85 results on '"Amin, Saurabh"'
Search Results
2. Modeling and Parameter Estimation of Hurricane Wind Fields with Asymmetry
- Author
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Chang, Derek, Amin, Saurabh, and Emanuel, Kerry
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- 2020
3. Exponential convergence rates for stochastically ordered Markov processes under perturbation
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Gaudio, Julia, Amin, Saurabh, and Jaillet, Patrick
- Published
- 2019
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4. Securing Infrastructure Facilities: When Does Proactive Defense Help?
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Wu, Manxi and Amin, Saurabh
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- 2019
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5. Learning an Unknown Network State in Routing Games
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Wu, Manxi and Amin, Saurabh
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- 2019
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6. Preface to the Focused Issue on Dynamic Games in Cyber Security
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Amin, Saurabh and Johansson, Karl Henrik
- Published
- 2019
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7. Bottleneck model with heterogeneous information
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Khan, Zaid and Amin, Saurabh
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- 2018
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8. Robust sensor placement for pipeline monitoring: Mixed integer and greedy optimization
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Sela, Lina and Amin, Saurabh
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- 2018
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9. Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference
- Author
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Gupta, Samarth and Amin, Saurabh
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Artificial Intelligence (cs.AI) ,Optimization and Control (math.OC) ,Computer Science - Artificial Intelligence ,FOS: Mathematics ,FOS: Electrical engineering, electronic engineering, information engineering ,Systems and Control (eess.SY) ,Mathematics - Optimization and Control ,Electrical Engineering and Systems Science - Systems and Control ,Machine Learning (cs.LG) - Abstract
We focus on the problem of uncertainty informed allocation of medical resources (vaccines) to heterogeneous populations for managing epidemic spread. We tackle two related questions: (1) For a compartmental ordinary differential equation (ODE) model of epidemic spread, how can we estimate and integrate parameter uncertainty into resource allocation decisions? (2) How can we computationally handle both nonlinear ODE constraints and parameter uncertainties for a generic stochastic optimization problem for resource allocation? To the best of our knowledge current literature does not fully resolve these questions. Here, we develop a data-driven approach to represent parameter uncertainty accurately and tractably in a novel stochastic optimization problem formulation. We first generate a tractable scenario set by estimating the distribution on ODE model parameters using Bayesian inference with Gaussian processes. Next, we develop a parallelized solution algorithm that accounts for scenario-dependent nonlinear ODE constraints. Our scenario-set generation procedure and solution approach are flexible in that they can handle any compartmental epidemiological ODE model. Our computational experiments on two different non-linear ODE models (SEIR and SEPIHR) indicate that accounting for uncertainty in key epidemiological parameters can improve the efficacy of time-critical allocation decisions by 4-8%. This improvement can be attributed to data-driven and optimal (strategic) nature of vaccine allocations, especially in the early stages of the epidemic when the allocation strategy can crucially impact the long-term trajectory of the disease.
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- 2023
10. Control for Societal-scale Challenges: Road Map 2030
- Author
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Alleyne, Andrew, Allgöwer, Frank, Ames, Aaron D., Amin, Saurabh, Anderson, James, Annaswamy, Anuradha M., Antsaklis, Panos J., Bagheri, Neda, Balakrishnan, Hamsa, Bamieh, Bassam, Baras, John, Bauer, Margret, Bayen, Alexandre, Bogdan, Paul, Brunton, Steven L., Bullo, Francesco, Burdet, Etienne, Burdick, Joel, Burlion, Laurent, Canudas de Wit, Carlos, Cao, Ming, Cassandras, Christos G., Chakrabortty, Aranya, Como, Giacomo, Csete, Marie, Dabbene, Fabrizio, Dahleh, Munther, Das, Amritam, Dassau, Eyal, De Persis, Claudio, di Bernardo, Mario, Di Cairano, Stefano, Dimarogonas, Dimos, Dörfler, Florian, Doyle, John C., Doyle III, Francis J., Dragan, Anca, Egerstedt, Magnus, Eker, Johan, Fay, Sarah, Filev, Dimitar, Fontan, Angela, Franco, Elisa, Fujita, Masayuki, Garcia-Sanz, Mario, Gayme, Dennice, Heemels, W.P.M.H., Hespanha, João P., Hirche, Sandra, Hosoi, Anette, How, Jonathan P., Hug, Gabriela, Ilić, Marija, Ishii, Hideaki, Jadbabaie, Ali, Jafarian, Matin, Qing-Shan Jia, Samuel, Johansen, Tor, Johansson, Karl H., Jones, Dalton, Khammash, Mustafa, Khargonekar, Pramod, Kochenderfer, Mykel J., Krause, Andreas, Kuh, Anthony, Kulić, Dana, Lamnabhi-Lagarrigue, Françoise, Leonard, Naomi E., Leve, Frederick, Li, Na, Low, Steven, Lygeros, John, Mareels, Iven, Martinez, Sonia, Matni, Nikolai, Menara, Tommaso, Mombaur, Katja, Moore, Kevin, Murray, Richard, Namerikawa, Toru, Nedich, Angelia, Neema, Sandeep, Netto, Mariana, O’Leary, Timothy, O’Malley, Marcia K., Pao, Lucy Y., Papachristodoulou, Antonis, Pappas, George J., Paré, Philip E., Parisini, Thomas, Pasqualetti, Fabio, Pavone, Marco, Rajhans, Akshay, Ranade, Gireeja, Rantzer, Anders, Ratliff, Lillian, Rossiter, J. Anthony, Sadigh, Dorsa, Samad, Tariq, Sandberg, Henrik, Sarma, Sri, Schenato, Luca, Scherpen, Jacquelien, Schoellig, Angela, Sepulchre, Rodolphe, Shamma, Jeff, Shorten, Robert, Sinopoli, Bruno, Sreenath, Koushil, Stoustrup, Jakob, Sun, Jing, Tabuada, Paulo, Tegling, Emma, Tilbury, Dawn, Tomlin, Claire J., Tumova, Jana, Wise, Kevin, Work, Dan, Zafar, Junaid, Zeilinger, Melanie, Control Systems, Spatial-Temporal Systems for Control, EAISI High Tech Systems, EAISI Health, EAISI Foundational, EAISI Mobility, Control Systems Technology, and Group Heemels
- Abstract
The world faces some of its greatest challenges of modern time and how we address them will have a dramatic impact on the life for generations to come. Simultaneously, control systems, consisting of information enriched by various degrees of analytics followed by decision-making, are pervading a variety of sectors, not only in engineering but beyond, into financial services, socio-economic analysis, entertainment and sports, and political and social sciences. Increased levels of automation are sought after in various sectors and being introduced into new domains. All of these advances and transformations urge a shift in the conversation toward how control systems can meet grand societal- scale challenges. The document seeks to chart a roadmap for the evolution of control systems, identifying several areas where our discipline can have an impact over the next decade.
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- 2023
11. Sensor placement for fault location identification in water networks: A minimum test cover approach
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Sela Perelman, Lina, Abbas, Waseem, Koutsoukos, Xenofon, and Amin, Saurabh
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- 2016
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12. Effective Dimension in Bandit Problems under Censorship
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Guinet, Gauthier, Amin, Saurabh, and Jaillet, Patrick
- Subjects
FOS: Computer and information sciences ,Computer Science - Machine Learning ,Statistics - Machine Learning ,Machine Learning (stat.ML) ,Machine Learning (cs.LG) - Abstract
In this paper, we study both multi-armed and contextual bandit problems in censored environments. Our goal is to estimate the performance loss due to censorship in the context of classical algorithms designed for uncensored environments. Our main contributions include the introduction of a broad class of censorship models and their analysis in terms of the effective dimension of the problem -- a natural measure of its underlying statistical complexity and main driver of the regret bound. In particular, the effective dimension allows us to maintain the structure of the original problem at first order, while embedding it in a bigger space, and thus naturally leads to results analogous to uncensored settings. Our analysis involves a continuous generalization of the Elliptical Potential Inequality, which we believe is of independent interest. We also discover an interesting property of decision-making under censorship: a transient phase during which initial misspecification of censorship is self-corrected at an extra cost, followed by a stationary phase that reflects the inherent slowdown of learning governed by the effective dimension. Our results are useful for applications of sequential decision-making models where the feedback received depends on strategic uncertainty (e.g., agents' willingness to follow a recommendation) and/or random uncertainty (e.g., loss or delay in arrival of information)., 45 pages, 5 figures, NeurIPS 2022
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- 2023
13. Electric-Gas Infrastructure Planning for Deep Decarbonization of Energy Systems
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Khorramfar, Rahman, Mallapragada, Dharik, and Amin, Saurabh
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FOS: Electrical engineering, electronic engineering, information engineering ,Systems and Control (eess.SY) ,Electrical Engineering and Systems Science - Systems and Control - Abstract
The transition to a deeply decarbonized energy system requires coordinated planning of infrastructure investments and operations serving multiple end-uses while considering technology and policy-enabled interactions across sectors. Electricity and natural gas (NG), which are vital vectors of today's energy system, are likely to be coupled in different ways in the future, resulting from increasing electrification, adoption of variable renewable energy (VRE) generation in the power sector and policy factors such as cross-sectoral emissions trading. This paper develops a least-cost investment and operations model for joint planning of electricity and NG infrastructures that considers a wide range of available and emerging technology options across the two vectors, including carbon capture and storage (CCS) equipped power generation, low-carbon drop-in fuels (LCDF) as well as long-duration energy storage (LDES). The model incorporates the main operational constraints of both systems and allows each system to operate under different temporal resolutions consistent with their typical scheduling timescales. We apply the modeling framework to evaluate power-NG system outcomes for the U.S. New England region under different technology, decarbonization goals, and demand scenarios. Under a global emissions constraint, ranging between 80-95\% emissions reduction compared to 1990 levels, the least-cost solution disproportionately relies on using the available emissions budget to serve non-power NG demand and results in the power sector using only 15-43\% of the emissions budget.
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- 2022
14. Probabilistic Modeling of Hurricane Wind-Induced Damage in Infrastructure Systems
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Chang, Derek, Emanuel, Kerry, and Amin, Saurabh
- Subjects
FOS: Computer and information sciences ,Statistics - Other Statistics ,Other Statistics (stat.OT) ,Applications (stat.AP) ,Statistics - Applications ,Physics::Atmospheric and Oceanic Physics - Abstract
This paper presents a modeling approach for probabilistic estimation of hurricane wind-induced damage to infrastructural assets. In our approach, we employ a Nonhomogeneous Poisson Process (NHPP) model for estimating spatially-varying probability distributions of damage as a function of hurricane wind field velocities. Specifically, we consider a physically-based, quadratic NHPP model for failures of overhead assets in electricity distribution systems. The wind field velocities are provided by Forecasts of Hurricanes using Large-Ensemble Outputs (FHLO), a framework for generating probabilistic hurricane forecasts. We use FHLO in conjunction with the NHPP model, such that the hurricane forecast uncertainties represented by FHLO are accounted for in estimating the probability distributions of damage. Furthermore, we evaluate the spatial variability and extent of hurricane damage under key wind field parameters (intensity, size, and asymmetries). By applying our approach to prediction of power outages (loss-of-service) in northwestern Florida due to Hurricane Michael (2018), we demonstrate a statistically significant relationship between outage rate and failure rate. Finally, we formulate parametric models that relate total damage and financial losses to the hurricane parameters of intensity and size. Overall, this paper's findings suggest that our approach is well-suited to jointly account for spatial variability and forecast uncertainty in the damage estimates, and is readily applicable to prediction of system loss-of-service due to the damage., Submitted to Risk Analysis, 21 pages (main text), 17 figures, includes appendix
- Published
- 2021
15. Understanding the physical and economic consequences of attacks on control systems
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Huang, Yu-Lun, Cárdenas, Alvaro A., Amin, Saurabh, Lin, Zong-Syun, Tsai, Hsin-Yi, and Sastry, Shankar
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- 2009
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16. Efficient Carpooling and Toll Pricing for Autonomous Transportation
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Amin, Saurabh, Jaillet, Patrick, and Wu, Manxi
- Subjects
FOS: Computer and information sciences ,TheoryofComputation_MISCELLANEOUS ,Computer Science - Computer Science and Game Theory ,ComputerApplications_COMPUTERSINOTHERSYSTEMS ,Computer Science and Game Theory (cs.GT) - Abstract
In this paper, we address the existence and computation of competitive equilibrium in the transportation market for autonomous carpooling first proposed by [Ostrovsky and Schwarz, 2019]. At equilibrium, the market organizes carpooled trips over a transportation network in a socially optimal manner and sets the corresponding payments for individual riders and toll prices on edges. The market outcome ensures individual rationality, stability of carpooled trips, budget balance, and market clearing properties under heterogeneous rider preferences. We show that the question of market's existence can be resolved by proving the existence of an integer optimal solution of a linear programming problem. We characterize conditions on the network topology and riders' disutility for carpooling under which a market equilibrium can be computed in polynomial time. This characterization relies on ideas from the theory of combinatorial auctions and minimum cost network flow problem. Finally, we characterize a market equilibrium that achieves strategyproofness and maximizes welfare of individual riders.
- Published
- 2021
17. On Cyber Security for Networked Control Systems
- Author
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Amin, Saurabh
- Subjects
Civil engineering ,System science ,Attack diagnosis and response ,Network security ,Secure Control Systems - Abstract
The instrumentation of infrastructure systems by embedded sensors, computation, and communication networks has enabled significant advances in their management. Examples include monitoring of structural health, traffic congestion, environmental hazards, and energy usage. The use of homogeneous (especially, commercially available off-the-shelf) information technology (IT) solutions makes infrastructure systems subject to correlated hardware malfunctions and software bugs. Over the past decade, many concerns have been raised about the vulnerabilities of infrastructure systems to both random failures and security attacks. Cyber-security of Supervisory Control and Data Acquisition (SCADA) systems is especially important, because these systems are employed for sensing and control of large physical infrastructures. So far, the existing research in robust and fault-tolerant control does not account for cyber attacks on networked control system (NCS) components. Also, the existing research in computer security neither considers the attacks targeting NCS components nor accounts for their interactions with the physical system. The goal of this thesis is to bridge this gap by focusing on (1) security threat assessment, (2) model-based attack diagnosis, and (3) resilient control design. First, cyber-security assessment for SCADA systems is performed based on well-defined attacker and defender objectives. The mathematical model of SCADA systems considered in this work has two control levels: regulatory control using distributed proportional-integral (PI) controllers, and supervisory fault diagnosis based on approximate dynamical system models. The performance of a PI control based regulatory scheme and a model-based supervisory diagnostic scheme is studied under a class of deception attacks. In order to test the system resilience, a class of stealthy attacks which can evade detection by SCADA systems is presented. Second, design of attack diagnosis schemes that incorporate the knowledge of physical dynamics of the system is presented. For SCADA systems used to manage water canal networks, an observer-based attack diagnostic scheme, in which each observer estimates the state of a reduced-order flow model, is presented. The observer parameters are computed using a convex optimization method, and the performance of this scheme is tested on a number of attack scenarios. An application of the theoretical results is illustrated by a field operational test performed on the SCADA system of the Gignac water canal system, located in Montpellier, France. A successful experimental cyber-attack on the sensors and actuators of this canal network revealed new vulnerabilities of the current SCADA system implementation. Another illustration includes security analysis of two benchmark scenarios: the Tennessee Eastman process control system (TE-PCS) and a power system state estimator (PSSE). In both these cases, model-based statistical detection schemes are used to study stealthy deception attacks. For the case of TE-PCS, design of practically implementable attack-detection and response mechanisms to maintain operational safety is presented. For the case of PSSE, it is assumed that the attacker only has a partial knowledge of the actual system model. For a set of attacker objectives, the trade-off between the attacker knowledge and possible impact of a successful attack on the performance of false data detection schemes is studied. Third, the stability of linear hyperbolic systems of PDEs when the boundary control actions and the system parameters switch discontinuously between a finite set of modes is studied. Switched PDE models can describe a class of fault and attack scenarios resulting from intermittent withdrawals through offtake nodes and compromise of sensor-control data. Motivated by such scenarios, a new condition for stability of linear hyperbolic systems of PDEs under arbitrary switching of boundary control actions and system parameters is derived. A class of switching attack strategies is presented, which violate the stability condition and result in unstable flow dynamics.Fourth, the problem of controlling stochastic linear systems for networked control settings is considered when the sensor-control data is prone to packet loss and jamming. For a class of packet drop models, feedback control policies which minimize a given objective function subject to safety constraints are synthesized. For marginally stable systems, under mild hypotheses on the noise introduced by the control channel and large enough control authority, the synthesis of a control policy that render the state of the closed-loop system mean-square bounded is presented.Finally, a class of games involving discrete interdependent risks is considered when each player is a NCS, and their security is interdependent due to the exposure to network induced risks. The problem of security decisions of individual players is formulated as a two-stage non-cooperative game defined as follows: in the first stage, the players decide whether to invest in security or not; and in the second stage, they apply control inputs to minimize the average operational costs. The characterization of the equilibria of the game is presented, which includes the determination of the individually optimal security levels. The presence of interdependent security causes a negative externality, and the individual players tend to under invest in security relative to the social optimum. From these results, for a wide parameter range, public policy incentivising higher security investments is desirable.
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- 2011
18. Network Inspection for Detecting Strategic Attacks.
- Author
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Dahan, Mathieu, Sela, Lina, and Amin, Saurabh
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BILEVEL programming ,INFRASTRUCTURE (Economics) ,NASH equilibrium ,COMPUTER network security ,DETECTORS - Abstract
Ensuring the security of critical infrastructures is crucial for the society's welfare and prosperity. However, these infrastructure networks are inherently vulnerable to both intentional and unintentional threats. In "Network Inspection for Detecting Strategic Attacks," Dahan, Sela, and Amin study a strategic network inspection problem, formulated as a large-scale bilevel optimization problem, in which a utility seeks to determine an inspection strategy with minimum number of smart detectors that ensures a desirable expected detection performance under worst-case attacks. The authors derive structural properties of optimal solutions and show that the problem can be solved using Nash equilibria of a large-scale zero-sum game. Their analysis leads to a computationally tractable and operationally feasible solution approach with theoretical guarantees based on combinatorial objects that capture the nature of equilibrium inspection and attack strategies. Their computational study indicates that utilities can achieve a high level of protection in large-scale networks by strategically positioning a small number of detectors. This article studies a problem of strategic network inspection, in which a defender (agency) is tasked with detecting the presence of multiple attacks in the network. An inspection strategy entails monitoring the network components, possibly in a randomized manner, using a given number of detectors. We formulate the network inspection problem (P) as a large-scale bilevel optimization problem, in which the defender seeks to determine an inspection strategy with minimum number of detectors that ensures a target expected detection rate under worst-case attacks. We show that optimal solutions of (P) can be obtained from the equilibria of a large-scale zero-sum game. Our equilibrium analysis involves both game-theoretic and combinatorial arguments and leads to a computationally tractable approach to solve (P). First, we construct an approximate solution by using solutions of minimum set cover (MSC) and maximum set packing (MSP) problems and evaluate its detection performance. In fact, this construction generalizes some of the known results in network security games. Second, we leverage properties of the optimal detection rate to iteratively refine our MSC/MSP-based solution through a column generation procedure. Computational results on benchmark water networks demonstrate the scalability, performance, and operational feasibility of our approach. The results indicate that utilities can achieve a high level of protection in large-scale networks by strategically positioning a small number of detectors. [ABSTRACT FROM AUTHOR]
- Published
- 2022
- Full Text
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19. Probability Distributions on Partially Ordered Sets and Network Interdiction Games.
- Author
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Dahan, Mathieu, Amin, Saurabh, and Jaillet, Patrick
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PARTIALLY ordered sets ,DISTRIBUTION (Probability theory) ,STRATEGY games ,POLYNOMIAL time algorithms ,NASH equilibrium - Abstract
This article poses the following problem: Does there exist a probability distribution over subsets of a finite partially ordered set (poset), such that a set of constraints involving marginal probabilities of the poset's elements and maximal chains is satisfied? We present a combinatorial algorithm to positively resolve this question. The algorithm can be implemented in polynomial time in the special case where maximal chain probabilities are affine functions of their elements. This existence problem is relevant for the equilibrium characterization of a generic strategic interdiction game on a capacitated flow network. The game involves a routing entity that sends its flow through the network while facing path transportation costs and an interdictor who simultaneously interdicts one or more edges while facing edge interdiction costs. Using our existence result on posets and strict complementary slackness in linear programming, we show that the Nash equilibria of this game can be fully described using primal and dual solutions of a minimum-cost circulation problem. Our analysis provides a new characterization of the critical components in the interdiction game. It also leads to a polynomial-time approach for equilibrium computation. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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20. Analyzing a Tandem Fluid Queueing Model with Stochastic Capacity and Spillback
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Jin, Li and Amin, Saurabh
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Optimization and Control (math.OC) ,Probability (math.PR) ,FOS: Mathematics ,Mathematics - Optimization and Control ,Mathematics - Probability - Abstract
The tandem fluid queueing model is a useful tool for performance analysis and control design for a variety of transportation systems. In this article, we study the joint impact of stochastic capacity and spillback on the long-time properties of this model. Our analysis focuses on the system of two fluid queueing links in series. The upstream link has a constant capacity (saturation rate) and an infinite buffer size. The downstream link has a stochastic capacity and a finite buffer size. Queue spillback occurs when the the downstream link is full. We derive a necessary condition and a sufficient condition for the total queue length to be bounded on average. The necessary (resp. sufficient) condition leads to an upper (resp. lower) bound for the throughput of the two-link system. Using our results, we analyze the sensitivity of throughput of the two-link system with respect to the frequency and intensity of capacity disruptions, and to the buffer size. In addition, we discuss how our analysis can be extended to feedback-controlled systems and to networks consisting of merges and splits.
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- 2019
21. Throughput-Improving Control of Highways Facing Stochastic Perturbations
- Author
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Jin, Li, Kurzhanskiy, Alexander A., and Amin, Saurabh
- Subjects
FOS: Electrical engineering, electronic engineering, information engineering ,Computer Science::Networking and Internet Architecture ,Systems and Control (eess.SY) ,Electrical Engineering and Systems Science - Systems and Control - Abstract
In this paper, we study the problem of traffic management in highways facing stochastic perturbations. To model the macroscopic traffic flow under perturbations, we use cell-transmission model with Markovian capacities. The decision variables are: (i) the admissible flow through each on-ramp, and (ii) whether individual on-ramps are metered to prioritize the mainline traffic or not. The objective is to maximize the throughput while ensuring that on-ramp queues remain bounded on average. We develop a computational approach to solving this stability-constrained, throughput-maximization problem. We establish a mixed-integer linear program for throughput maximization and construct an algorithm that gives the optimal solution in particular settings. We illustrate the performance benefits of the proposed approach through a computational study on a segment of Interstate 210 in California, USA., 27 pages
- Published
- 2018
22. Analysis and Design of Vehicle Platooning Operations on Mixed-Traffic Highways.
- Author
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Jin, Li, Cicic, Mladen, Johansson, Karl H., and Amin, Saurabh
- Subjects
HIGHWAY capacity ,TRAFFIC flow ,AUTONOMOUS vehicles ,TRAFFIC engineering ,MARKOV processes - Abstract
Platooning of connected and autonomous vehicles (CAVs) has a significant potential for throughput improvement. However, the interaction between CAVs and non-CAVs may limit the practically attainable improvement due to platooning. To better understand and address this limitation, we introduce a new fluid model of mixed-autonomy traffic flow and use this model to analyze and design platoon coordination strategies. We propose a tandem-link fluid model that considers randomly arriving platoons sharing highway capacity with non-CAVs. We derive verifiable conditions for stability of the fluid model by analyzing an underlying M/D/1 queuing process and establishing a Foster–Lyapunov drift condition for the fluid model. These stability conditions enable a quantitative analysis of highway throughput under various scenarios. The model is useful for designing platoon coordination strategies that maximize throughput and minimize delay. Such coordination strategies are provably optimal in the fluid model and are practically relevant. We also validate our results using standard macroscopic (cell transmission model) and microscopic (simulation for urban mobility) simulation models. [ABSTRACT FROM AUTHOR]
- Published
- 2021
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23. Value of Information in Bayesian Routing Games.
- Author
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Wu, Manxi, Amin, Saurabh, and Ozdaglar, Asuman E.
- Subjects
ROUTE choice ,UNCERTAIN systems ,INFORMATION storage & retrieval systems ,MARKET share ,GAMES - Abstract
Over the years, travelers are increasingly relying on navigation services to inform their route choices and save time. How much value—in terms of saved time—can these services indeed provide to their users? The answer is not as simple as one may think because the value of traffic information not only depends on its accuracy, but also on how widely it is shared among travelers. For instance, information on an incident has the highest value for a traveler when the traveler is the only person who knows about it and takes a detour. However, this information becomes less valuable when it is shared with more travelers, who may take the same detour and cause congestion. Our work uses a game-theoretic approach to analyze how the relative value of information provided by different navigation services changes with their market share and how travelers may choose from different available services. We study a routing game in an environment with multiple heterogeneous information systems and an uncertain state that affects edge costs of a congested network. Each information system sends a noisy signal about the state to its subscribed traveler population. Travelers make route choices based on their private beliefs about the state and other populations' signals. The question then arises, "How does the presence of asymmetric and incomplete information affect the travelers' equilibrium route choices and costs?" We develop a systematic approach to characterize the equilibrium structure and determine the effect of population sizes on the relative value of information (i.e., difference in expected traveler costs) between any two populations. This effect can be evaluated using a population-specific size threshold. One population enjoys a strictly positive value of information in comparison with the other if and only if its size is below the corresponding threshold. We also consider the situation when travelers may choose an information system based on its value and characterize the set of equilibrium adoption rates delineating the sizes of subscribed traveler populations. The resulting routing strategies are such that all travelers face an identical expected cost and no traveler has the incentive to change subscriptions. [ABSTRACT FROM AUTHOR]
- Published
- 2021
- Full Text
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24. Demand response scheme based on lottery-like rebates
- Author
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Schwartz, Galina A., Tembine, Hamidou, Amin, Saurabh, Sastry, S. Shankar, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, and Amin, Saurabh
- Abstract
In this paper, we develop a novel mechanism for reducing volatility of residential demand for electricity. We construct a reward-based (rebate) mechanism that provides consumers with incentives to shift their demand to off-peak time. In contrast to most other mechanisms proposed in the literature, the key feature of our mechanism is its modest requirements on user preferences, i.e., it does not require exact knowledge of user responsiveness to rewards for shifting their demand from the peak to the off-peak time. Specifically, our mechanism utilizes a probabilistic reward structure for users who shift their demand to the off-peak time, and is robust to incomplete information about user demand and/or risk preferences. We approach the problem from the public good perspective, and demonstrate that the mechanism can be implemented via lottery-like schemes. Our mechanism permits to reduce the distribution losses, and thus improve efficiency of electricity distribution. Finally, the mechanism can be readily incorporated into the emerging demand response schemes (e.g., the time-of-day pricing, and critical peak pricing schemes), and has security and privacy-preserving properties., National Science Foundation (U.S.) (Grant CNS-1239166), National Science Foundation (U.S.) (Grant CNS-0910711), National Science Foundation (U.S.) (CCF-0424422)
- Published
- 2014
25. Incentive mechanisms for internet congestion management: Fixed-budget rebate versus time-of-day pricing
- Author
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Loiseau, Patrick, Schwartz, Galina, Musacchio, John, Amin, Saurabh, Sastry, Shankar S, Loiseau, Patrick, Schwartz, Galina, Musacchio, John, Amin, Saurabh, and Sastry, Shankar S
- Subjects
congestion pricing ,lottery-based incentive mechanisms ,public good provisioning ,probabilistic pricing - Published
- 2014
26. Effects of Information Heterogeneity in Bayesian Routing Games
- Author
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Liu, Jeffrey, Amin, Saurabh, and Schwartz, Galina
- Subjects
FOS: Computer and information sciences ,Computer Science - Computer Science and Game Theory ,ComputingMilieux_PERSONALCOMPUTING ,TheoryofComputation_GENERAL ,Computer Science and Game Theory (cs.GT) - Abstract
This article studies the value of information in route choice decisions when a fraction of players have access to high accuracy information about traffic incidents relative to others. To model such environments, we introduce a Bayesian congestion game, in which players have private information about incidents, and each player chooses her route on a network of parallel links. The links are prone to incidents that occur with an ex-ante known probability. The demand is comprised of two player populations: one with access to high accuracy incident information and another with low accuracy information, i.e. the populations differ only by their access to information. The common knowledge includes: (i) the demand and route cost functions, (ii) the fraction of highly-informed players, (iii) the incident probability, and (iv) the marginal type distributions induced by the information structure of the game. We present a full characterization of the Bayesian Wardrop Equilibrium of this game under the assumption that low information players receive no additional information beyond common knowledge. We also compute the cost to individual players and the social cost as a function of the fraction of highly-informed players when they receive perfectly accurate information. Our first result suggests that below a certain threshold of highly-informed players, both populations experience a reduction in individual cost, with the highly-informed players receiving a greater reduction. However, above this threshold, both populations realize the same equilibrium cost. Secondly, there exists another (lower or equal) threshold above which a further increase in the fraction of highly-informed players does not reduce the expected social costs. Thus, once a sufficiently large number of players are highly informed, wider distribution of more accurate information is ineffective at best, and otherwise socially harmful.
- Published
- 2016
27. Game-Theoretic Models of Electricity Theft Detection in Smart Utility Networks: Providing New Capabilities with Advanced Metering Infrastructure
- Author
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Amin, Saurabh, Schwartz, Galina A., Cardenas, Alvaro A., Sastry, S. Shankar, Amin, Saurabhci, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, and Amin, Saurabhci
- Abstract
The smart grid refers to the modernization of the power grid infrastructure with new technologies, enabling a more intelligently networked automated system with the goal of improving efficiency, reliability, and security, while providing more transparency and choices to electricity customers. A key technology being widely deployed on the consumption side of the grid is advanced metering infrastructure (AMI)., National Science Foundation (U.S.) (Award CNS-1239054)
- Published
- 2015
28. Leveraging sUAS for Infrastructure Network Exploration and Failure Isolation.
- Author
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Lee, Andrew C., Dahan, Mathieu, Weinert, Andrew J., and Amin, Saurabh
- Abstract
Large-scale infrastructures are prone to simultaneous faults when struck by a natural or man-made event. The current operating procedure followed by many utilities needs improvement, both in terms of monitoring performance and time to repair. Motivated by the recent technological progress on small Unmanned Aerial Systems (sUAS), we propose a practical framework to integrate the monitoring capabilities of sUAS into standard utility repair operations. A key aspect of our framework is the use of monitoring locations for sUAS-based inspection of failures within a certain spatial zone (called a localization set). This set is defined based on the alerts from fixed sensors or customer calls. The positioning of monitoring locations is subject to several factors such as sUAS platform, network topology, and airspace restrictions. We formulate the problem of minimizing the maximum time to respond to all failures by routing repair vehicles to various localization sets and exploring these sets using sUAS. The formulation admits a natural decomposition into two sub-problems: the sUAS Network Exploration Problem (SNEP); and the Repair Vehicle Routing Problem (RVRP). Standard solvers can be used to solve the RVRP in a scalable manner; however, solving the SNEP for each localization set can be computationally challenging. To address this limitation, we propose a set cover based heuristic to approximately solve the SNEP. We implement the overall framework on a benchmark network. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
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29. Congestion pricing using a raffle-based scheme
- Author
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Amin, Saurabh, Loiseau, Patrick, Schwartz, Galina, Musacchio, John, Sastry, S. Shankar, Massachusetts Institute of Technology. Department of Civil and Environmental Engineering, and Amin, Saurabh
- Abstract
We propose a raffle-based scheme for the decongestion of a shared resource. Our scheme builds on ideas from the economic literature on incentivizing contributions to a public good. We formulate a game-theoretic model for the decongestion problem in a setup with a finite number of users, as well as in a setup with an infinite number of non-atomic users. We analyze both setups, and show that the former converges toward the latter when the number of users becomes large. We compare our results to existing results for the public good provision problem. Overall, our results establish that raffle-based schemes are useful in addressing congestion problems., National Science Foundation (U.S.) (Grant CNS-0910711), National Science Foundation (U.S.) (Grant CCF-0424422), United States. Air Force Office of Scientific Research (FA9550-06-1-0244)
- Published
- 2011
30. Security of interdependent and identical networked control systems
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Amin, Saurabh, Schwartz, Galina A., and Shankar Sastry, S.
- Published
- 2013
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31. Analysis of a Stochastic Switching Model of Freeway Traffic Incidents.
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Jin, Li and Amin, Saurabh
- Subjects
- *
STOCHASTIC analysis , *MARKOV processes , *LYAPUNOV functions , *TRAFFIC flow , *INVARIANTS (Mathematics) - Abstract
This paper introduces a model for freeway traffic dynamics under stochastic capacity-reducing incidents, and provides insights for freeway incident management by analyzing long-time (stability) properties of the proposed model. Incidents on a multicell freeway are modeled by reduction in capacity at the affected freeway sections, which occur and clear according to a Markov chain. We develop conditions under which the traffic queue induced by stochastic incidents is bounded. A necessary condition is that the demand must not exceed the time-average capacity adjusted for spillback. A sufficient condition, in the form of a set of bilinear inequalities, is also established by constructing a Lyapunov function and applying the classical Foster–Lyapunov drift condition. Both conditions can be easily verified for realistic instances of the stochastic incident model. Our analysis relies on the construction of a globally attracting invariant set, and exploits the properties of the traffic flow dynamics. We use our results to analyze the impact of stochastic capacity fluctuation (frequency, intensity, and spatial correlation) on the throughput of a freeway segment. [ABSTRACT FROM AUTHOR]
- Published
- 2019
- Full Text
- View/download PDF
32. Electricity demand response via randomized rewards
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Schwartz, G., Tembine, Hamidou, Amin, Saurabh, Sastry, Skankar, Supélec Sciences des Systèmes (E3S), Ecole Supérieure d'Electricité - SUPELEC (FRANCE), Massachusetts Institute of Technology (MIT), and Magnet, Catherine
- Subjects
[INFO.INFO-NI]Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,[INFO.INFO-NI] Computer Science [cs]/Networking and Internet Architecture [cs.NI] ,ComputingMilieux_MISCELLANEOUS - Abstract
International audience
- Published
- 2012
33. Stability of Fluid Queueing Systems With Parallel Servers and Stochastic Capacities.
- Author
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Jin, Li and Amin, Saurabh
- Subjects
- *
DETERMINISTIC processes , *MARKOV processes , *TRAFFIC engineering , *SWITCHING systems (Telecommunication) , *STOCHASTIC models - Abstract
This note 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 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. [ABSTRACT FROM AUTHOR]
- Published
- 2018
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34. Vulnerability of Transportation Networks to Traffic-Signal Tampering.
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Laszka, Aron, Potteiger, Bradley, Vorobeychik, Yevgeniy, Amin, Saurabh, and Koutsoukos, Xenofon
- Published
- 2016
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35. An integrated performance model learning and planning approach for optimal infrastructure facility maintenance under partial observability
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Amin, Saurabh
- Subjects
Reinforcement learning ,Sequence classification ,Infrastructure inspection and maintenance - Abstract
Infrastructure inspection and maintenance decision making is a stochastic and partially observable problem. This thesis presents a learning and decision-making approach for developing optimal joint inspection and maintenance policies for civil infrastructure facilities under performance model uncertainty and partially observable condition state. Joint inspection and maintenance decision making refers to the cost-effective policies that capture the interactions between the inspections, and the maintenance and rehabilitation decisions. Performance model uncertainty is the uncertainty about the actual facility deterioration model. Partially observation condition states imply that the true or latent condition of the facility is not captured by the measurement technology used for inspecting the facility due to measurement errors. The proposed approach models single facility inspection and maintenance decision-making problem as a partially observable Markov decision process. The data collected during the agency-facility interaction is used to learn the maximum likelihood estimate of performance model using the Baum-Welch algorithm. Both offline and online versions of the learning algorithm are presented. The probing-optimizing dichotomy, also known as exploration-exploitation dilemma, in choosing between the best strategy based on the past knowledge of deterioration and the strategy that provides information leading to better performance model is also illustrated. Once a set of performance models is learned, each of the models is quantitatively compared with the corresponding true model using an information theoretic measure known as asymmetric Kullback-Leibler divergence to determine its goodness. It is also shown that given a set of new observation sequences gathered after the model learning, each of the sequences or the entire set of sequences can be assigned the model that gives the least log-likelihood on that sequence or the set. This can help in classifying the facility deterioration into different deterioration modes like slow, medium, or fast. Finally, an exact algorithm for arriving at optimal inspection and maintenance decision-making policies is described. It is argued that the proposed approach scores over other recently developed adaptive control approaches in that it directly learns the best model from historical data instead of maintaining and updating beliefs over a set of pre-selected models. Furthermore, since the proposed learning approach is model-based, it makes more efficient use of costly data as compared to the model-less learning approaches. Keywords: Infrastructure Inspection and Maintenance, Baum-Welch Algorithm, Kullback-Liebler Divergence, Sequence Classification, Partially Observable Markov Decision Process, Reinforcement Learning.
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- 2004
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36. A distributed strategy for electricity distribution network control in the face of DER compromises.
- Author
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Shelar, Devendra, Giraldo, Jairo, and Amin, Saurabh
- Published
- 2015
- Full Text
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37. Network flow routing under strategic link disruptions.
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Dahan, Mathieu and Amin, Saurabh
- Published
- 2015
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38. Analyzing vulnerability of electricity distribution networks to DER disruptions.
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Shelar, Devendra and Amin, Saurabh
- Published
- 2015
- Full Text
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39. A network interdiction model for analyzing the vulnerability of water distribution systems.
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Perelman, Lina and Amin, Saurabh
- Published
- 2014
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40. A piecewise-deterministic Markov model of freeway accidents.
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Jin, Li and Amin, Saurabh
- Published
- 2014
- Full Text
- View/download PDF
41. A game theory model for electricity theft detection and privacy-aware control in AMI systems.
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Cardenas, Alvaro A., Amin, Saurabh, Schwartz, Galina, Dong, Roy, and Sastry, Shankar
- Published
- 2012
- Full Text
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42. On the characterization and computation of Nash equilibria on parallel networks with horizontal queues.
- Author
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Krichene, Walid, Reilly, Jack, Amin, Saurabh, and Bayen, Alexandre
- Abstract
We study inefficiencies in parallel networks with horizontal queues due to the selfish behavior of players, by comparing social optima to Nash equilibria. The article expands studies on routing games which traditionally model congestion with latency functions that increase with the flow on a particular link. This type of latency function cannot capture congestion effects on horizontal queues. Latencies on horizontal queues increase as a function of density, and flow can decrease with increasing latencies. This class of latency functions arises in transportation networks. For static analysis of horizontal queues on parallel-link networks, we show that there may exist multiple Nash equilibria with different total costs, which contrasts with results for increasing latency functions. We present a novel algorithm, quadratic in the number of links, for computing the Nash equilibrium that minimizes total cost (best Nash equilibrium). The relative inefficiencies of best Nash equilibria are evaluated through analysis of the price of stability, and analytical results are presented for two-link networks. Price of stability is shown to be sensitive to changes in demand when links are near capacity, and congestion mitigation strategies are discussed, motivated by our results. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
43. On Stackelberg routing on parallel networks with horizontal queues.
- Author
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Krichene, Walid, Reilly, Jack, Amin, Saurabh, and Bayen, Alexandre
- Abstract
In order to address inefficiencies of Nash equilibria for congestion networks with horizontal queues, we study the Stackelberg routing game on parallel networks: assuming a coordinator has control over a fraction of the flow, and that the remaining players respond selfishly, what is an optimal Stackelberg strategy of the coordinator, i.e. a strategy that minimizes the cost of the induced equilibrium? We study Stackelberg routing for a new class of latency functions, which models congestion on horizontal queues. We introduce a candidate strategy, the non-compliant first strategy, and prove it to be optimal. Then we apply these results by modeling a transportation network in which a coordinator can choose the routes of a subset of the drivers, while the rest of the drivers choose their routes selfishly. [ABSTRACT FROM PUBLISHER]
- Published
- 2012
- Full Text
- View/download PDF
44. Incentives and Security in Electricity Distribution Networks.
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Amin, Saurabh, Schwartz, Galina A., and Tembine, Hamidou
- Published
- 2012
- Full Text
- View/download PDF
45. Attacks against process control systems.
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Cárdenas, Alvaro A., Amin, Saurabh, Lin, Zong-Syun, Huang, Yu-Lun, Huang, Chi-Yen, and Sastry, Shankar
- Published
- 2011
- Full Text
- View/download PDF
46. On the interdependence of reliability and security in Networked Control Systems.
- Author
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Amin, Saurabh, Schwartz, Galina A., and Sastry, S. Shankar
- Abstract
This paper studies player incentives to invest in network reliability and security. We consider heterogeneous networked control system (NCS) - also called players - facing a class of problems involving discrete interdependent risks. We formulate the problem of security choices of the individual NCS as a non-cooperative two-stage game, in which players make they security and control decisions, respectively. We characterize equilibria of the game, thus determining the individually optimal security levels. The presence of interdependent security causes a negative externality, and the individual players tend to under invest in security relative to the social optimum. From our results, security and reliability decisions are tightly coupled, and should be considered jointly to improve efficiency. [ABSTRACT FROM PUBLISHER]
- Published
- 2011
- Full Text
- View/download PDF
47. Network design game with both reliability and security failures.
- Author
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Schwartz, Galina A., Amin, Saurabh, Gueye, Assane, and Walrand, Jean
- Published
- 2011
- Full Text
- View/download PDF
48. Incentive schemes for Internet congestion management: Raffles versus time-of-day pricing.
- Author
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Loiseau, Patrick, Schwartz, Galina, Musacchio, John, and Amin, Saurabh
- Published
- 2011
- Full Text
- View/download PDF
49. Stealthy deception attacks on water SCADA systems.
- Author
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Amin, Saurabh, Litrico, Xavier, Sastry, S. Shankar, and Bayen, Alexandre M.
- Published
- 2010
- Full Text
- View/download PDF
50. Security Interdependencies for Networked Control Systems with Identical Agents.
- Author
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Amin, Saurabh, Schwartz, Galina A., and Sastry, S. Shankar
- Abstract
This paper studies the security choices of identical plant- controller systems, when their security is interdependent due the exposure to network induced risks. Each plant is modeled by a discrete-time stochastic linear system, which is sensed and controlled over a communication network. We model security decisions of the individual systems (also called players) as a game. We consider a two-stage game, in which first, the players choose whether to invest in security or not; and thereafter, choose control inputs to minimize the average operational costs. We fully characterize equilibria of the game, which give us the individually optimal security choices. We also find the socially optimal choices. The presence of security interdependence creates a negative externality, and results in a gap between the individual and the socially optimal security choices for a wide range of security costs. Due to the negative externality, the individual players tend to under invest in security. [ABSTRACT FROM AUTHOR]
- Published
- 2010
- Full Text
- View/download PDF
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