contaminated with noisy segments with SNR as low as -6 dB.

Modeling and Simulation of Network Aspects for Distributed Cyber-Physical Energy Systems

Electric power grids are presently being integrated with sensors that provide measurements at high rates and resolution. The abundance of sensor measurements, as well as the added complexity of applications trigger a demand for cyber-physical system (CPS) modeling and simulation for evaluating the characteristics of appropriate network fabrics, timing profiles and distributed application workflow of power applications. Although simulation aids in the pre-deployment decision making process, system models for complex CPS can quickly become impractical for the purposes of specialized evaluation of design aspects. Existing modeling techniques are inadequate for capturing the heterogeneous nature of CPS and tend to inherently couple orthogonal design concerns. To address this issue, we present an aspect-oriented modeling and simulation paradigm. The aspectoriented approach provides a separation between functional models and crosscutting modeling concerns such as network topology, latency profiles, security aspects, and quality of service (QoS) requirements. As a case study, we consider a three-area smart grid topology and demonstrate the aspect-oriented approach to modeling network and middleware behavior for a distributed state estimation application. We also explore how aspects leverage scalable co-simulation, fault modeling, and middleware-in-the loop simulation for complex smart grid models.

PTIDES model on a distributed testbed emulating smart grid real-time applications

PTIDES, a programming model for distributed real-time systems, was proposed previously. The model captures both the functionality of the system and the desired timing of interactions with the environment. The PTIDES simulator supports simulation of both of these aspects. In this work, we focus on the PTIDES development environment in the context of applications drawn from the control of electric power systems. The evaluation is based on experiments on a system of distributed computing platforms emulating typical power system control and monitoring devices and an emulation of portions of the electric power grid based on conventional micro-controller instrumentation.

Uncertainty Analysis of Middleware Services for Streaming Smart Grid Applications

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.

Modeling and analysis of middleware design for streaming power grid applications

High quality, high throughput sensor devices in the power distribution network are driving an increase in the volume and the rate of data streams available to monitor and control the power grid. Middleware support is essential to coordinate data streams with distributed power models and adapt to situations with data communication failures and errors in the sensor measurements. One challenge in designing this middleware support is scalability. In particular, the number of sensor devices and their intercommunications is a significant factor in determining temporal and functional properties of power models such as distributed state estimation. In this paper, we present our experience modeling the entire data flow from sensor devices to distributed state estimators using middleware. This model helps to analyze the middlewares behavior and its scalability in coordinating data streams.

Modeling uncertainty for middleware-based streaming power grid applications

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 middlewares 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.

Coordinated actor model of self-adaptive track-based traffic control systems

Abstract Self-adaptation is a well-known technique to handle growing complexities of software systems, where a system autonomously adapts itself in response to changes in a dynamic and unpredictable environment. With the increasing need for developing self-adaptive systems, providing a model and an implementation platform to facilitate integration of adaptation mechanisms into the systems and assuring their safety and quality is crucial. In this paper, we target Track-based Traffic Control Systems (TTCSs) in which the traffic flows through pre-specified sub-tracks and is coordinated by a traffic controller. We introduce a coordinated actor model to design self-adaptive TTCSs and provide a general mapping between various TTCSs and the coordinated actor model. The coordinated actor model is extended to build large-scale self-adaptive TTCSs in a decentralized setting. We also discuss the benefits of using Ptolemy II as a framework for model-based development of large-scale self-adaptive systems that supports designing multiple hierarchical MAPE-K feedback loops interacting with each other. We propose a template based on the coordinated actor model to design a self-adaptive TTCS in Ptolemy II that can be instantiated for various TTCSs. We enhance the proposed template with a predictive adaptation feature. We illustrate applicability of the coordinated actor model and consequently the proposed template by designing two real-life case studies in the domains of air traffic control systems and railway traffic control systems in Ptolemy II.