Biblio

This study contributes to the analysis of civil society and knowledge by examining mobilizations by civil society organizations and grassroots networks in opposition to wireless smart meters in the United States. Three types of mobilizations are reviewed: grassroots anti-smart-meter networks, privacy organizations, and organizations that advocate for reduced exposure to non-ionizing electromagnetic fields. The study shows different relationships to scientific knowledge that include publicizing risks and conducting citizen science, identifying non-controversial areas of future research, and pointing to deeper problems of undone science (a particular type of non-knowledge that emerges when actors mobilize in the public interest and find an absence or low volume of research that could have been used to support their concerns). By comparing different types of knowledge claims made by the civil society organizations and networks, the study examines the conditions under which mobilized civil society generates positive responses from incumbent organizations versus resistance and undone science.

Decentralized planning for multi-agent systems,such as fleets of robots in a search-and-rescue operation, is oftenconstrained by limitations on how agents can communicate witheach other. One such limitation is the case when agents cancommunicate with each other only when they are in line-of-sight (LOS). Developing decentralized planning methods thatguarantee safety is difficult in this case, as agents that areoccluded from each other might not be able to communicateuntil it’s too late to avoid a safety violation. In this paper, wedevelop a decentralized planning method that explicitly avoidssituations where lack of visibility of other agents would leadto an unsafe situation. Building on top of an existing Rapidly-exploring Random Tree (RRT)-based approach, our methodguarantees safety at each iteration. Simulation studies showthe effectiveness of our method and compare the degradationin performance with respect to a clairvoyant decentralizedplanning algorithm where agents can communicate despite notbeing in LOS of each other.

Simultaneous Localization and Mapping (SLAM) algorithms perform visual-inertial estimation via filtering or batch optimization methods. Empirical evidence suggests that filtering algorithms are computationally faster, while optimization methods are more accurate. This work presents an optimization-based framework that unifies these approaches, and allows users to flexibly implement different design choices, e.g., the number and types of variables maintained in the algorithm at each time. We prove that filtering methods correspond to specific design choices in our generalized framework. We then reformulate the Multi-State Constrained Kalman Filter (MSCKF), implement the reformulation on challenging image sequence datasets in simulation, and contrast its performance with that of sliding window based filters. Using these results, we explain the relative performance characteristics of these two classes of algorithms in the context of our algorithm. Finally, we illustrate that under different design choices, the empirical performance of our algorithm interpolates between those of state-of-the-art approaches.

Cyber-physical systems (CPS) are smart systems that consist of highly interconnected networks of physical and computational components. The tight integration of a wide range of heterogeneous components enables new functionality and quality of life improvements in critical infrastructures such as smart cities, intelligent buildings, and smart energy systems. One approach to study CPS uses both simulations and hardware-in-theloop (HIL) to test the physical dynamics of hardware in a controlled environment. However, because CPS experiment design may involve domain experts from multiple disciplines who use different simulation tool suites, it can be a challenge to integrate the heterogeneous simulation languages and hardware interfaces into a single HIL simulation. The National Institute of Standards and Technology (NIST) is working on the development of a universal CPS environment for federation (UCEF) that can be used to design and run experiments that incorporate heterogeneous physical and computational resources over a wide geographic area. This development environment uses the High Level Architecture (HLA), which the Department of Defense has advocated for co-simulation in the field of distributed simulations, to enable communication between hardware and different simulation languages such as Simulink and LabVIEW. This paper provides an overview of UCEF and motivates how the environment could be used to develop energy applications using an illustrative example of an emulated heat pump system.

The introduction of connected and autonomous vehicles enables new possibilities in vehicle routing: Knowing the origin and destination of each vehicle in the network can allow for coordinated real-time routing of the vehicles to optimize network performance. However, this relies on individual vehicles being “altruistic,” i.e., willing to accept alternative less-preferred routes. We conduct a study to compare different levels of agent altruism in decentralized vehicles coordination and the effect on the network-level traffic performance. This work introduces novel load-balancing scenarios of traffic flow in real-world cities for varied levels of agent altruism. We show evidence that the new decentralized optimization router is more effective with networks of high load.

The NIST Transactive Energy (TE) Modeling and Simulation Challenge for the Smart Grid (Challenge) spanned from 2015 to 2018. The TE Challenge was initiated to identify simulation tools and expertise that might be developed or combined in co-simulation platforms to enable the evaluation of transactive energy approaches. Phase I of the Challenge spanned 2015 to 2016, with team efforts that improved understanding of TE concepts, identified relevant simulation tools and co-simulation platforms, and inspired the development of a TE co-simulation abstract component model that paved the way for Phase II. The Phase II effort spanned Spring 2017 through Spring 2018, where the teams collaboratively developed a specific TE problem scenario, a common grid topology, and common reporting metrics to enable direct comparison of results from simulation of each team's TE approach for the defined scenario. This report presents an overview of the TE Challenge, the TE abstract component model, and the common scenario. It also compiles the individual Challenge participants' research reports from Phase II. The common scenario involves a weather event impacting a distribution grid with very high penetration of photovoltaics, leading to voltage regulation challenges that are to be mitigated by TE methods. Four teams worked with this common scenario and different TE models to incentivize distributed resource response to voltage deviations, performing these simulations on different simulation platforms. A fifth team focused on a co-simulation platform that can be used for online TE simulations with existing co-simulation components. The TE Challenge Phase II has advanced co-simulation modeling tools and platforms for TE system performance analysis, developed a referenceable TE scenario that can support ongoing comparative simulations, and demonstrated various TE approaches for managing voltage on a distribution grid with high penetration of photovoltaics.

Simulation-based analysis is essential in the model-based design process of Cyber-Physical Systems (CPS). Since heterogeneity is inherent to CPS, virtual prototyping of CPS designs and the simulation of their behavior in various environments typically involve a number of physical and computation/ communication domains interacting with each other. Affordability of the model-based design process makes the use of existing domain-specific modeling and simulation tools all but mandatory. However, this pressure establishes the requirement for integrating the domain-specific models and simulators into a semantically consistent and efficient system-of-system simulation. The focus of the paper is the interoperability of popular integration platforms supporting heterogeneous multi-model simulations. We examine the relationship among three existing platforms: the High-Level Architecture (HLA)-based CPS Wind Tunnel (CPSWT), MOSAIK, and the Functional Mockup Unit (FMU). We discuss approaches to establish interoperability and present results of ongoing work in the context of an example.

While automated driving techniques are increasingly capturing the market, it is particularly important to consider vital functional properties of these systems. We introduce an approach to logically reason about functional properties of crossing maneuvers at intersections. To this end, we introduce an abstract model for urban traffic situations and extended timed automata crossing controllers using formulas of our traffic logic Urban Multi-lane Spatial Logic (UMLSL) for turn maneuvers at intersections. We show that even at complex intersections we can use purely spatial reasoning, detached from the underlying car dynamics, to prove safety (collision freedom) of the crossing controllers. We also examine liveness (something good finally happens) and fairness (no queue-jumping) of the controllers with the help of UPPAAL, a model checker for (extended) timed automata. Furthermore, we introduce a case study, where we adapt the approach to a hazard warning communication protocol.

Accountability is an often called for property of technical systems. It is a requirement for algorithmic decision systems, autonomous cyber-physical systems, and for software systems in general. As a concept, accountability goes back to the early history of Liberalism and is suggested as a tool to limit the use of power. This long history has also given us many, often slightly differing, definitions of accountability. The problem that software developers now face is to understand what accountability means for their systems and how to reflect it in a system's design. To enable the rigorous study of accountability in a system, we need models that are suitable for capturing such a varied concept. In this paper, we present a method to express and compare different definitions of accountability using Structural Causal Models. We show how these models can be used to evaluate a system's design and present a small use case based on an autonomous car.