| 1. |
- Chatterjee, Sarthak, et al.
(author)
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Discrete-Time Fractional-Order Dynamical Networks Minimum-Energy State Estimation
- 2023
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In: IEEE Transactions on Control of Network Systems. - : IEEE. - 2325-5870. ; 10:1, s. 226-237
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Journal article (peer-reviewed)abstract
- Fractional-order dynamical networks are increasingly being used to model and describe processes demonstrating long-term memory or complex interlaced dependencies among the spatial and temporal components of a wide variety of dynamical networks. Notable examples include networked control systems or neurophysiological networks which are created using electroencephalographic (EEG) or blood-oxygen-level-dependent data. As a result, the estimation of the states of fractional-order dynamical networks poses an important problem. To this effect, this article addresses the problem of minimum-energy state estimation for discrete-time fractional-order dynamical networks, where the state and output equations are affected by an additive noise that is considered to be deterministic, bounded, and unknown. Specifically, we derive the corresponding estimator and show that the resulting estimation error is exponentially input-to-state stable with respect to the disturbances and to a signal that is decreasing with the increase of the accuracy of the adopted approximation model. An illustrative example shows the effectiveness of the proposed method on real-world neurophysiological networks. Our results may significantly contribute to the development of novel neurotechnologies, particularly in the development of state estimation paradigms for neural signals such as EEG, which are often noisy signals known to be affected by artifacts not having any particular stochastic characterization.
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| 2. |
- Chen, Xiaomeng, et al.
(author)
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A Differentially Private Method for Distributed Optimization in Directed Networks via State Decomposition
- 2023
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In: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; 10:4, s. 2165-2177
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Journal article (peer-reviewed)abstract
- In this article, we study the problem of consensus-based distributed optimization, where a network of agents, abstracted as a directed graph, aims to minimize the sum of all agents' cost functions collaboratively. In the existing distributed optimization approaches (Push-Pull/AB) for directed graphs, all the agents exchange their states with neighbors to achieve the optimal solution with a constant step size, which may lead to the disclosure of sensitive and private information. For privacy preservation, we propose a novel state-decomposition-based gradient tracking approach (SD-Push-Pull) for distributed optimization over directed networks that preserves differential privacy, which is a strong notion that protects agents' privacy against an adversary with arbitrary auxiliary information. The main idea of the proposed approach is to decompose the gradient state of each agent into two substates. Only one substate is exchanged by the agent with its neighbors over time, and the other one is not shared. That is to say, only one substate is visible to an adversary, protecting the sensitive information from being leaked. It is proved that under certain decomposition principles, a bound for the suboptimality of the proposed algorithm can be derived, and the differential privacy is achieved simultaneously. Moreover, the tradeoff between differential privacy and the optimization accuracy is also characterized. Finally, a numerical simulation is provided to illustrate the effectiveness of the proposed approach.
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| 3. |
- Milosevic, Jezdimir, et al.
(author)
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Strategic Monitoring of Networked Systems with Heterogeneous Security Levels
- 2023
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In: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; , s. 1-12
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Journal article (peer-reviewed)abstract
- We consider a strategic network monitoring problem involving the operator of a networked system and an attacker. The operator aims to randomize the placement of multiple protected sensors to monitor and protect components that are vulnerable to attacks. We account for the heterogeneity in the components' security levels and formulate a large-scale maximin optimization problem. After analyzing its structure, we propose a three-step approach to approximately solve the problem. First, we solve a generalized covering set problem and run a combinatorial algorithm to compute an approximate solution. Then, we compute approximation bounds by solving a nonlinear set packing problem. To evaluate our solution approach, we implement two classical solution methods based on column generation and multiplicative weights updates, and test them on real-world water distribution and power systems. Our numerical analysis shows that our solution method outperforms the classical methods on large-scale networks, as it efficiently generates solutions that achieve a close to optimal performance and that are simple to implement in practice.
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| 4. |
- Pare, Philip E., et al.
(author)
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Multilayer SIS Model With an Infrastructure Network
- 2023
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In: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; 10:1, s. 295-307
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Journal article (peer-reviewed)abstract
- In this article, we develop a layered networked spread model for a susceptible-infected-susceptible pathogen-borne disease spreading over a human contact network and an infrastructure network, and refer to it as a layered networked susceptible-infected-water-susceptible model (SIWS). The "W" in SIWS represents any infrastructure network contamination, not necessarily restricted to a water distribution network. We identify sufficient conditions for the existence, uniqueness, and stability of various equilibria of the aforementioned model. Further, we study an observability problem, where, assuming that the measurements of the pathogen levels in the infrastructure network are available, we provide a necessary and sufficient condition for estimation of the sickness levels of the nodes in the human contact network. Our results are illustrated through an in-depth set of simulations.
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| 5. |
- Restrepo, Esteban, et al.
(author)
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Simultaneous Topology Identification and Synchronization of Directed Dynamical Networks
- 2023
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In: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; , s. 1-11
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Journal article (peer-reviewed)abstract
- We propose an approach for simultaneous topology identification and synchronization of a complex dynamical network with directed interconnections that relies on the edge-agreement framework and on adaptive-control approaches by design of an auxiliary synchronizing network. Our method guarantees the identification of the unknown directed topology without the need for verifying the Linear Independence Conditions normally required by previous works in the literature. Furthermore, it also guarantees that the complex network reaches synchronization as determined by the internal dynamics of the system. Under our identification algorithm we provide strong stability results for the estimation errors in the form of uniform semiglobal practical asymptotic stability of the estimation errors. Finally, we demonstrate the effectiveness of our approach with a numerical example.
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| 6. |
- Rikos, Apostolos, et al.
(author)
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Distributed Event-Triggered Algorithms for Finite-Time Privacy-Preserving Quantized Average Consensus
- 2023
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In: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; 10:1, s. 38-50
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Journal article (peer-reviewed)abstract
- In this paper, we consider the problem of privacy preservation in the average consensus problem when communication among nodes is quantized. More specifically, we consider a setting where nodes in the network can be curious, while certain nodes in the network want to ensure that their initial states cannot be inferred exactly by these curious nodes. Curious nodes are not malicious, i.e., they try to identify the initial states of other nodes based on the data they receive during their operation (and some of them might even collude) but do not interfere in the computation in any other way. Each node in the network (including curious nodes) can opt to execute a privacy-preserving algorithm (so as not to reveal the initial state it contributes to the average calculation) or its underlying (plain) average consensus algorithm (if privacy is not a concern). We propose two privacy-preserving event-triggered quantized average consensus algorithms. Under certain topological conditions, both proposed algorithms allow the nodes who adopt privacy-preserving protocols to preserve their privacy and at the same time to obtain, after a finite number of steps, the exact average of the initial states while processing and transmitting quantized information. We also present illustrative examples and comparisons of our algorithms against other algorithms in the existing literature, and discuss a motivating application in which smart meters in a smart grid collect real-time demands in a privacy-preserving manner.
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| 7. |
- Sou, Kin Cheong, et al.
(author)
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Resilient Scheduling of Control Software Updates in Radial Power Distribution Systems
- 2023
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In: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; , s. 1-12
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Journal article (peer-reviewed)abstract
- In response to newly found security vulnerabilities, or as part of a moving target defense, a fast and safe control software update scheme for networked control systems is highly desirable. We here develop such a scheme for intelligent electronic devices (IEDs) in power distribution systems, which is a solution to the so-called software update rollout problem. This problem seeks to minimize the makespan of the software rollout, while guaranteeing safety in voltage and current at all buses and lines despite possible worst-case update failure where malfunctioning IEDs may inject harmful amounts of power into the system. Based on the nonlinear DistFlow equations, we derive linear relations relating software update decisions to the worst-case voltages and currents, leading to a decision model both tractable and more accurate than previous models based on the popular linearized DistFlow equations. Under reasonable protection assumptions, the rollout problem can be formulated as a vector bin packing problem and instances can be built and solved using scalable computations. Using realistic benchmarks including one with 10,476 buses, we demonstrate that the proposed method can generate safe and effective rollout schedules in real-time.
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| 8. |
- Yi, Xinlei, et al.
(author)
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Sublinear and Linear Convergence of Modified ADMM for Distributed Nonconvex Optimization
- 2023
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In: IEEE Transactions on Control of Network Systems. - : Institute of Electrical and Electronics Engineers (IEEE). - 2325-5870. ; 10:1, s. 75-86
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Journal article (peer-reviewed)abstract
- In this article, we consider distributed nonconvex optimization over an undirected connected network. Each agent can only access to its own local nonconvex cost function and all agents collaborate to minimize the sum of these functions by using local information exchange. We first propose a modified alternating direction method of multipliers (ADMM) algorithm. We show that the proposed algorithm converges to a stationary point with the sublinear rate O(1/T) if each local cost function is smooth and the algorithm parameters are chosen appropriately. We also show that the proposed algorithm linearly converges to a global optimum under an additional condition that the global cost function satisfies the Polyak-Łojasiewicz condition, which is weaker than the commonly used conditions for showing linear convergence rates including strong convexity. We then propose a distributed linearized ADMM (L-ADMM) algorithm, derived from the modified ADMM algorithm, by linearizing the local cost function at each iteration. We show that the L-ADMM algorithm has the same convergence properties as the modified ADMM algorithm under the same conditions. Numerical simulations are included to verify the correctness and efficiency of the proposed algorithms.
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| 9. |
- Zino, Lorenzo, et al.
(author)
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Fast Spread in Controlled Evolutionary Dynamics
- 2023
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In: IEEE Transactions on Control of Network Systems. - 2325-5870. ; 10:3, s. 1555-1567
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Journal article (peer-reviewed)abstract
- We study a controlled evolutionary dynamics that models the spread of a novel state in a network where the exogenous control aims to quickly spread the novel state. We estimate the performance of the system by analytically establishing upper and lower bounds on the expected time needed for the novel state to replace the original one. Such bounds are expressed as functions of the control policy adopted and of the network structure, and establish fundamental limitations on the system's performance. Leveraging these results, we classify network structures depending on the possibility of achieving a fast spread of the novel state (i.e., complete replacement in a time growing logarithmically with the network size) using simple open-loop control policies. Finally, we propose a feedback control policy that using little knowledge of the network and of the system's evolution at a macroscopic level, allows for a substantial speed up of the spreading process, guaranteeing fast spread on topologies where simple open-loop control policies are not sufficient. Examples and simulations corroborate our findings.
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