DDDAS Research Articles

Reinforcement learning-based prediction approach for distributed Dynamic Data-Driven Application Systems

With the current advances in cloud and distributed system technology, data have become ubiquitous and their dynamics has increased. It is an extreme challenge to find the interdependencies among distributed data in order to dynamically manage and predict the trend within large amounts of data sources. This paper proposes a new distributed dynamic data-driven model and strategy to direct and evaluate the interlinked data sets in Dynamic Data-Driven Application Systems (DDDAS). The underlying technique involves the introduction of a reinforcement Q-Learning approach which includes search strategies to determine how to drill and drive a series of highly dependent data in order to enhance prediction accuracy and efficiency. It can tackle dynamic data issues in a real-time, dynamic and resource-bounded environment. The proposed framework is a comprehensive skeleton for modeling complex, flexible and dynamic tasks in a distributed environment for solving DDDAS problems. In simulation, the new model utilizes individual sensors, distributed databases and predictors in Dynamic Data Stream Nodes with multiple dimensional variables which can be instantiated to explore the search space, thereby improving the search convergence. This study shows the effectiveness and applicability of using the technique in the analysis of typhoon rainfall data. The result shows that the proposed approach performed better than traditional linear regression approaches, reducing the error rate by 36.34 %.

Application of dynamic data driven application system in environmental science

The paradigm of dynamic data driven application system (DDDAS) has been proposed as a framework to analyze and predict the character and behavior of complex systems that influence computational models significantly. Its accuracy and efficiency lies in its ability to integrate observations on different temporal and spatial scales from real-time sensors, and in its measurement steering and controlling capabilities. Many problems in environmental sciences are nonlinear and complex, impossible to solve by using input/output sequence flows without feedback control. Nonlinear system efficiency depends on measurement control and steering, on-line data assimilation, and model selection with dynamic optimization. Compared with traditional methods, DDDAS possesses the capacity to overcome these limitations. This paper discusses DDDAS and classifies typical cases of its application in environmental sciences into three levels of paradigm. Short reviews of multi-model simulation and data assimilation are provided for practical use. Recent developments and future perspectives are reviewed. Future work may address determining automatically where, when, and how to acquire real-time data, and its integration with GIS, to improve efficiency and accuracy. User-generated content will find wide application in the future. Considering the differences between DDDAS and other data-driven methods in solving the same nonlinear complex system problems, a combination of nonlinear science and chaos theory is advocated.

A computational framework for dynamic data‐driven material damage control, based on Bayesian inference and model selection

SummaryIn the present study, a general dynamic data‐driven application system (DDDAS) is developed for real‐time monitoring of damage in composite materials using methods and models that account for uncertainty in experimental data, model parameters, and in the selection of the model itself. The methodology involves (i) data data from uniaxial tensile experiments conducted on a composite material; (ii) continuum damage mechanics based material constitutive models; (iii) a Bayesian framework for uncertainty quantification, calibration, validation, and selection of models; and (iv) general Bayesian filtering, as well as Kalman and extended Kalman filters. A software infrastructure is developed and implemented in order to integrate the various parts of the DDDAS. The outcomes of computational analyses using the experimental data prove the feasibility of the Bayesian‐based methods for model calibration, validation, and selection. Moreover, using such DDDAS infrastructure for real‐time monitoring of the damage and degradation in materials results in results in an improved prediction of failure in the system. Copyright © 2014 John Wiley & Sons, Ltd.

Control of Artificial Swarms with DDDAS

A framework for incorporating a swarm intelligent system with the Dynamic Data Driven Application System (DDDAS) is presented. Swarm intelligent systems, or artificial swarms, self-organize into useful emergent structures that are capable of solving complex problems, but are difficult to control and predict. The DDDAS concept utilizes repeated simulations of an executing application to improve analytic and predictive capability by creating a synergistic feedback loop. Incorporating DDDAS with swarm applications can significantly improve control of the swarm. An overview of the DDDAS framework for swarm control is presented, and then demonstrated with an example swarm application.

An Open Framework for Dynamic Big-data-driven Application Systems (DBDDAS) Development

In this paper, we outline key features that dynamic data-driven application systems (DDDAS) have. A DDDAS is an application that has data assimilation that can change the models and/or scales of the computation and that the application controls the data collection based on the computational results. The term Big Data (BD) has come into being in recent years that is highly applicable to most DDDAS since most applications use networks of sensors that generate an overwhelming amount of data in the lifespan of the application runs. We describe what a dynamic big-data-driven application system (DBDDAS) toolkit must have in order to provide all of the essential building blocks that are necessary to easily create new DDDAS without re-inventing the building blocks.

A Dynamic Data Driven Application System for Vehicle Tracking

Tracking the movement of vehicles in urban environments using fixed position sensors, mobile sensors, and crowd-sourced data is a challenging but important problem in applications such as law enforcement and defense. A dynamic data driven application system (DDDAS) is described to track a vehicle's movements by repeatedly identifying the vehicle under investigation from live image and video data, predicting probable future locations, and repositioning sensors or retargeting requests for information in order to reacquire the vehicle. An overview of the envisioned system is described that includes image processing algorithms to detect and recapture the vehicle from live image data, a computational framework to predict probable vehicle locations at future points in time, and a power aware data distribution management system to disseminate data and requests for information over ad hoc wireless communication networks. A testbed under development in the midtown area of Atlanta, Georgia in the United States is briefly described.

Static Versus Dynamic Data Information Fusion Analysis Using DDDAS for Cyber Security Trust

Information fusion includes signals, features, and decision -level analysis over various types of data including imagery, te xt, and cyber security detection. With the maturity of data processing, the e xplosion of big data, and the need fo r user acceptance; the Dynamic Data-Driven Application System (DDDAS) philosophy fosters insights into the usability of information systems solutions. In this paper, we e xp lore a notion of an adaptive adjustment of secure communication trust analysis that seeks a balance between standard static solutions versus dynamic -data driven updates. A use case is provided in determin ing trust for a cyber security scenario exp loring comparisons of Bayesian versus evidential reasoning for dynamic security detection updates. Using the evidential reasoning proportional conflict redistribution (PCR) method, we demonstrate improved trust for dynamically changing detections of denial of service attacks.

A DYNAMIC DATA-DRIVEN APPLICATION SIMULATION MODEL FOR OIL SPILL EMERGENCY DECISION IN PORT WATER AREA

In order to solve the difficulty in simulation and prediction the evolution of oil spill incidents in port water area caused by system complexity and environment variety, a simulation model based on Dynamic Data-Driven Application System (DDDAS) was developed. By assimilating real-time data, simulation model can update its initial condition and solutions, and modify its parameters. Firstly, the framework for DDDAS-based simulation model was designed. Then the method for mapping of real-time data to simulation model, the method for recovery of initial data for DDDAS, and the algorithms to simulate the behavior of oil spill were studied. Finally, numerical tests were provided to illustrate the validity of the proposed model. Results indicate that the DDDAS-based simulation method can improve the prediction accuracy of oil spill incidents.

Dynamic Data Driven Application System for Plume Estimation Using UAVs

In this article, a full dynamic data-driven application system (DDDAS) is proposed for dynamically estimating a concentration plume and planning optimal paths for unmanned aerial vehicles (UAVs) eq...

Una estrategia basada en la descomposición propia generalizada para aplicaciones gobernadas por datos dinámicos

Dynamic Data Driven Application Systems (DDDAS) appear as a new paradigm in the field of applied sciences and engineering, and in particular in simulation-based engineering sciences. By DDDAS we mean a set of techniques that allow the linkage of simulation tools with measurement devices for real-time control of systems and processes. DDDAS entails the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process. DDDAS needs for accurate and fast simulation tools making use if possible of off-line computations for limiting as possible the on-line computations. We could define efficient solvers by introducing all the sources of variability as extra-coordinates in order to solve off-line only once the model to obtain its most general solution to be then considered in on-line purposes. However, such models result defined in highly multidimensional spaces suffering the so-called curse of dimensionality. We proposed recently a technique, the Proper Generalized Decomposition (PGD), able to circumvent the redoubtable curse of dimensionality. The marriage of DDDAS concepts and tools and PGD off-line computations could open unimaginable possibilities in the field of dynamics data-driven application systems. In this work we explore some possibilities in the context of parameter estimation.

Real-time estimation of TP load in a Mississippi Delta stream using a dynamic data driven application system

Elevated phosphorus (P) in surface waters can cause eutrophication of aquatic ecosystems and can impair water for drinking, industry, agriculture, and recreation. Currently, no effort has been devoted to estimating real-time variation and load of total P (TP) in surface waters due to the lack of suitable and/or cost-effective wireless sensors. However, when considering human health, drinking water supply, and rapidly developing events such as algal blooms, the availability of timely P information is very critical. In this study, we developed a new approach in the form of a dynamic data driven application system (DDDAS) for monitoring the real-time variation and load of TP in surface water. This DDDAS consisted of the following three major components: (1) a User Control that interacts with Schedule Run to implement the DDDAS with starting and ending times; (2) a Schedule Run that activates the Hydstra model; and (3) a Hydstra model that downloads the real-time data from a US Geological Survey (USGS) website that is updated every 15 min with data from USGS monitoring stations, predicts real-time variation and load of TP, graphs the variables in real-time on a computer screen, and sends email alerts when the TP exceeds a certain value. The DDDAS was applied to monitor real-time variation and load of TP for 30 days in Deer Creek, a stream located east of Leland, Mississippi, USA. Results showed that the TP concentrations in the stream ranged from 0.24 to 0.48 mg L−1 with an average of 0.30 mg L−1 for a 30-day monitoring period, whereas the cumulative load of TP from the stream was about 2.8 kg for the same monitoring period. Our study suggests that the DDDAS developed in this study was useful for estimating the real-time variation and load of TP in surface water ecosystems.

A Testbed for Investigating the UAV Swarm Command and Control Problem Using DDDAS

Unmanned Aerial Vehicles (UAVs) may become the future of military aviation as technology advances, especially sensors and miniaturization techniques. Currently, however, UAVs are controlled individually and require many resources, including ground-based pilots, to function. In our project, we attempt to explore how to remedy this using a Dynamic Data-Driven Application System (DDDAS) to control a group, or swarm, of UAVs. DDDAS takes real data and injects it into a running simulation, as well as allowing the running simulation to influence what real data is gathered, and as such is an ideal system to control real UAVs. We describe here how we created a testbed system that allowed two simulations to communicate data to one another using DDDAS principles, as well as the beginnings of incorporating commercially-available UAVs into the system.

Dynamic Data Driven Applications System Concept for Information Fusion

We present a framework of Information Fusion (IF) using the Dynamic Data Driven Applications Systems (DDDAS) concept. Existing literature at the intersection of these two topics supports environmental modeling (e.g., terrain understanding) for context enhanced applications. Taking advantage of sensor models, statistical methods, and situation- specific spatio-temporal fusion products derived from wide area sensor networks, DDDAS demonstrates robust multi-scale and multi-resolution geographical terrain computations. We highlight the complementary nature of these seemingly parallel approaches and propose a more integrated analytical framework in the context of a cooperative multimodal sensing application. In particular, we use a Wide-Area Motion Imagery (WAMI) application to draw parallels and contrasts between IF and DDDAS systems that warrants an integrated perspective. This elementary work is aimed at triggering a sequence of deeper insightful research towards exploiting sparsely sampled piecewise dense WAMI measurements – an application where the challenges of big-data with regards to mathematical fusion relationships and high-performance computations remain significant and will persist. Dynamic data-driven adaptive computations are required to effectively handle the challenges with exponentially increasing data volume for advanced information fusion systems solutions such as simultaneous target tracking and identification.

Challenges in Developing DDDAS based Methodology for Volcanic Ash Hazard Analysis – Effect of Numerical Weather Prediction Variability and Parameter Estimation

In this paper, we will present ongoing work on using a dynamic data driven application system (DDDAS) based approach to the forecast of volcanic ash transport and dispersal. Our primary modeling tool will be a new code puffin formed by the combination of a plume eruption model Bent and the ash transport model Puff. Data from satellite imagery, observation of vent parameters and windfields will drive our simulations. We will use ensemble based uncertainty quantification and parameter estimation methodology – polynomial chaos quadrature in combination with data integration to complete the DDDAS loop.

Building resilient cloud services using DDDAS and moving target defence

It is widely accepted that we cannot build cloud systems that are free from vulnerabilities and cannot be penetrated or attacked. Our approach to address cloud security challenges is based on using the dynamic data driven application system (DDDAS) and moving target defence (MTD) strategies to develop resilient cloud services (RCS). The use of the MTD strategy makes it extremely difficult for an attacker to exploit existing vulnerabilities by varying different aspects of the system execution environment. By continuously changing the execution environment based on the DDDAS paradigm to meet the dynamic changes in system and application security requirements, we can reduce the attack surface and consequently, the attackers will have very limited time to figure out the current execution environment and what vulnerabilities are to be exploited. The DDDAS-based resilient cloud services (DRCS) implementation utilises the following capabilities: software behaviour encryption (SBE), replication, diversity, automated checkpointing and recovery.

Swarm Control of UAVs for Cooperative Hunting with DDDAS

Swarm control is a problem of increasing importance with technological advancements. Recently, governments have begun employing UAVs for reconnaissance, including swarms of drones searching for evasive targets. An agent-based simulation for dynamic cooperative cleaning is augmented with additional behaviors and implemented into a Dynamic Data-Driven Applica- tion System (DDDAS) framework for dynamic swarm control.

Using Shape Memory Alloys: A Dynamic Data Driven Approach

Shape Memory Alloys (SMAs) are capable of changing their crystallographic structure due to changes of either stress or temperature. SMAs are used in a number of aerospace devices and are required in some devices in exotic environments. We are developing dynamic data driven application system (DDDAS) tools to monitor and change SMAs in real time for delivering payloads by aerospace vehicles. We must be able to turn on and off the sensors and heating units, change the stress on the SMA, monitor on-line data streams, change scales based on incoming data, and control what type of data is generated. The application must have the capability to be run and steered remotely as an unmanned feedback control loop.

Resilient Dynamic Data Driven Application Systems (rDDDAS)

There is a growing interest in Cloud Computing for delivering computing as a utility. Security in Cloud Computing is a challenging research problem because it involves many interdependent tasks including vulnerability scanning, application layer firewalls, configuration management, alert monitoring and analysis, source code analysis, and user identity management. It is widely accepted that we cannot build software and computing systems that are free from vulnerabilities and cannot be penetrated or attacked. Consequently, there is a strong interest in resilience approach because of its potential to address the cybersecurity challenges. Our is based on using the Dynamic Data Driven Application System (DDDAS) and Moving Target Defence (MTD) strategies to develop resilient DDDAS. The Resilient Applications utilize the following capabilities: Software Behaviour Encryption (SBE), Replication, Diversity, Automated Checkpointing and Recovery. Software Behaviour Encryption employs spatiotemporal behaviour encryption and a moving target defence to make active software components change their implementations and their resources randomly and consequently evade attackers. Diversity and random execution is achieved by “hot” shuffling multiple functionally- equivalent, behaviourally-different software versions at runtime (This encryption of the execution environment will make it extremely difficult for an attack to disrupt the normal operations of a cloud application. Also, the dynamic change in the execution environment will hide the software flaws that would otherwise be exploited by a cyberattacker. Checkpointing is used to save the current state of the task to a reliable storage and thus enabling rollback recovery if it is required to tolerate cyberattacks and mitigate their impacts. We use the Compiler for Portable Checkpointing (CPPC), a tool for automatically inserting portable checkpoints into the code.We also evaluate the performance and overhead of running three applications in our rDDDAS environment. Our experimental results show that the rDDDAS environment can be used to develop resilient cloud applications are resilient against attacks with around 7% in execution time overhead.

An Operation-Time Simulation Framework for UAV Swarm Configuration and Mission Planning

In recent years, Unmanned Aerial Vehicles (UAV), have been increasingly utilized by both military and civilian organizations because they are less expensive, provide greater flexibilities and remove the need for on-board pilot support. Largely due to their utility and increased capabilities, in the near future, swarms of UAVs will replace single UAV use. Efficient control of swarms opens a set of new challenges, such as automatic UAV coordination, efficient swarm monitoring and dynamic mission planning. In this paper, we investigate the problem of dynamic mission planning for a UAV swarm. A centralized-distributed hybrid control framework is proposed for mission assignment and scheduling. The Dynamic Data Driven Application System (DDDAS) principles are applied to the framework so that it can adapt to the changing nature of the environment and the missions. A prototype simulation program is implemented as a proof-of- concept of the framework. Experimentation with the framework suggests the effectiveness of swarm control for several mission planning mechanisms.

Dynamic Data Driven Sensor Array Fusion for Target Detection and Classification

Target detection and classification using unattended ground sensors (UGS) has been addressed in literature. Various tech- niques have been proposed for target detection, but target classification is a challenging task to accomplish using the limited processing power on each sensor module. The major hindrance in using these sensors reliably is, that, the sensor observations are significantly affected by external conditions, which are referred to as context. When the context is slowly time-varying (e.g., day-night cycling and seasonal variations) the usage of the same classifier may not be a good way to perform target classification. In this paper, a new framework is proposed as a Dynamic Data Driven Application System (DDDAS) to dy- namically extract and use the knowledge of context as feedback in order to adaptively choose the appropriate classifiers and thereby enhance the target classification performance. The features are extracted by symbolic dynamic filtering (SDF) from the time series of sensors in an array and spatio-temporal aggregation of these features represents the context. Then, a context evolution model is constructed as a deterministic finite state automata (DFSA) and, for every context state in this DFSA, an event classifier is trained to classify the targets. The proposed technique of detection and classification has been compared with a traditional method of training classifiers without using any contextual information.