In the paper a new deterministic continuum-strategy two-player discrete-time dynamic Stackelberg game is proposed with fixed finite time duration and closed-loop information structure. The considered payoff functions ...
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In the paper a new deterministic continuum-strategy two-player discrete-time dynamic Stackelberg game is proposed with fixed finite time duration and closed-loop information structure. The considered payoff functions can be widely used in different applications (mainly in conflicts of consuming a limited resource, where one player, called leader, is a superior authority choosing strategy first, and another player, called follower, chooses after). In case of convex payoff functions and certain parameter values, we give a new particular backward induction algorithm, which can be easily realized to find a (leader-follower) equilibrium of the game (in a certain sequential equilibrium realization from the last step towards the first one with respect to the current strategy choices of the players). Considerations on uniqueness and game regulation (i.e. setting parameters of the game to achieve a predefined equilibrium) are also provided. The finite version of the game (with finite strategy sets) is also given along with its simplification and solution method. Several practical examples are shown to illustrate the comprehensive application possibilities of the results. (C) 2014 Elsevier B.V. All rights reserved.
Self-healing has been increasingly integrated into systems to enhance their reliability. Many popular intrinsic self-healing policies are not cost-effective because their self-healing actions are not always performed ...
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Self-healing has been increasingly integrated into systems to enhance their reliability. Many popular intrinsic self-healing policies are not cost-effective because their self-healing actions are not always performed at the right time. This paper investigates the economic design of a self-healing policy with limited agents for an intelligent system that executes a mission of finite length. Several healing agents are embedded into the system before the mission. The system performs self-detection at equidistant epochs to reveal deterioration levels. The deterioration can be randomly healed by releasing healing agents, and the healing effect depends on the number of agents released. At each inspection epoch, a decision is made on how many healing agents to be released. The objective is to jointly determine the capacity of agents and the self-healing policy to minimize the expected total cost over the mission length. The optimization problem is formulated in the stochastic dynamic programming framework. A backward induction algorithm is developed to find the optimal solution. A numerical example is provided to illustrate the effectiveness of the proposed approach. The comparison with a control-limit policy confirms the outstanding performance of the proposed policy.
This article considers remote state estimation in cyber-physical systems (CPSs) with multiple sensors. Each plant is modeled by a discrete-time stochastic linear system with measurements of each sensor transmitted to ...
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This article considers remote state estimation in cyber-physical systems (CPSs) with multiple sensors. Each plant is modeled by a discrete-time stochastic linear system with measurements of each sensor transmitted to the corresponding remote estimator over a shared communication network when their securities are interdependent due to network-induced risks. A dynamic nonzero-sum game with asymmetric information is formulated in which each sensor subject to a resource budget constraint needs to decide whether to invest in security for sending data packets, taking the behaviors of other sensors into account. To overcome the difficulty in characterizing or computing the Nash equilibria (NE), the game with asymmetric information is transformed into another game with symmetric information such that the equilibrium of the original game can be obtained by solving the equilibrium of the new game. Under certain conditions, we devise a backward induction algorithm to obtain a subclass of NE of the original game, known as common information-based Markov perfect equilibria (CIBMPE). Finally, a numerical example is provided to illustrate the results obtained.
This article investigates the transmission scheduling problem of cyber physical systems (CPSs). Specifically, in a CPS, a sensor collects real-time data of the monitoring area and at each decision epoch, the system mu...
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This article investigates the transmission scheduling problem of cyber physical systems (CPSs). Specifically, in a CPS, a sensor collects real-time data of the monitoring area and at each decision epoch, the system must determine whether to transmit data packets to the gateway through an unreliable wireless channel. Furthermore, we assume that the CPS is subject to an energy-harvesting (EH) eavesdropper, and the communication channel is wiretapped randomly when the harvested energy of the eavesdropper is sufficient. The objective is to obtain the optimal transmission scheduling to minimize the Age of Information (AoI) of the CPS while keeping the AoI of eavesdropper above a certain level. To achieve this, we first transform the system model into a Markov decision process (MDP). We then prove that the optimal transmission scheduling policy is a threshold behavior on the AoI of both the CPS and the eavesdropper, respectively. Based on the structural properties of the optimal policy, we have developed a new backward induction algorithm to compute the optimal AoI-based transmission scheduling and the performance index function with lower computational costs compared to the conventional inductionalgorithm. Finally, we verify the validity of the algorithm and the correctness of the theoretical results through simulations.
This paper considers remote state estimation in cyber-physical systems (CPSs) with multiple sensors, where measurements of each sensor are transmitted to the corresponding remote estimator over a shared communication ...
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This paper considers remote state estimation in cyber-physical systems (CPSs) with multiple sensors, where measurements of each sensor are transmitted to the corresponding remote estimator over a shared communication network with interdependent security. A stochastic non-cooperative game framework with asymmetric information is provided, in which each sensor is in pursuit of minimizing the security investment cost on the first stage associated with the expected error covariance as small as possible at the corresponding remote estimator on the second stage. The asymmetry of information among sensors poses a challenge to characterize or compute the Nash equilibria (NE). To overcome the challenge, based on the common information among sensors, the game with asymmetric information is transformed into another game with symmetric information such that a subclass of NE refer to common information based Markov perfect equilibria of the original game can be achieved by using a backward induction algorithm. Finally, a numerical example is presented to verify the obtained results.
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