Juntao Chen

Interdependent network formation games with an application to critical infrastructures

The increasing connectivity between critical infrastructures creates a network of networks in which the interdependencies between the networks play an important role in understanding the emerging functions and performances. To this end, this paper aims to establish a game-theoretic framework to capture the interactions between two system designers who aim to maximize individual network utilities. In particular, we use the game model to investigate the decentralized interdependent network for maximizing the algebraic connectivity of the global network. We develop an alternating play algorithm, and show its convergence to a Nash equilibrium network after a finite number of iterations. We corroborate our results through case studies of power and communication networks, and compare the Nash equilibrium solutions with their constrained team solution counterparts. The experimental results provide design guidelines and insights to increase the efficiency of the interdependent network formation games.

Resilient and decentralized control of multi-level cooperative mobile networks to maintain connectivity under adversarial environment

Network connectivity plays an important role in the information exchange between different agents in the multi-level networks. In this paper, we establish a game-theoretic framework to capture the uncoordinated nature of the decision-making at different layers of the multi-level networks. Specifically, we design a decentralized algorithm that aims to maximize the algebraic connectivity of the global network iteratively. In addition, we show that the designed algorithm converges to a Nash equilibrium asymptotically and yields an equilibrium network. To study the network resiliency, we introduce three adversarial attack models and characterize their worst-case impacts on the network performance. Case studies based on a two-layer mobile robotic network are used to corroborate the effectiveness and resiliency of the proposed algorithm and show the interdependency between different layers of the network during the recovery processes.

A Game-Theoretic Framework for Resilient and Distributed Generation Control of Renewable Energies in Microgrids

The integration of microgrids that depend on the renewable distributed energy resources with the current power systems is a critical issue in the smart grid. In this paper, we propose a non-cooperative game-theoretic framework to study the strategic behavior of distributed microgrids that generate renewable energies and characterize the power generation solutions by using the Nash equilibrium concept. Our framework not only incorporates economic factors but also takes into account the stability and efficiency of the microgrids, including the power flow constraints and voltage angle regulations. We develop two decentralized update schemes for microgrids and show their convergence to a unique Nash equilibrium. Also, we propose a novel fully distributed PMU-enabled algorithm which only needs the information of voltage angle at the bus. To show the resiliency of the distributed algorithm, we introduce two failure models of the smart grid. Case studies based on the IEEE 14-bus system are used to corroborate the effectiveness and resiliency of the proposed algorithms.

Optimal Contract Design Under Asymmetric Information for Cloud-Enabled Internet of Controlled Things

The development of advanced wireless communication technologies and smart embedded control devices makes everything connected, leading to an emerging paradigm of the Internet of Controlled Things IoCT. IoCT consists of two layers of systems: cyber layer and physical layer. This work aims to establish a holistic framework that integrates the cyber-physical layers of the IoCT through the lens of contract theory. For the cyber layer, we use a FlipIt game to capture the cloud security. We focus on two types of cloud, high-type and low-type, in terms of their provided quality of service QoS. The clouds type is of private information which is unknown to the contract maker. Therefore, the control system administrator CSA at the physical layer needs to design a menu of two contracts for each type of service provider SP due to this asymmetric information structure. According to the received contract, SP decides his cyber defense strategy in the FlipIt game of which the Nash equilibrium determines the QoS of the cloud, and further influences the physical system performance. The objective of CSA is to minimize the payment to the cloud SP and the control cost jointly by designing optimal contracts. Due to the interdependence between the cyber and physical layers in the cloud-enabled IoCT, we need to address the cloud security and contract design problems in an integrative manner. We find that CSA always requires the best QoS from two types of cloud. In addition, under the optimal contracts, the utilities of both SPs are constants. Furthermore, no contracts will be offered to the cloud if the resulting service cannot stabilize the physical system.

Security as a Service for Cloud-Enabled Internet of Controlled Things Under Advanced Persistent Threats: A Contract Design Approach

In this paper, we aim to establish a holistic framework that integrates the cyber-physical layers of a cloud-enabled Internet of Controlled Things (IoCT) through the lens of contract theory. At the physical layer, the device uses cloud services to operate the system. The quality of cloud services is unknown to the device, and hence the device designs a menu of contracts to enable a reliable and incentive-compatible service. Based on the received contracts, the cloud service provider (SP) serves the device by determining its optimal cyber defense strategy. A contract-based FlipCloud game is used to assess the security risk and the cloud quality of service (QoS) under advanced persistent threats. The contract design approach creates a pricing mechanism for on-demand security as a service for cloud-enabled IoCT. By focusing on high and low QoS types of cloud SPs, we find that the contract design can be divided into two regimes (regimes I and II) with respect to the provided cloud QoS. Specifically, the physical devices whose optimal contracts are in regime I always request the best possible cloud security service. In contrast, the device only asks for a cloud security level that can stabilize the system when the optimal contracts lie in regime II. We illustrate the obtained results via case studies of a cloud-enabled smart home.

A Large-Scale Markov Game Approach to Dynamic Protection of Interdependent Infrastructure Networks

The integration of modern information and communication technologies (ICTs) into critical infrastructures (CIs) improves its connectivity and functionalities yet also brings cyber threats. It is thus essential to understand the risk of ICTs on CIs holistically as a cyber-physical system and design efficient security hardening mechanisms. To this end, we capture the system behaviors of the CIs under malicious attacks and the protection strategies by a zero-sum game. We further propose a computationally tractable approximation for large-scale networks which builds on the factored graph that exploits the dependency structure of the nodes of CIs and the approximate dynamic programming tools for stochastic Markov games. This work focuses on a localized information structure and the single-controller game solvable by linear programming. Numerical results illustrate the proper tradeoff of the approximation accuracy and computation complexity in the new design paradigm and show the proactive security at the time of unanticipated attacks.

A Stackelberg Game Approach for Two-Level Distributed Energy Management in Smart Grids

The pursuit of sustainability motivates microgrids that depend on distributed resources to produce more renewable energies. An efficient operation and planning relies on a holistic framework that takes into account the interdependent decision-making of the generators of the existing power grids and the distributed resources of the microgrid in the integrated system. To this end, we use a Stackelberg game-theoretic framework to study the interactions between generators (leaders) and microgrids (followers). Entities on both sides make strategic decisions on the amount of power generation to maximize their payoffs. Our framework not only takes into account the economic factors but also incorporates the stability and efficiency of the smart grid, such as the power flow constraints and voltage angle regulations. We present three update schemes for microgrids. In addition, we develop three other algorithms for generators, and among which a fully distributed algorithm enabled by phasor measurement units is proposed. The distributed algorithm merely requires the information of voltage angles at local buses for updates, and its convergence to the unique equilibrium is shown. We further develop the implementation architectures of the update schemes in the smart grid. Finally, case studies are used to corroborate the effectiveness of the proposed algorithms.

Security investment under cognitive constraints: A Gestalt Nash equilibrium approach

With the increasing connectivity enabled by the Internet of Things (IoT), security becomes a critical concern, and the users should invest to secure their IoT applications. Due to the massive devices in the IoT network, users cannot be aware of the security policies taken by all its connected neighbors. Instead, a user makes security decisions based on the cyber risks he perceives by observing a selected number of nodes. To this end, we propose a model which incorporates the limited attention or bounded rationality nature of players in the IoT. Specifically, each individual builds a sparse cognitive network which includes the users to respond to. Based on this simplified cognitive network representation, each user then determines his security investment policy by minimizing his own real-world security cost. The bounded rational decision-makings of players and their cognitive network formations are interdependent, and thus should be addressed in a holistic manner. We propose a Gestalt Nash equilibrium (GNE) solution concept to characterize the decisions of agents. Then, we design a proximal-based iterative algorithm to compute the GNE and show its convergence. With case studies to smart home communities, the designed algorithm can successfully identify the critical users whose decisions need to be taken into account by the other users during the security investment.

A Dynamic Game Analysis and Design of Infrastructure Network Protection and Recovery: 125

Infrastructure networks are vulnerable to both cyber and physical attacks. Building a secure and resilient networked system is essential for providing reliable and dependable services. To this end, we establish a two-player three-stage game framework to capture the dynamics in the infrastructure protection and recovery phases. Specifically, the goal of the infrastructure network designer is to keep the network connected before and after the attack, while the adversary aims to disconnect the network by compromising a set of links. With costs for creating and removing links, the two players aim to maximize their utilities while minimizing the costs. In this paper, we use the concept of subgame perfect equilibrium (SPE) to characterize the optimal strategies of the network defender and attacker. We derive the SPE explicitly in terms of system parameters. Finally, we use a case study of UAV-enabled communication networks for disaster recovery to corroborate the obtained analytical results.

Optimal allocation of robotic wind turbine inspectors in a wind farm

Wind turbines play an important role in producing clean energy and reducing pollution, but they can fail during the operation due to various types of faults and environment uncertainties. Therefore, one of the key problems in wind energy systems is the resilient control to achieve their high reliability. To reach this goal, we first design a resilient controller by incorporating faults into the wind turbine model. Second, we utilize various kinds of robotic wind turbine inspectors (RWTI) that can detect different types of faults to support the resilient control. Then, we formulate an RWTI allocation problem which aims to minimize the damage due to faults in a wind farm. We quantify the damage by computing the control cost under faults, and also take the probability of fault occurrence into account. The solution to this optimization problem yields an optimal dispatching strategy of RWTIs in a wind farm, and enhances the resiliency of the wind energy system.