Showing papers in "IEEE Transactions on Control of Network Systems in 2020"

Distributed Resilient Observer-Based Fault-Tolerant Control for Heterogeneous Multiagent Systems Under Actuator Faults and DoS Attacks

Abstract: In this article, we investigate the distributed resilient observer-based fault-tolerant control problem for heterogeneous linear multiagent systems in the presence of actuator faults and denial-of-service (DoS) attacks. In order to estimate the system matrix and the state of the exosystem, novel resilient adaptive distributed observers are proposed. By using the dwell time technique and the Lyapunov stability theory, the observer error system is shown to be exponentially stable even under the influence of DoS attacks. Then, an algorithm is proposed to determine observer parameters with the aim of maximizing the ability to resist attacks. Besides, a novel fault-tolerant controller without causing chattering behaviors is designed to compensate for actuator faults. Moreover, it is proved that the developed controller solves the cooperative fault-tolerant output regulation problem by using a newly derived lemma. Finally, an illustrative example is provided to verify the effectiveness of the proposed method.

Online Optimization as a Feedback Controller: Stability and Tracking

Abstract: This paper develops and analyzes feedback-based online optimization methods to regulate the output of a linear time invariant (LTI) dynamical system to the optimal solution of a time-varying convex optimization problem. The design of the algorithm is based on continuous-time primal-dual dynamics, properly modified to incorporate feedback from the LTI dynamical system, applied to a proximal augmented Lagrangian function. The resultant closed-loop algorithm tracks the solution of the time-varying optimization problem without requiring knowledge of (time varying) disturbances in the dynamical system. The analysis leverages integral quadratic constraints to provide linear matrix inequality (LMI) conditions that guarantee global exponential stability and bounded tracking error. Analytical results show that under a sufficient time-scale separation between the dynamics of the LTI dynamical system and the algorithm, the LMI conditions can be always satisfied. This paper further proposes a modified algorithm that can track an approximate solution trajectory of the constrained optimization problem under less restrictive assumptions. As an illustrative example, the proposed algorithms are showcased for power transmission systems, to compress the time scales between secondary and tertiary control, and allow to simultaneously power rebalancing and tracking of the DC optimal power flow points.

Distributed Adaptive Time-Varying Group Formation Tracking for Multiagent Systems With Multiple Leaders on Directed Graphs

Abstract: This paper proposes a fully distributed control protocol that achieves time-varying group formation tracking for linear multiagent systems connected via a directed graph. The group formation tracking often leads to sub-formations especially when the leaders are placed far apart or they have separate control inputs. In the proposed approach, the followers are distributed into several subgroups and each subgroup attains the predefined subformation along with encompassing the leaders. Each subgroup can be assigned multiple leaders, contrary to the single-leader case considered in most existing literature, which makes the current problem nontrivial. When multiple leaders exist in a subgroup, the subformation attained by that subgroup keeps tracking a convex combination of the states of the leaders. A distributed adaptive control protocol has been introduced in this paper which uses only relative state information and, thus, avoids direct computation of the graph Laplacian matrix. Due to the virtue of this, the proposed scheme remains effective even when some of the agents get disconnected from the network due to sudden communication failure. An algorithm is provided to outline the steps to design the control law to attain time-varying group formation tracking with multiple leaders. Toward the end, a case study on multitarget surveillance operation is taken up to show an important application of the proposed adaptive control technique.

Finite-Time Consensus for Linear Multiagent Systems via Event-Triggered Strategy Without Continuous Communication

Abstract: Taking into account multiagent systems with general linear dynamics and directed topologies, the issue of achieving finite-time consensus in a distributed event-triggered fashion is discussed in this paper. A novel model-based triggering function, which depends only on local information, is adopted. To ensure the finite-time convergence of the disagreement vector and the triggering error, a dynamic threshold, which is guaranteed to converge to zero in finite time, is adopted in the proposed triggering function design. By employing a novel distributed event-triggered controller for each agent, finite-time consensus of multiagent systems can be achieved. In the proposed approach, no continuous communication is needed in either controller updates or triggering detection. Furthermore, the triggering number is significantly reduced and the high frequency triggering is restrained. In addition, the feasibility of the proposed approach is guaranteed by the comprehensive theoretical demonstration of the finite-time consensus stability and the analysis of the Zeno behavior. Finally, numerical simulations are carried out to illustrate the effectiveness of our results.

Dynamic NE Seeking for Multi-Integrator Networked Agents With Disturbance Rejection

Abstract: In this paper, we consider game problems played by (multi)-integrator agents, subject to external disturbances. We propose Nash equilibrium seeking dynamics based on gradient-play, augmented with a dynamic internal-model based component, which is a reduced-order observer of the disturbance. We consider single-, double-, and extensions to multi-integrator agents, in a partial-information setting, where agents have only partial knowledge on the others’ decisions over a network. The lack of global information is offset by each agent maintaining an estimate of the others’ states, based on local communication with its neighbors. Each agent has an additional dynamic component that drives its estimates to the consensus subspace. In all cases, we show convergence to the Nash equilibrium irrespective of disturbances. Our proofs leverage input-to-state stability under strong monotonicity of the pseudo-gradient and Lipschitz continuity of the extended pseudo-gradient.

Stability Conditions for Cluster Synchronization in Networks of Heterogeneous Kuramoto Oscillators

Abstract: In this paper, we study cluster synchronization in networks of oscillators with heterogenous Kuramoto dynamics, where multiple groups of oscillators with identical phases coexist in a connected network. Cluster synchronization is at the basis of several biological and technological processes; yet, the underlying mechanisms to enable the cluster synchronization of Kuramoto oscillators have remained elusive. In this paper, we derive quantitative conditions on the network weights, cluster configuration, and oscillators’ natural frequency that ensure the asymptotic stability of the cluster synchronization manifold; that is, the ability to recover the desired cluster synchronization configuration following a perturbation of the oscillators’ states. Qualitatively, our results show that cluster synchronization is stable when the intracluster coupling is sufficiently stronger than the intercluster coupling, the natural frequencies of the oscillators in distinct clusters are sufficiently different, or, in the case of two clusters, when the intracluster dynamics is homogeneous. We validate the effectiveness of our theoretical results via numerical studies.

Finite-Time Distributed Convex Optimization for Continuous-Time Multiagent Systems With Disturbance Rejection

Abstract: This paper presents continuous distributed algorithms to solve the finite-time distributed convex optimization problems of multiagent systems in the presence of disturbances. The objective is to design distributed algorithms such that a team of agents seeks to minimize the sum of local objective functions in a finite-time and robust manner. Specifically, a distributed optimization algorithm, combined with a continuous integral sliding-mode control scheme, is proposed to solve this finite-time optimization problem, while rejecting local disturbance signals. The developed algorithm is further applied to solve economic dispatch and resource allocation problems, and proven that under proposed schemes, the optimal solution can be achieved in finite time, while satisfying both global equality and local inequality constraints. Examples and numerical simulations are provided to show the effectiveness of the proposed methods.

On the Interaction Between Autonomous Mobility-on-Demand Systems and the Power Network: Models and Coordination Algorithms

Abstract: We study the interaction between a fleet of electric self-driving vehicles servicing on-demand transportation requests (referred to as autonomous mobility-on-demand, or AMoD, systems) and the electric power network. We propose a joint model that captures the coupling between the two systems stemming from the vehicles’ charging requirements, capturing time-varying customer demand, battery depreciation, and power transmission constraints. First, we show that the model is amenable to efficient optimization. Then, we prove that the socially optimal solution to the joint problem is a general equilibrium if locational marginal pricing is used for electricity. Finally, we show that the equilibrium can be computed by selfish transportation and generator operators (aided by a nonprofit independent system operator) without sharing private information. We assess the performance of the approach and its robustness to stochastic fluctuations in demand through case studies and agent-based simulations. Collectively, these results provide a first-of-a-kind characterization of the interaction between AMoD systems and the power network, and shed additional light on the economic and societal value of AMoD.

Consensus Disturbance Rejection for Linear Multiagent Systems With Directed Switching Communication Topologies

Abstract: This paper addresses the consensus disturbance rejection problem for multiple-input multiple-output linear multiagent systems (MASs) with directed fixed as well as switching communication topologies in the presence of deterministic disturbances. Based on the relative output information among neighboring agents, a controller, which incorporates the consensus error estimator, the state estimator with static coupling strength and the disturbance observer, is given such that consensus tracking could be achieved in the considered MASs under directed switching communication topologies provided that the control parameters are suitably chosen and the average dwell time is larger than a positive threshold, while the disturbances are fully rejected by the designed disturbance observer. Furthermore, by designing a state estimator with adaptive coupling law, a controller is given such that consensus disturbance rejection of MASs with directed fixed topology could be achieved in a fully distributed manner. The obtained theoretical results are finally validated by performing simulations on the unmanned aerial vehicles.

Constraint-Coupled Distributed Optimization: A Relaxation and Duality Approach

Abstract: In this paper, we consider a general challenging distributed optimization setup arising in several important network control applications. Agents of a network want to minimize the sum of local cost functions, each one depending on a local variable, subject to local and coupling constraints, with the latter involving all the decision variables. We propose a novel fully distributed algorithm based on a relaxation of the primal problem and an elegant exploration of duality theory. Despite its complex derivation, based on several duality steps, the distributed algorithm has a very simple and intuitive structure. That is, each node finds a primal-dual optimal solution pair of a local relaxed version of the original problem and then updates suitable auxiliary local variables. We prove that agents asymptotically compute their portion of an optimal (feasible) solution of the original problem. This primal recovery property is obtained without any averaging mechanism typically used in dual decomposition methods. To corroborate the theoretical results, we show how the methodology applies to an instance of a distributed model-predictive control scheme in a microgrid control scenario.

Resilient Consensus Through Event-Based Communication

Abstract: We consider resilient versions of discrete-time multiagent consensus in the presence of faulty or even malicious agents in the network. In particular, we develop event-triggered update rules that can mitigate the influence of the malicious agents and, at the same time, reduce the communication. Each regular agent updates its state based on a given rule using its neighbors’ information. Only when the triggering condition is satisfied, the regular agents send their current states to their neighbors. Otherwise, the neighbors will continue to use the state received the last time. Assuming that a bound on the number of malicious nodes is known, we propose two update rules with event-triggered communication. They follow the so-called mean subsequence reduced-type algorithms and ignore values received from potentially malicious neighbors. We characterize the necessary connectivity in the network for the algorithms to perform correctly, which are stated in terms of the notion of graph robustness. A numerical example is provided to demonstrate the effectiveness of the proposed approach.

Distributed Fault-Tolerant Control of Large-Scale Systems: An Active Fault Diagnosis Approach

Abstract: The paper proposes a methodology to effectively address the increasingly important problem of distributed fault-tolerant control for large-scale interconnected systems. The approach dealt with combines, in a holistic way, a distributed fault detection and isolation algorithm with a specific tube-based model predictive control scheme. A distributed fault-tolerant control strategy is illustrated to guarantee overall stability and constraint satisfaction even after the occurrence of a fault. In particular, each subsystem is controlled and monitored by a local unit. The fault diagnosis component consists of a passive set-based fault detection algorithm and an active fault isolation one, yielding fault-isolability subject to local input and state constraints. The distributed active fault isolation module–thanks to a modification of the local inputs–allows to isolate the fault that has occurred, avoiding the usual drawback of controllers that possibly hide the effect of the faults. The Active Fault Isolation method is used as a decision support tool for the fault-tolerant control strategy after fault detection. The distributed design of the tube-based model predictive control allows the possible disconnection of faulty subsystems or the reconfiguration of local controllers after fault isolation. Simulation results on a well-known power network benchmark show the effectiveness of the proposed methodology.

Adaptive Protocol Design For Distributed Tracking With Relative Output Information: A Distributed Fixed-Time Observer Approach

Abstract: This paper studies fully distributed adaptive protocol design for consensus tracking under leader–follower directed communication graphs, where only relative output information between neighboring agents is available. The main difficulty lies in the coupling between the structural constraint and the nonlinearity of the nominal controller, where the former is introduced by distributed observer design with only relative output information and the latter is due to adaptive gain to estimate the connectivity of the Laplacian matrix. To circumvent the aforementioned difficulty, we decouple it into two steps. First, the idea of an unknown input observer is introduced to propose the distributed observer, which can estimate the consensus error in fixed time. The fully distributed adaptive protocol is then generated by the proposed distributed observer to achieve consensus tracking. Both the full-order and reduced-order distributed fixed-time observers are proposed to form fully distributed adaptive protocols based on only relative output information, without using any eigenvalue information of the Laplacian matrix, or exchanged information of distributed observers between neighboring agents.

Security Against False Data-Injection Attack in Cyber-Physical Systems

Abstract: In this paper, secure, remote estimation of a linear Gaussian process via observations at multiple sensors is considered. Such a framework is relevant to many cyber-physical systems and Internet-of-things applications. Sensors make sequential measurements that are shared with a fusion center; the fusion center applies a filtering algorithm to make its estimates. The challenge is the presence of a few unknown malicious sensors which can inject anomalous observations to skew the estimates at the fusion center. The set of malicious sensors may be time varying. The problems of malicious sensor detection and secure estimation are considered. First, an algorithm for secure estimation is proposed. The proposed estimation scheme uses a novel filtering and learning algorithm, where an optimal filter is learned over time by using the sensor observations in order to filter out malicious sensor observations while retaining other sensor measurements. Next, a novel detector to detect injection attacks on an unknown sensor subset is developed. Numerical results demonstrate up to 3-dB gain in the mean-squared error and up to $\text{75} \%$ higher attack detection probability under a small false alarm rate constraint, against a competing algorithm that requires additional side information.

Distributed Continuous-Time Nonsmooth Convex Optimization With Coupled Inequality Constraints

Abstract: This paper studies distributed convex optimization problems over continuous-time multiagent networks subject to two types of constraints, i.e., local feasible set constraints and coupled inequality constraints, where all involved functions are not necessarily differentiable, only assumed to be convex. In order to solve this problem, a modified primal-dual continuous-time algorithm is proposed by projections on local feasible sets. With the aid of constructing a proper Lyapunov function candidate, the existence of solutions of the algorithm in the Caratheodory sense and the convergence of the algorithm to an optimal solution for the distributed optimization problem are established. Additionally, a sufficient condition is provided for making the algorithm fully distributed. Finally, the theoretical result is corroborated by a simulation example.

How Will the Presence of Autonomous Vehicles Affect the Equilibrium State of Traffic Networks

Abstract: It is known that connected and autonomous vehicles are capable of maintaining shorter headway and distances when they form platoons of vehicles. Thus, such technologies can potentially increase the road capacities of traffic networks. Consequently, it is envisioned that their deployment will also increase the overall network mobility. In this paper, we examine the validity of this expected impact, assuming that drivers select their routes selfishly, in traffic networks with mixed vehicle autonomy, that is, traffic networks with both regular and autonomous vehicles. We consider a nonatomic routing game on a network with inelastic (fixed) demands for a set of network origin destination (O/D) pairs, and study how replacing a fraction of regular vehicles by autonomous vehicles will affect the mobility of the network. Using well-known U.S. Bureau of Public Roads traffic delay models, we show that the resulting Wardrop equilibrium is not necessarily unique for networks with mixed autonomy. Then, we state the conditions under which the total network delay at equilibrium is guaranteed to not increase as the fraction of autonomous vehicles increases. However, we show that when these conditions do not hold, counterintuitive behaviors may occur—the total network delay can grow as the fraction of autonomous cars increases. In particular, we prove that for networks with a single O/D pair, if the road degrees of capacity asymmetry (i.e., the ratio between the road capacity when all vehicles are regular and the road capacity when all vehicles are autonomous) are homogeneous, the total delay is: 1) unique and 2) a nonincreasing continuous function of the fraction of autonomous vehicles in the network. We show that for heterogeneous degrees of capacity asymmetry, the total delay is not unique, and it can further grow as the fraction of autonomous vehicles increases. We demonstrate that similar behaviors may be observed in networks with multiple O/D pairs. We further bound such performance degradations due to the introduction of autonomy in general homogeneous networks.

Robust Formation Tracking Control for Multiple Quadrotors Subject to Switching Topologies

Abstract: This article focuses on formation control of multiagent systems composed of a team of quadrotors subject to switching topologies. The quadrotor model is underactuated and includes nonlinear dynamics, parameter uncertainties, and external disturbance. A robust control approach is proposed that consists of a position and an attitude controller, both consisting of two subsystems: the nominal controller that is designed to achieve desired tracking for the nominal system, and the disturbance estimating controller that restrains the influence of uncertainties on the real system. Theoretical foundations, detailed simulation studies, and experimental results validate the effectiveness of the proposed methodology.

A Distributed Algorithm For Almost-Nash Equilibria of Average Aggregative Games With Coupling Constraints

Abstract: We consider the framework of average aggregative games, where the cost function of each agent depends on his own strategy and on the average population strategy. We focus on the case in which the agents are coupled not only via their cost functions, but also via a shared constraint coupling their strategies. We propose a distributed algorithm that achieves an $\varepsilon$ -Nash equilibrium by requiring only local communications of the agents, as specified by a sparse communication network. The proof of convergence of the algorithm relies on the auxiliary class of network aggregative games. We apply our theoretical findings to a multimarket Cournot game with transportation costs and maximum market capacity.

Barrier Function Based Collaborative Control of Multiple Robots Under Signal Temporal Logic Tasks

Abstract: Motivated by the recent interest in cyber-physical and autonomous robotic systems, we study the problem of dynamically coupled multiagent systems under a set of signal temporal logic tasks. In particular, the satisfaction of each of these signal temporal logic tasks depends on the behavior of a distinct set of agents. Instead of abstracting the agent dynamics and the temporal logic tasks into a discrete domain and solving the problem therein or using optimization-based methods, we derive collaborative feedback control laws.These control laws are based on a decentralized control barrier function condition that results in discontinuous control laws, as opposed to a centralized condition resembling the single-agent case. The benefits of our approach are inherent robustness properties typically present in feedback control as well as satisfaction guarantees for continuous-time multiagent systems. More specifically, time-varying control barrier functions are used that account for the semantics of the signal temporal logic tasks at hand. For a certain fragment of signal temporal logic tasks, we further propose a systematic way to construct such control barrier functions. Finally, we show the efficacy and robustness of our framework in an experiment, including a group of three omnidirectional robots.

Privacy-Preserving Distributed Online Optimization Over Unbalanced Digraphs via Subgradient Rescaling

Abstract: In this article, we investigate a distributed online constrained optimization problem with differential privacy where the network is modeled by an unbalanced digraph with a row-stochastic adjacency matrix. To address such a problem, a distributed differentially private algorithm without introducing a trusted third-party is proposed to preserve the privacy of the participating nodes. Under mild conditions, we show that the proposed algorithm attains an $O(\log T)$ expected regret bound for strongly convex local cost functions, where $T$ is the time horizon. Moreover, we remove the need for knowing the time horizon $T$ in advance by adopting doubling trick scheme, and derive an $O(\sqrt{T})$ expected regret bound for general convex local cost functions. Our results coincide with the best theoretical regrets that can be achieved in the state-of-the-art algorithms. Finally, simulation results are conducted to validate the efficiency of our proposed algorithm.

A Novel Fault Detection Method Under Weighted Try-Once-Discard Scheduling Over Sensor Networks

Abstract: In this article, a distributed method based on set-membership filtering is developed to solve the fault detection problem over sensor networks, where each sensor consists of multiple sensing units. In order to prevent data collisions and reduce the energy cost, the weighted try-once-discard (WTOD) scheduling, which allows only a sensor unit to send its measurement data at each transmission instant, is considered to govern the disseminated information between the sensor units and a filter. The aim of the presented problem is to propose a novel fault detection method to determine whether there exist faults in discrete time-varying networked systems. To this end, in the presence of the WTOD protocol, two ellipsoids are obtained, which include all possible prediction states and measurement updated states. Then, the sensor fault occurs when there is no intersection between the prediction set and the update set. Furthermore, two recursive optimization methods are scheduled to look for minimal ellipsoids in order to improve fault detection performance. At last, the effectiveness and the applicability of our proposed fault detection method are demonstrated via a practical example.

A Hybrid Event-Triggered Approach to Consensus of Multiagent Systems With Disturbances

Abstract: This article investigates the event-triggered consensus problem for multiagent systems (MAS) with external disturbances. A hybrid event-triggering mechanism (ETM) is proposed, which utilizes time regularization to force the minimum interevent time to be strictly positive, such that Zeno freeness is ensured in the presence of disturbances. Model-based control protocol is used to avoid continuous communication between neighboring agents. With the proposed hybrid ETM, the closed-loop MAS contains both flow dynamics and jumping dynamics, which cannot be described by the existing MAS models. By means of hybrid system framework and auxiliary variables, a hybrid model of the MAS is constructed to describe the flow dynamics and jumping dynamics. Based on this model, the Lypuanov-based consensus analysis and hybrid ETM design are developed, and the finite-gain $\mathcal {L}_2$ stability property is guaranteed. An example of spacecraft formation is provided to show the effectiveness of the proposed approach.

The Outputs Robustness of Boolean Control Networks via Pinning Control

Abstract: The outputs robustness is a property where the outputs of a system are insensitive to disturbances, and the property plays an important role in biological systems or engineering design. This paper presents an approach to analyze the outputs robustness with respect to disturbances (w.r.t. disturbances) for Boolean control networks (BCNs). Based on the wiring digraph of a BCN rather than the state transition digraph, an algorithm is proposed to construct a corresponding permutation digraph and permutation system. Then, we prove that if there exists a pinning controller such that the outputs of a permutation system are robust w.r.t. disturbances, then there must also exist another pinning controller such that the outputs of the corresponding original systems achieve robustness. Furthermore, pinning controllers are designed based on the neighbors of the pinned-nodes rather than all of the nodes. Finally, the proposed method is well demonstrated by a reduced signal transduction network.

Optimal Scheduling of Water Distribution Systems

Abstract: With dynamic electricity pricing, the operation of water distribution systems (WDS) is expected to become more variable. The pumps moving water from reservoirs to tanks and consumers can serve as energy storage alternatives if properly operated. Nevertheless, optimal WDS scheduling is challenged by the hydraulic law, according to which the pressure along a pipe drops proportionally to its squared water flow (WF). The optimal water flow (OWF) task is formulated here as a mixed-integer nonconvex problem incorporating flow and pressure constraints, critical for the operation of fixed-speed pumps, tanks, reservoirs, and pipes. The hydraulic constraints of the OWF problem are subsequently relaxed to second-order cone constraints. To restore feasibility of the original nonconvex constraints, a penalty term is appended to the objective of the relaxed OWF. The modified problem can be solved as a mixed-integer second-order cone program, which is analytically shown to yield WDS-feasible minimizers under certain sufficient conditions. Under these conditions, by suitably weighting the penalty term, the minimizers of the relaxed problem can attain arbitrarily small optimality gaps, thus providing OWF solutions. Numerical tests using real-world demands and prices on benchmark WDS demonstrate the relaxation to be exact even for setups where the sufficient conditions are not met.

Optimal Secure Two-Layer IoT Network Design

Abstract: With the remarkable growth of the Internet and communication technologies over the past few decades, Internet of Things (IoTs) is enabling the ubiquitous connectivity of heterogeneous physical devices with software, sensors, and actuators. IoT networks are naturally two layers with the cloud and cellular networks coexisting with the underlaid device-to-device communications. The connectivity of IoTs plays an important role in information dissemination for mission-critical and civilian applications. However, IoT communication networks are vulnerable to cyber attacks including the denial-of-service and jamming attacks, resulting in link removals in the IoT network. In this paper, we develop a heterogeneous IoT network design framework in which a network designer can add links to provide additional communication paths between two nodes or secure links against attacks by investing resources. By anticipating the strategic cyber attacks, we characterize the optimal design of the secure IoT network by first providing a lower bound on the number of links a secure network requires for a given budget of protected links, and then developing a method to construct networks that satisfy the heterogeneous network design specifications. Therefore, each layer of the designed heterogeneous IoT network is resistant to a predefined level of malicious attacks with minimum resources. Finally, we provide case studies on the Internet of Battlefield Things to corroborate and illustrate our obtained results.

Optimal Control for Heterogeneous Node-Based Information Epidemics Over Social Networks

Abstract: In this article, we investigate the optimal control problems of heterogeneous node-based information epidemics. A node-based susceptible–infected–recovered–susceptible model is introduced to describe the information diffusion processes taking into account heterogeneities in both network structures and individual characters. Aiming at guiding information dissemination processes toward the desired performance, we propose an optimal control framework with respect to two typical scenarios, i.e., impeding the spread of rumors and enhancing the spread of marketing or campaigning information. We prove the existence of the solutions and solve the optimal control problems by the Pontryagin maximum principle and the forward–backward sweep method. Moreover, numerical experiments validate the use of the node-based SIRS model by comparing it with the exact $3^N$ -state Markov chain model. The effectiveness of the proposed control rules is demonstrated on both models. Furthermore, discussion on the influence of the parameters provides insights into the strategies of guiding information diffusion processes.

Globally Convergent Distributed Network Localization Using Locally Measured Bearings

Abstract: This paper studies the problem of bearing-based network localization, which aims to estimate the absolute positions of the nodes in a network by using the inter-node bearings measured in each node's local reference frame and the absolute positions of a small number of nodes called anchors. In the first part of the paper, we propose a continuous-time localization algorithm, which consists of coupled orientation and position estimation procedures. Compared to the existing works, the proposed algorithm has a concise form and guarantees global estimation convergence. In the second part of the paper, we study the discrete-time case which is still an open problem till now. We fill this gap by proposing a discrete-time localization algorithm to globally localize three-dimensional networks using locally measured bearings. The discrete-time algorithm does not require designing sufficiently small step sizes to ensure convergence. Numerical simulation is presented to verify the proposed algorithms.

Estimating the Impact of Cyber-Attack Strategies for Stochastic Networked Control Systems

Abstract: Risk assessment is an inevitable step in implementation of a cyber-defense strategy. An important part of this assessment is to reason about the impact of possible attacks. In this paper, we study the problem of estimating the impact of cyber-attacks in stochastic linear networked control systems. For the stealthiness constraint, we adopt the Kullback–Leibler divergence between attacked and nonattacked residual sequences. Two impact metrics are considered: the probability that some of the critical states leave a safety region and the expected value of the infinity norm of the critical states. For the first metric, we prove that the optimal value of the impact estimation problem can be calculated by solving a set of convex problems. For the second, we derive efficiency to calculate lower and upper bounds. Finally, we show compatibility of our framework with a number of attack strategies proposed in the literature and demonstrate how it can be used for risk assessment in an example.

A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks

Abstract: Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency of information exchange. However, malware and a virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use a susceptible–infected–susceptible epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus resistance can be achieved by a distributed weight adaptation scheme.

False Data Injection and Detection in LQG Systems: A Game Theoretic Approach

Abstract: Cyber-physical systems are vulnerable to false data injection by adversaries who compromise cyber communication links. In this paper, an infinite horizon linear quadratic Gaussian (LQG) system is considered wherein the control inputs transmitted over cyber links are vulnerable to compromise and false data injection by adversaries. The adversarial cyber-attack is driven to minimize the performance of the LQG system, and the controller is equipped with an intrusion detection system that monitors the sequence of internal physical states to detect adversarial input modification. The problem is formulated as a two-player zero-sum game with the false alarm probability as the reward, wherein the attacker aims to achieve a target increase in controller cost while maximizing the false alarm probability, and a detector who wishes to minimize the false alarm probability while remaining consistent. It is shown that in such a game, an $ \epsilon$ -equilibrium exists. The equilibrium attacker strategy is the one that minimizes the Kullback–Leibler distance between legitimate and falsified state dynamics, and the equilibrium detector strategy is the corresponding likelihood-ratio test. Numerical simulations are presented that showcase the equilibrium strategy pair and the intuitive strategies comparisons.