arXiv subjects

Juntao Chen

Publications and source records attributed to Juntao Chen.

At least 55 records · Page 3Linked to original sources

arXiv2023

UNO-QA: An Unsupervised Anomaly-Aware Framework with Test-Time Clustering for OCTA Image Quality Assessment

Medical image quality assessment (MIQA) is a vital prerequisite in various medical image analysis applications. Most existing MIQA algorithms are fully supervised that request a large amount of annotated data. However, annotating medical images is time-consuming and labor-intensive. In this paper, we propose an unsupervised anomaly-aware framework with test-time clustering for optical coherence tomography angiography (OCTA) image quality assessment in a setting wherein only a set of high-quality samples are accessible in the training phase. Specifically, a feature-embedding-based low-quality representation module is proposed to quantify the quality of OCTA images and then to discriminate between outstanding quality and non-outstanding quality. Within the non-outstanding quality class, to further distinguish gradable images from ungradable ones, we perform dimension reduction and clustering of multi-scale image features extracted by the trained OCTA quality representation network. Extensive experiments are conducted on one publicly accessible dataset sOCTA-3*3-10k, with superiority of our proposed framework being successfully established.

cs.CV↗

arXiv2023

Security Investment Over Networks with Bounded Rational Agents: Analysis and Distributed Algorithm

This paper considers the security investment problem over a network in which the resource owners aim to allocate their constrained security resources to heterogeneous targets strategically. Investing in each target makes it less vulnerable, and thus lowering its probability of a successful attack. However, humans tend to perceive such probabilities inaccurately yielding bounded rational behaviors; a phenomenon frequently observed in their decision-making when facing uncertainties. We capture this human nature through the lens of cumulative prospect theory and establish a behavioral resource allocation framework to account for the human's misperception in security investment. We analyze how this misperception behavior affects the resource allocation plan by comparing it with the accurate perception counterpart. The network can become highly complex with a large number of participating agents. To this end, we further develop a fully distributed algorithm to compute the behavioral security investment strategy efficiently. Finally, we corroborate our results and illustrate the impacts of human's bounded rationality on the resource allocation scheme using cases studies.

cs.SI↗

arXiv2023

QoS Based Contract Design for Profit Maximization in IoT-Enabled Data Markets

The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The massive deployment of Internet of Things (IoT) devices, including sensors and actuators, is ushering in smart and connected communities of the future. The availability of real-time and high-quality sensor data is crucial for various IoT applications, particularly in healthcare, energy, transportation, etc. However, data collection may have to be outsourced to external service providers (SPs) due to cost considerations or lack of specialized equipment. Hence, the data market plays a critical role in such scenarios where SPs have different quality levels of available data, and IoT users have different application-specific data needs. The pairing between data available to the SP and users in the data market requires an effective mechanism design that considers the SPs' profitability and the quality-of-service (QoS) needs of the users. We develop a generic framework to analyze and enable such interactions efficiently, leveraging tools from contract theory and mechanism design theory. It can enable and empower emerging data sharing paradigms such as Sensing-as-a-Service (SaaS). The contract design creates a pricing structure for on-demand sensing data for IoT users. By considering a continuum of user types, we capture a diverse range of application requirements and propose optimal pricing and allocation rules that ensure QoS provisioning and maximum profitability for the SP. Furthermore, we provide analytical solutions for fixed distributions of user types to analyze the developed approach. For comparison, we consider the benchmark case assuming complete information of the user types and obtain optimal contract solutions. Finally, a case study is presented to demonstrate the efficacy of the proposed contract design framework.

eess.SY↗

arXiv2022

Differentially Private ADMM-Based Distributed Discrete Optimal Transport for Resource Allocation

Optimal transport (OT) is a framework that can guide the design of efficient resource allocation strategies in a network of multiple sources and targets. To ease the computational complexity of large-scale transport design, we first develop a distributed algorithm based on the alternating direction method of multipliers (ADMM). However, such a distributed algorithm is vulnerable to sensitive information leakage when an attacker intercepts the transport decisions communicated between nodes during the distributed ADMM updates. To this end, we propose a privacy-preserving distributed mechanism based on output variable perturbation by adding appropriate randomness to each node's decision before it is shared with other corresponding nodes at each update instance. We show that the developed scheme is differentially private, which prevents the adversary from inferring the node's confidential information even knowing the transport decisions. Finally, we corroborate the effectiveness of the devised algorithm through case studies.

cs.SI↗

arXiv2022

VaxEquity: A Data-Driven Risk Assessment and Optimization Framework for Equitable Vaccine Distribution

With the continuous rise of the COVID-19 cases worldwide, it is imperative to ensure that all those vulnerable countries lacking vaccine resources can receive sufficient support to contain the risks. COVAX is such an initiative operated by the WHO to supply vaccines to the most needed countries. One critical problem faced by the COVAX is how to distribute the limited amount of vaccines to these countries in the most efficient and equitable manner. This paper aims to address this challenge by first proposing a data-driven risk assessment and prediction model and then developing a decision-making framework to support the strategic vaccine distribution. The machine learning-based risk prediction model characterizes how the risk is influenced by the underlying essential factors, e.g., the vaccination level among the population in each COVAX country. This predictive model is then leveraged to design the optimal vaccine distribution strategy that simultaneously minimizes the resulting risks while maximizing the vaccination coverage in these countries targeted by COVAX. Finally, we corroborate the proposed framework using case studies with real-world data.

cs.CE↗

arXiv2021

A Dynamic Game Approach to Designing Secure Interdependent IoT-Enabled Infrastructure Network

The emerging Internet of Things (IoT) applications that leverage ubiquitous connectivity and big data are facilitating the realization of smart everything initiatives. IoT-enabled infrastructures have naturally a multi-layer system architecture with an overlaid or underlaid device network and its coexisting infrastructure network. The connectivity between different components in these two heterogeneous interdependent networks plays an important role in delivering real-time information and ensuring a high-level situational awareness. However, IoT-enabled infrastructures face cyber threats due to the wireless nature of communications. Therefore, maintaining network connectivity in the presence of adversaries is a critical task for infrastructure network operators. In this paper, we establish a three-player three-stage dynamic game-theoretic framework including two network operators and one attacker to capture the secure design of multi-layer interdependent infrastructure networks by allocating limited resources. We use subgame perfect Nash equilibrium (SPE) to characterize the strategies of players with sequential moves. In addition, we assess the efficiency of the equilibrium network by comparing with its team optimal solution counterparts in which two network operators can coordinate. We further design a scalable algorithm to guide the construction of the equilibrium IoT-enabled infrastructure networks. Finally, we use case studies on the emerging paradigm of the Internet of Battlefield Things (IoBT) to corroborate the obtained results.

cs.NI↗

arXiv2021

Resilient and Distributed Discrete Optimal Transport with Deceptive Adversary: A Game-Theoretic Approach

Optimal transport (OT) is a framework that can be used to guide the optimal allocation of a limited amount of resources. The classical OT paradigm does not consider malicious attacks in its formulation and thus the designed transport plan lacks resiliency to an adversary. To address this concern, we establish an OT framework that explicitly accounts for the adversarial and stealthy manipulation of participating nodes in the network during the transport strategy design. Specifically, we propose a game-theoretic approach to capture the strategic interactions between the transport planner and the deceptive attacker. We analyze the properties of the established two-person zero-sum game thoroughly. We further develop a fully distributed algorithm to compute the optimal resilient transport strategies, and show the convergence of the algorithm to a saddle-point equilibrium. Finally, we demonstrate the effectiveness of the designed algorithm using case studies.

eess.SY↗

arXiv2021

Optimal Curing Strategy for Competing Epidemics Spreading over Complex Networks

Optimal curing strategy of suppressing competing epidemics spreading over complex networks is a critical issue. In this paper, we first establish a framework to capture the coupling between two epidemics, and then analyze the system's equilibrium states by categorizing them into three classes, and deriving their stability conditions. The designed curing strategy globally optimizes the trade-off between the curing cost and the severity of epidemics in the network. In addition, we provide structural results on the predictability of epidemic spreading by showing the existence and uniqueness of the solution. We also demonstrate the robustness of curing strategy by showing the continuity of epidemic severity with respect to the applied curing effort. A gradient descent algorithm based on a fixed-point iterative scheme is proposed to find the optimal curing strategy. Depending on the system parameters, the curing strategy can lead to switching between equilibria of the epidemic network as the control cost varies. Finally, we use case studies to corroborate and illustrate the obtained theoretical results.

eess.SY↗

arXiv2021

Transactive Resilience in Renewable Microgrids: A Contract-Theoretic Approach

Renewable energy-based microgrids play a critical role in future smart grids. Due to the uncertainties of renewable generations, the microgrids face potential risk of load shedding during operation. To address this problem, we propose a contract-based approach to enhance the resilience of microgrids. Specifically, in the framework, the microgrids who may not be self-efficient to meet their local demands can purchase the needed power from their connected microgrids by signing a contract that specifies the power price in advance. We leverage a principal-agent model to capture the energy trading relationships between the microgrids through a resilience as a service (RaaS) paradigm. By focusing on the incentive compatible and individual rational constraints of the service requester, the service provider designs the optimal contracts for the transactive resilience that yields the largest payoff despite the incomplete information. We characterize analytical solutions of the optimal contracts for several scenarios where the service requester has various options on its hidden actions. Numerical simulations are used to illustrate and corroborate the obtained results.

eess.SY↗

arXiv2021

Fair and Distributed Dynamic Optimal Transport for Resource Allocation over Networks

Optimal transport is a framework that facilitates the most efficient allocation of a limited amount of resources. However, the most efficient allocation scheme does not necessarily preserve the most fairness. In this paper, we establish a framework which explicitly considers the fairness of dynamic resource allocation over a network with heterogeneous participants. As computing the transport strategy in a centralized fashion requires significant computational resources, it is imperative to develop computationally light algorithm that can be applied to large scale problems. To this end, we develop a fully distributed algorithm for fair and dynamic optimal transport with provable convergence using alternating method of multipliers. In the designed algorithm, each corresponding pair of resource supplier and receiver compute their own solutions and update the transport schemes through negotiation iteratively which do not require a central planner. The distributed algorithm can yield a fair and efficient resource allocation mechanism over a network. We corroborate the obtained results through case studies.

math.OC↗

arXiv2021

Assets Defending Differential Games with Partial Information and Selected Observations

In this paper, we consider a linear-quadratic-Gaussian defending assets differential game (DADG) where the attacker and the defender do not know each other's state information while they know the trajectory of a moving asset. Both players can choose to observe the other player's state information by paying a cost. The defender and the attacker have to craft both control strategies and observation strategies. We obtain a closed-form feedback solution that characterizes the Nash control strategies. We show that the trajectory of the asset does not affect both players' observation choices. Moreover, we show that the observation choices of the defender and the attacker can be decoupled and the Nash observation strategies can be found by solving two independent optimization problems. A set of necessary conditions is developed to characterize the optimal observation instances. Based on the necessary conditions, an effective algorithm is proposed to numerically compute the optimal observation instances. A case study is presented to demonstrate the effectiveness of the optimal observation instances.

eess.SY↗

arXiv2021

Locally-Aware Constrained Games on Networks

Network games have been instrumental in understanding strategic behaviors over networks for applications such as critical infrastructure networks, social networks, and cyber-physical systems. One critical challenge of network games is that the behaviors of the players are constrained by the underlying physical laws or safety rules, and the players may not have complete knowledge of network-wide constraints. To this end, this paper proposes a game framework to study constrained games on networks, where the players are locally aware of the constraints. We use \textit{awareness levels} to capture the scope of the network constraints that players are aware of. We first define and show the existence of generalized Nash equilibria (GNE) of the game, and point out that higher awareness levels of the players would lead to a larger set of GNE solutions. We use necessary and sufficient conditions to characterize the GNE, and propose the concept of the dual game to show that one can convert a locally-aware constrained game into a two-layer unconstrained game problem. We use linear quadratic games as case studies to corroborate the analytical results, and in particular, show the duality between Bertrand games and Cournot games.%, where each layer comprises an unconstrained game.

eess.SY↗

arXiv2020

Distributed Stabilization of Two Interdependent Markov Jump Linear Systems with Partial Information

In this paper, we study the stabilization of two interdependent Markov jump linear systems (MJLSs) with partial information, where the interdependency arises as the transition of the mode of one system depends on the states of the other system. First, we formulate a framework for the two interdependent MJLSs to capture the interactions between various entities in the system, where the modes of the system cannot be observed directly. Instead, a signal which contains information of the modes can be obtained. Then, depending on the scope of the available system state information (global or local), we design centralized and distributed controllers, respectively, that can stochastically stabilize the overall interdependent MJLS. In addition, the sufficient stabilization conditions for the system under both types of information structure are derived. Finally, we provide a numerical example to illustrate the effectiveness of the designed controllers.

eess.SY↗

arXiv2019

Control of Multi-Layer Mobile Autonomous Systems in Adversarial Environments: A Games-in-Games Approach

Mobile autonomous system (MAS) becomes pervasive especially in the vehicular and robotic networks. Multiple heterogeneous MAS networks can be integrated together as a multi-layer MAS network to offer holistic services. The network connectivity of multi-layer MAS plays an important role in the information exchange between agents within and across different layers of the network. In this paper, we establish a games-in-games framework to capture the uncoordinated nature of decision makings under adversarial environment at different layers. Specifically, each network operator controls the mobile agents in his own subnetwork and designs a secure strategy to maximize the global network connectivity by considering the behavior of jamming attackers that aim to disconnect the network. The solution concept of meta-equilibrium is proposed to characterize the system-of-systems behavior of the autonomous agents. For online implementation of the control, we design a resilient algorithm that improves the network algebraic connectivity iteratively. We show that the designed algorithm converges to a meta-equilibrium asymptotically. Finally, we use case studies of a two-layer MAS network to corroborate the security and agile resilience of the network controlled by the proposed strategy.

eess.SY↗

arXiv2019

Dynamic Games for Secure and Resilient Control System Design

Modern control systems are featured by their hierarchical structure composing of cyber, physical, and human layers. The intricate dependencies among multiple layers and units of modern control systems require an integrated framework to address cross-layer design issues related to security and resilience challenges. To this end, game theory provides a bottom-up modeling paradigm to capture the strategic interactions among multiple components of the complex system and enables a holistic view to understand and design cyber-physical-human control systems. In this review, we first provide a multi-layer perspective toward increasingly complex and integrated control systems and then introduce several variants of dynamic games for modeling different layers of control systems. We present game-theoretic methods for understanding the fundamental tradeoffs of robustness, security, and resilience and developing a clean-slate cross-layer approach to enhance the system performance in various adversarial environments. This review also includes three quintessential research problems that represent three research directions where dynamic game approaches can bridge between multiple research areas and make significant contributions to the design of modern control systems. The paper is concluded with a discussion on emerging areas of research that crosscut dynamic games and control systems.

eess.SY↗

arXiv2019

Dynamic Contract Design for Systemic Cyber Risk Management of Interdependent Enterprise Networks

The interconnectivity of cyber and physical systems and Internet of things has created ubiquitous concerns of cyber threats for enterprise system managers. It is common that the asset owners and enterprise network operators need to work with cybersecurity professionals to manage the risk by remunerating them for their efforts that are not directly observable. In this paper, we use a principal-agent framework to capture the service relationships between the two parties, i.e., the asset owner (principal) and the cyber risk manager (agent). Specifically, we consider a dynamic systemic risk management problem with asymmetric information where the principal can only observe cyber risk outcomes of the enterprise network rather than directly the efforts that the manager expends on protecting the resources. Under this information pattern, the principal aims to minimize the systemic cyber risks by designing a dynamic contract that specifies the compensation flows and the anticipated efforts of the manager by taking into account his incentives and rational behaviors. We formulate a bi-level mechanism design problem for dynamic contract design within the framework of a class of stochastic differential games. We show that the principal has rational controllability of the systemic risk by designing an incentive compatible estimator of the agent's hidden efforts. We characterize the optimal solution by reformulating the problem as a stochastic optimal control program which can be solved using dynamic programming. We further investigate a benchmark scenario with complete information and identify conditions that yield zero information rent and lead to a new certainty equivalence principle for principal-agent problems. Finally, case studies over networked systems are carried out to illustrate the theoretical results obtained.

eess.SY↗

arXiv2019

A Dynamic Game Approach to Strategic Design of Secure and Resilient Infrastructure Network

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. We further investigate the resilience planning of the defender and the strategic timing of attack of the adversary. Finally, we use case studies of UAV-enabled communication networks for disaster recovery to corroborate the obtained analytical results.

eess.SY↗

arXiv2019

A Games-in-Games Approach to Mosaic Command and Control Design of Dynamic Network-of-Networks for Secure and Resilient Multi-Domain Operations

This paper presents a games-in-games approach to provide design guidelines for mosaic command and control that enables the secure and resilient multi-domain operations. Under the mosaic design, pieces or agents in the network are equipped with flexible interoperability and the capability of self-adaptability, self-healing, and resiliency so that they can reconfigure their responses to achieve the global mission in spite of failures of nodes and links in the adversarial environment. The proposed games-in-games approach provides a system-of-systems science for mosaic distributed design of large-scale systems. Specifically, the framework integrates three layers of design for each agent including strategic layer, tactical layer, and mission layer. Each layer in the established model corresponds to a game of a different scale that enables the integration of threat models and achieve self-mitigation and resilience capabilities. The solution concept of the developed multi-layer multi-scale mosaic design is characterized by Gestalt Nash equilibrium (GNE) which considers the interactions between agents across different layers. The developed approach is applicable to modern battlefield networks which are composed of heterogeneous assets that access highly diverse and dynamic information sources over multiple domains. By leveraging mosaic design principles, we can achieve the desired operational goals of deployed networks in a case study and ensure connectivity among entities for the exchange of information to accomplish the mission.

eess.SY↗