Dual Rate Control for Security in Cyber-physical Systems

We consider malicious attacks on actuators and sensors of a feedback system which can be modeled as additive, possibly unbounded, disturbances at the digital (cyber) part of the feedback loop. We precisely characterize the role of the unstable poles and zeros of the system in the ability to detect stealthy attacks in the context of the sampled data implementation of the controller in feedback with the continuous (physical) plant. We show that, if there is a single sensor that is guaranteed to be secure and the plant is observable from that sensor, then there exist a class of multirate sampled data controllers that ensure that all attacks remain detectable. These dual rate controllers are sampling the output faster than the zero order hold rate that operates on the control input and as such, they can even provide better nominal performance than single rate, at the price of higher sampling of the continuous output

Target Assignment in Robotic Networks: Distance Optimality Guarantees and Hierarchical Strategies

We study the problem of multi-robot target assignment to minimize the total distance traveled by the robots until they all reach an equal number of static targets. In the first half of the paper, we present a necessary and sufficient condition under which true distance optimality can be achieved for robots with limited communication and target-sensing ranges. Moreover, we provide an explicit, non-asymptotic formula for computing the number of robots needed to achieve distance optimality in terms of the robots' communication and target-sensing ranges with arbitrary guaranteed probabilities. The same bounds are also shown to be asymptotically tight. In the second half of the paper, we present suboptimal strategies for use when the number of robots cannot be chosen freely. Assuming first that all targets are known to all robots, we employ a hierarchical communication model in which robots communicate only with other robots in the same partitioned region. This hierarchical communication model leads to constant approximations of true distance-optimal solutions under mild assumptions. We then revisit the limited communication and sensing models. By combining simple rendezvous-based strategies with a hierarchical communication model, we obtain decentralized hierarchical strategies that achieve constant approximation ratios with respect to true distance optimality. Results of simulation show that the approximation ratio is as low as 1.4

Passenger Flows in Underground Railway Stations and Platforms, MTI Report 12-43

Urban rail systems are designed to carry large volumes of people into and out of major activity centers. As a result, the stations at these major activity centers are often crowded with boarding and alighting passengers, resulting in passenger inconvenience, delays, and at times danger. This study examines the planning and analysis of station passenger queuing and flows to offer rail transit station designers and transit system operators guidance on how to best accommodate and manage their rail passengers. The objectives of the study are to: 1) Understand the particular infrastructural, operational, behavioral, and spatial factors that affect and may constrain passenger queuing and flows in different types of rail transit stations; 2) Identify, compare, and evaluate practices for efficient, expedient, and safe passenger flows in different types of station environments and during typical (rush hour) and atypical (evacuations, station maintenance/ refurbishment) situations; and 3) Compile short-, medium-, and long-term recommendations for optimizing passenger flows in different station environments

Moving from Walkability? Evaluation Traditional and Merging Data Sources for Evaluating Changes in Campus-Generated Greenhouse Gas Emissions

Universities are increasingly committing to reduce campus-generated greenhouse gas emissions, whether voluntarily or in response to a legal mandate. As an initial step to keeping these commitments, universities need an accounting of baseline greenhouse gas emissions levels and means of monitoring changes in campus-generated greenhouse gas emissions over time. Commute-generated greenhouse gas emissions from travel to and from campus by students and employees are among the most difficult to quantify. This report examines some of the challenges associated with estimating campus-generated greenhouse gas emissions and evaluates ways to address those challenges. The purpose of this study is to identify changes in campus-generated travel behavior at California Polytechnic State University based on the results of three successive campus-wide travel surveys; to evaluate alternative data sources that have the potential to supplement or replace campus travel surveys as a source of data for campus-generated greenhouse gas emissions; and to evaluate alternate methods to estimating greenhouse gas emissions from campus-generated vehicle miles traveled, depending on the presence of campus-specific information about vehicle fleet characteristics. The results of successive travel surveys suggest that the campus population has become more car-dependent over time. Comparison of survey results with data collected from automating traffic counting devices and mobile device data suggest that surveys that are limited to members of the campus community are likely to undercount campus-generated vehicle miles traveled by excluding infrequent, but potentially long, trips by campus visitors. Finally, we find that using campus-specific information on the model years of vehicles used to commute to campus yields higher estimates of campus-generated greenhouse gas emissions, relative to average regional emissions rates