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IEEE This article introduces a Piecewise-Deterministic Queueing (PDQ) model to study the stability of traffic queues in parallel-link transportation systems facing stochastic capacity fluctuations. The saturation rate (capacity) of the PDQ model switches between a finite set of modes according to a Markov chain, and link inflows are controlled by a state-feedback policy. A PDQ system is stable only if a lower bound on the time-average link inflows does not exceed the corresponding time-average saturation rate. Furthermore, a PDQ system is stable if the following two conditions hold: the nominal mode's saturation rate is high enough that all queues vanish in this mode, and a Bilinear Matrix Inequality (BMI) involving an underestimate of the discharge rates of the PDQ in individual modes is feasible. The stability conditions can be strengthened for two-mode PDQs. These results can be used for design of routing policies that guarantee stability of traffic queues under stochastic capacity fluctuations., National Science Foundation (U.S.) (CNS-1239054 CPS Frontiers), National Science Foundation (U.S.). Faculty Early Career Development Program Award (CNS-145312), United States. Air Force Research Laboratory. Lablet-Secure and Resilient Cyber-Physical Systems

Infrastructure systems are increasingly facing new security threats due to the vulnerabilities of cyber-physical components that support their operation. In this article, we investigate how the infrastructure operator (defender) should prioritize the investment in securing a set of facilities in order to reduce the impact of a strategic adversary (attacker) who can target a facility to increase the overall usage cost of the system. We adopt a game-theoretic approach to model the defender-attacker interaction and study two models: normal-form game -- where both players move simultaneously; and sequential game -- where attacker moves after observing the defender's strategy. For each model, we provide a complete characterization of how the set of facilities that are secured by the defender in equilibrium vary with the costs of attack and defense. Importantly, our analysis provides a sharp condition relating the cost parameters for which the defender has the first mover advantage. Specifically, we show that to fully deter the attacker from targeting any facility, the defender needs to proactively secure all "vulnerable facilities" at an appropriate level of effort. We illustrate the outcome of the attacker-defender interaction on a simple transportation network. We also suggest a dynamic learning setup to understand how this outcome can affect the ability of imperfectly informed users to make their decisions about using the system in the post-attack stage.

Mobile data traffic has been steadily rising in the past years. This has generated a significant interest in the deployment of incentive mechanisms to reduce peak-time congestion. Typically, the design of these mechanisms requires information about user demand and sensitivity to prices. Such information is naturally imperfect. In this paper, we propose a fixed-budget rebate mechanism that gives each user a reward proportional to his percentage contribution to the aggregate reduction in peak-time demand. For comparison, we also study a time-of-day pricing mechanism that gives each user a fixed reward per unit reduction of his peak-time demand. To evaluate the two mechanisms, we introduce a game-theoretic model that captures the public good nature of decongestion. For each mechanism, we demonstrate that the socially optimal level of decongestion is achievable for a specific choice of the mechanism's parameter. We then investigate how imperfect information about user demand affects the mechanisms' effectiveness. From our results, the fixed-budget rebate pricing is more robust when the users' sensitivity to congestion is “sufficiently” convex. This feature of the fixed-budget rebate mechanism is attractive for many situations of interest and is driven by its closed-loop property, i.e., the unit reward decreases as the peak-time demand decreases., National Science Foundation (U.S.) (Grant CNS-1239166), National Science Foundation (U.S.) (Grant CNS-0910711), National Science Foundation (U.S.) (CCF-0424422), United States. Air Force Office of Scientific Research (FA9550-06-1-0244)

This paper focuses on the security assessment of electricity distribution networks (DNs) with vulnerable distributed energy resource (DER) nodes. The adversary model is a simultaneous compromise of DER nodes by strategic manipulation of generation setpoints. The loss to the defender (DN operator) includes loss of voltage regulation and cost of induced load control under supply-demand mismatch caused by the attack. A three-stage defender-attacker-defender (DAD) game is formulated: in Stage 1, the defender chooses a security strategy to secure a subset of DER nodes; in Stage 2, the attacker compromises a set of vulnerable DERs and injects false generation setpoints; in Stage 3, the defender responds by controlling loads and non-compromised DERs. Solving this trilevel optimization problem is hard due to nonlinear power flows and mixed-integer decision variables. To address this challenge, the problem is approximated by a tractable formulation based on an ε-linear power-flow model. The set of critical DER nodes and the setpoint manipulations characterizing the optimal attack strategy are computed. An iterative greedy approach to computing attacker-defender strategies for the original nonlinear problem is proposed. These results provide guidelines for optimal security investment and defender response in preattack and postattack conditions, respectively., National Science Foundation (U.S.) (Award CNS-1238959), National Science Foundation (U.S.) (Award CNS-1238962), National Science Foundation (U.S.) (Award CNS- 1239054), National Science Foundation (U.S.) (Award CNS-1239166), Sandia National Laboratories, United States. Air Force. Research Laboratory (Award FA8750-14-2-0180)

Traffic signals were originally standalone hardware devices running on fixed schedules, but by now, they have evolved into complex networked systems. As a consequence, traffic signals have become susceptible to attacks through wireless interfaces or even remote attacks through the Internet. Indeed, recent studies have shown that many traffic lights deployed in practice have easily exploitable vulnerabilities, which allow an attacker to tamper with the configuration of the signal. Due to hardware-based failsafes, these vulnerabilities cannot be used to cause accidents. However, they may be used to cause disastrous traffic congestions. Building on Daganzo's well- known traffic model, we introduce an approach for evaluating vulnerabilities of transportation networks, identifying traffic signals that have the greatest impact on congestion and which, therefore, make natural targets for attacks. While we prove that finding an attack that maximally impacts congestion is NP-hard, we also exhibit a polynomial-time heuristic algorithm for computing approximately optimal attacks. We then use numerical experiments to show that our algorithm is extremely efficient in practice. Finally, we also evaluate our approach using the SUMO traffic simulator with a real-world transportation network, demonstrating vulnerabilities of this network. These simulation results extend the numerical experiments by showing that our algorithm is extremely efficient in a microsimulation model as well., National Science Foundation (U.S.) (Award CNS-1238959), National Science Foundation (U.S.) (Award CNS-1238962), National Science Foundation (U.S.) (Award CNS- 1239054), National Science Foundation (U.S.) (Award CNS-1239166), United States. Air Force. Research Laboratory (Award FA8750-14-2-0180), Sandia National Laboratories

This paper focuses on the optimal sensor placement problem for the identification of pipe failure locations in large-scale urban water systems. The problem involves selecting the minimum number of sensors such that every pipe failure can be uniquely localized. This problem can be viewed as a minimum test cover (MTC) problem, which is NP-hard. We consider two approaches to obtain approximate solutions to this problem. In the first approach, we transform the MTC problem to a minimum set cover (MSC) problem and use the greedy algorithm that exploits the submodularity property of the MSC problem to compute the solution to the MTC problem. In the second approach, we develop a new augmented greedy algorithm for solving the MTC problem. This approach does not require the transformation of the MTC to MSC. Our augmented greedy algorithm provides in a significant computational improvement while guaranteeing the same approximation ratio as the first approach. We propose several metrics to evaluate the performance of the sensor placement designs. Finally, we present detailed computational experiments for a number of real water distribution networks. Keywords: Fault identification; Minimum test cover; Water networks

We consider the initial-boundary value problem governed by systems of linear hyperbolic partial differential equations in the canonical diagonal form and study conditions for exponential stability when the system discontinuously switches between a finite set of modes. The switching system is fairly general in that the system matrix functions as well as the boundary conditions may switch in time. We show how the stability mechanism developed for classical solutions of hyperbolic initial boundary value problems can be generalized to the case in which weaker solutions become necessary due to arbitrary switching. We also provide an explicit dwell-time bound for guaranteeing exponential stability of the switching system when, for each mode, the system is exponentially stable. Our stability conditions only depend on the system parameters and boundary data. These conditions easily generalize to switching systems in the nondiagonal form under a simple commutativity assumption. We present tutorial examples to illustrate the instabilities that can result from switching.

The Internet is plagued with congestion problems of growing severity which are worst at peak periods. In this paper, we compare two schemes that incentivize users to shift part of their usage from the peak-time to the off-peak time. The traditional time-of-day pricing scheme gives a fixed reward per unit of shifted usage. Conversely, the raffle-based scheme provides a random reward distributed in proportion of each user's fraction of the total shifted usage. Using a game-theoretic model, we show that both schemes can achieve an optimal level of decongestion at a unique Nash equilibrium. We provide a comparison of the schemes' sensitivity to uncertainty of the users' utilities., National Science Foundation (U.S.) (Grant CNS-0910711)