Developing a distributed controller system for pro-duction software defined networks (SDN) requires substantial en-gineering effort to satisfy the stringent performance, adaptability, availability and security requirements that are well beyond the basic function development. The softwarization nature of SDN provides the opportunity to leverage the recent advancements in software engineering and system automation. In this paper we present our recent work to develop and deploy a wide-area SDN control framework prototype towards production deployment and operation. We primarily focus on three critical areas (1) enhancement of the software functions and the substrate virtualization configurations to fully support advanced connection services and fault tolerance in both data plane and control plane (2) a high-fidelity testing pipeline con-sisting of unit tests emulation and a testbed and (3) a continuous integration and continuous deployment (CI/CD) pipeline. Our experience proved that the presented environment and process greatly increased the software quality and development efficiency which would ultimately lead to a reliable and continuous deploy-ment and automation of the targeted production SDN network.

In this paper we describe an architecture developed and prototyped in the course of the NSF-funded project called ImPACT—Infrastructure for Privacy-Assured CompuTations. This architecture addresses the common problems that arise from the need to securely store, control access to and process privacy-restricted data in a multi-institutional, multi-stakeholder setting. Specifically the architecture includes several components—a way to publicly advertise a limited set of data attributes without exposing the sensitive data itself; a set of mechanisms for a data owner to specify and automatically enforce complex data-access policies commonly expressed today as Data Use Agreements (DUAs); a way to securely collect digital attestations from multiple stakeholders to satisfy those policies; and a reproducible template to deploy secure processing enclaves in which groups of researchers can analyze the data in a way that complies with data owner policies using the tools of their choice. The paper describes the architecture and its instantiation in a prototype, providing a performance evaluation of several components.

A majority of today’s cloud services are independently operated by individual cloud service providers. In this approach, the locations of cloud resources are strictly constrained by the distribution of cloud service providers’ sites. As the popularity and scale of cloud services increase, we believe this traditional paradigm is about to change toward further federated services, a.k.a., multi-cloud, due to the improved performance, reduced cost of compute, storage and network resources, as well as increased user demands. In this paper, we present COMET, a lightweight, distributed storage system for managing metadata on large scale, federated cloud infrastructure providers, end users, and their applications (e.g. HTCondor Cluster or Hadoop Cluster). We showcase use case from NSF’s, Chameleon, ExoGENI and JetStream research cloud testbeds to show the effectiveness of COMET design and deployment.

We present NIMS3D, a novel 3-D cabled robot for actuated sensing applications. We provide a brief overview of the main hardware components. Next, we describe installation procedures, including novel calibration methods, that enable rapid in-field deployability for nonexpert end users, and provide simulations and experimental results to highlight their effectiveness. Kinematic and dynamic analysis of the system are provided, followed by a description of control methods. We provide experimental results that illustrate tracking of linear and nonlinear paths by NIMS3D. Thereafter, we briefly present an example of an actuated sensing task performed by the system. Finally, we describe methods of improving energy efficiency by leveraging nonlinear trajectories and energy-optimal tension distributions. Experimental and simulated results show that energy efficiency can be improved significantly by using optimized parabolic trajectories. Furthermore, we provide simulation results that demonstrate improved efficiency enabled by optimal, least norm tension distributions.

We present the Networked InfoMechanical System for Planar Translation, which is a novel two-degree-of-freedom (2-DOF) cable-driven robot with self-calibration and online drift-correction capabilities. This system is intended for actuated sensing applications in aquatic environments. The actuation redundancy resulting from in-plane translation driven by four cables results in an infinite set of tension distributions, thus requiring real-time computation of optimal tension distributions. To this end, we have implemented a highly efficient, iterative linear programming solver, which requires a very small number of iterations to converge to the optimal value. In addition, two novel self-calibration methods have been developed that leverage the robot's actuation redundancy. The first uses an incremental displacement, or jitter method, whereas the second uses variations in cable tensions to determine end-effector location. We also propose a novel least-squares drift-detection algorithm, which enables the robot to detect long-term drift. Combined with self-calibration capabilities, this drift-monitoring algorithm enables long-term autonomous operation. To verify the performance of our algorithms, we have performed extensive experiments in simulation and on a real system.

In this paper we describe an algorithm to generate energy efficient trajectories for NIMS3D, a three-dimensional cabled robotic platform. Optimized parabolic paths are used to exploit the relatively low I2R loss associated with operation in lower regions of the workspace. Trajectory optimization is sufficiently fast to enable real time operation. Experimental results on a physical system for a three cable deployment show substantial reductions in energy consumption as compared to linear trajectories.

As concern for water resource availability increases, so does the need for intelligent aquatic sensing applications. The requirements, and complexity of such applications has also increased due to demands for: 1) broad spatial coverage and high spatial resolution monitoring, 2) capability for resolving fine scale spatiotemporal dynamics and 3) the need for rapid system deployment with semi-autonomous operation. With these criteria in mind, we present the Aquatic Networked InfoMechanical System (NIMS-AQ). NIMS-AQ was developed based on experience gained from engineering research and collaboration with aquatic scientists and environmental engineers during several in-field measurement campaigns [1], [2], [3]. In this paper we demonstrate the effectiveness of NIMS- AQ through two experimental sensing campaigns encompassing both river and lake environments. Each campaign is centered around critical water resource monitoring objectives such as temperature, flow and contaminant levels. Experimental results for autonomous depth profiling using a submersible sonar system as well as adaptive sampling algorithms guided by phenomena models are presented herein. The found results conform with our objectives for rapid and systematic operation. Preliminary studies also indicate the systems viability for use with an autonomous iterative experiment design for environmental applications (A-IDEA) methodology that is currently under development. The IDEA methodology [1] provides effective characterization of spatiotemporal dynamics in aquatic environments. A-IDEA, as it is to be implemented on the NIMS-AQ platform, is also described.

Distributed, high-density spatiotemporal observations are proposed for answering many river related questions, including those pertaining to hydraulics and multi-dimensional river modeling, geomorphology, sediment transport and riparian habitat restoration. In spite of the recent advancements in technology, currently available systems have many constraints that preclude long term, remote, autonomous, high resolution monitoring in the real environment. We present here a case study of an autonomous, high resolution robotic spatial mapping of cross-sectional velocity and salt concentration in a river basin. The scientific objective of this investigation was to characterize the transport and mixing phenomena at the confluence of two distinctly different river streams - San Joaquin River and its tributary Merced River. Several experiments for analyzing the spatial and temporal trends at multiple cross-sections of the San Joaquin River were performed during the campaign from August 21-25, 2006. These include deterministic dense raster scans and in-field adapted experimental design. Preliminary analysis from these experiments illustrating the range of investigations is presented with the focus on adaptive experiments that enable sparse sampling to provide larger spatial coverage without discounting the dynamics in the phenomena. Lessons learned during the campaign are discussed to provide useful insights for similar robotic investigations in aquatic environments.

In this paper we present algorithms that enable precise trajectory control of NIMS3D, an underconstrained, three-dimensional cabled robot intended for use in actuated sensing. We begin by offering a brief system overview and then describe methods to determine the range of operation of the robot. Next, a discrete-time model of the system is presented. Thereafter, we present an online algorithm for modeling motor behavior. The majority of the paper is dedicated to describing three feedback control laws used to enable accurate trajectory tracking for both linear and non-linear motion profiles. We present experimental results that highlight the strengths and weaknesses of these mechanisms and conclude by offering a series of future plans for NIMS3D.

In this paper, we present NIMS3D, a novel, rapidly deployable cable based robotic system capable of accurate positioning within its 3-dimensional span. The system is designed for indoor and outdoor use. In NIMS3D, a node moves via three cables which enable navigation in the 3D volume spanned by the system. The hardware is composed primarily of commercially available components and the software consists of three tiers: low level motor control, motion planning, and user interface. The proposed system has health monitoring capabilities that seek to ensure that robot integrity is not compromised. We provide theoretical and empirical analysis of system characteristics and present results that advocate its use for a variety of applications such as topographical and optical intensity mapping. Finally, we propose a number of future enhancements and plans for the system

Many environmental applications require high temporal frequency (rapidly changing) and spatially distributed phenomena to be sampled with high fidelity. This requires mobile sensing elements to perform guided sampling in regions of high variability. We propose a multiscale approach for efficiently sampling such phenomena. This approach introduces a hierarchy of sensors according to the sampling fidelity, spatial coverage, and mobility characteristics. In this paper, we report the development of a two-tier multiscale system where information from a low-fidelity, high spatial (global) sensor actuates a mobile robotic node, carrying a high-fidelity, low spatial coverage (spot measurement) sensor, to perform guided sampling in the regions of high phenomenon variability. As a case study of the proposed multiscale paradigm, we investigated the spatiotemporal distribution of the light intensity in a forest understory. The performance of the multiscale approach is verified in simulation and on a physical system. Results suggest that our approach is adequate for the problem of high-frequency spatiotemporal phenomena sampling and significantly outperforms traditional sampling approaches such as a raster scan