Fabio Pasqualetti

My research interests are in the area of multi-agent, large-scale, and networked systems, such as power grids, water distribution networks, and cooperative robotic systems. My research objectives are to establish scientific foundations for dependable networked systems, and to design networked systems that are robust, resilient and survivable against accidental failures and malignant attacks. My technical approach relies on tools from Control Theory, Graph Theory, Distributed Computing, and Probability. A second line of interest is in mobile robotics, and specifically in the design of coordination and surveillance strategies for teams of autonomous robots. For this part, I borrow tools from Robotics, Computational Geometry, and Combinatorics. Below is a description of recent projects.

Security of Cyber-Physical Systems and Critical Infrastructures

Distributed systems and networks have permeated modern society in many domains including energy production, health care, and telecommunications. The integration of cyber technologies with physical processes increases system efficiency but, at the same time, it introduces vulnerabilities that undermine the reliability of critical infrastructures -- an infamous example is the case of the Maroochy water breach. In contrast with legacy control system, cyber-physical systems are prone to failures and coordinated attacks against their physical infrastructure, as well as cyber attacks against their data management and communication layer. Attackers can now compromise physical processes by exploiting cyber vulnerabilities, and appropriate cyber-physical protections are needed -- see the SQL Slammer worm against the David-Besse nuclear plant, and the Stuxnet worm against the Iranian nuclear facilities. Cyber-physical security is a multidisciplinary field involving Control Theory, Game Theory, Computer Security, and Optimization, and it is attracting interest from several scientific societies.
We contribute to cyber-physical security by (i) proposing a mathematical framework for cyber-physical systems, malicious attackers, and security monitors, (ii) establishing a detailed link between security and the fault detection and identification literature, (iii) characterizing fundamental monitoring limitations from system-theoretic and graph-theoretic perspectives, (iv) designing centralized and distributed attack detection and identification monitors, and (v) providing insight into the design of coordinated attacks amenable to simple implementation. For more details see our works Consensus Computation in Unreliable Networks: A System Theoretic Approach and Attack Detection and Identification in Cyber-Physical Systems.

Controllability and Observability of Complex Networks

Networks accomplish complex behaviors via local interactions of simpler units. The electrical power grid, mass transportation systems, and cellular networks are instances of modern technological networks, while metabolic and brain networks are biological examples. The ability to control and reconfigure complex networks via external controls is fundamental to guarantee a reliable and efficient network functionality. Despite important advances in the theory of control of dynamical systems, several questions regarding the control of complex networks are largely unexplored.
The problem of controlling complex networks consists of the selection of a set of control nodes, and the design of a distributed control law to steer the network to a target state. We study the problem of controlling complex networks from an energy perspective. Inspired by classic controllability notions for dynamical systems, we define the energy to control a network as the worst-case energy of the control input to reach a target state. By combining this controllability notion with graph theory, we characterize tradeoffs between the energy to control a given network and the number of control nodes, and we develop a distributed control strategy with performance guarantees for complex networks. For more details see our work Metrics and Algorithms for Controllability and Observability of Complex Networks.

Robotic Patrolling and Surveillance

Coordinated teams of autonomous agents can efficiently complete tasks requiring repetitive execution, such as monitoring oil spills, detecting forest fires, tracking border changes, and surveilling an environment. Surveillance of an environment requires that the robots persistently travel around the area, and one of the main challenges consists in scheduling the robots trajectories as to optimize a certain performance criteria. We consider two different class of robots, namely mobile robots and PTZ cameras. For the case of mobile robots, we model the environment as a robotic roadmap, and we consider time-based performance criteria, such as the longest time gap between any two visits of the same region (refresh time), and the longest time to propagate a message to all robots (latency). We formulate deterministic and stochastic surveillance problems, and we derive centralized and distributed surveillance strategies with performance guarantees. For the case of PTZ cameras, we study the interplay between cameras and smart evaders, and we develop centralized and distributed algorithms for cameras coordination and detection. For more details see our works On Cooperative Patrolling: Optimal Trajectories, Complexity Analysis and Approximation Algorithms, Cooperative Patrolling via Weighted Tours: Performance Analysis and Distributed Algorithms, Stochastic Surveillance Strategies for Spatial Quickest Detection, and Camera Network Coordination for Intruder Detection.

Networks over Finite-Fields

Sensor and actuator networks have recently attracted interest from different research communities., and classic computation, control, and estimation problems have been reformulated to conform the distributed nature of these networked systems. An important example is the consensus problem, where members of a network aim to agree on a parameter of interest via distributed computation. Consensus algorithms have applicability in many domains, including robotics, estimation, and parallel computation. In this work we focus on the consensus problem for networks of agents with limited memory, computation, and communication capabilities. We assume that agents are capable of storing, processing, and transmitting exclusively elements from a finite and pre-specified alphabet. We model this situation with the formalism of finite fields, where the alphabet consists of a set of integers, and operations are performed according to modular arithmetic. We study linear consensus networks over finite fields where, at each time instant, each agent updates its state as a weighted combination over a finite field of its own value and those received from its neighbors. Besides consensus in capacity and memory constrained networks, our finite-field consensus method is applicable to problems in cooperative control, networked systems, and network coding, such as averaging, load balancing, and pose estimation from relative measurements. Additionally, the use of a finite alphabet for computation and communication makes our consensus method easily implementable and resilient to communication noise. For more details see our work Consensus Networks over Finite Fields.

Distributed Estimation and Control over Networks

State estimation is needed to maintain a system in a secure operating condition. For high-dimensional and spatially distributed systems, centralized methods cannot be used to solve estimation, control, and fault detection problems, and new decentralized techniques need to be developed. One possibility is to deploy monitors in different geographical locations, each one responsible for a subpart of the whole system. Monitors have local information about the system topology and state, and implement local estimation and control schemes together with an information exchange mechanisms to recover the performance of a centralized scheme. We propose distributed and scalable algorithms with finite convergence time for state estimation in large-scale systems. The proposed monitors (i) require local system knowledge and computation, and communication among neighboring control centers, (ii) tolerate system and measurement noise and modeling uncertainties, and (iii) are robust against a fairly general class of attacks. Finally, our analysis reveals elegant connections between state estimation, decentralized control theory, and parallel computation methods. For more details see our works Distributed Estimation via Iterative Projections with Application to Power Network Monitoring and Continuous-Time Distributed Observers with Discrete Communication.