Working groups

The Technical Committee on Networks and Communication Systems focuses on research at the interplay between networks, communication and systems control. It is organized in eight working groups:

Control of Network Systems

This working group is interested in the analysis and design of networked systems. In particular, analysis questions focus on control-theoretic properties of network systems, including notions such as controllability and observability, along with collective and emergent properties of network systems, such as consensus and synchronization. Design methods aim at developing distributed algorithms to achieve certain goals for the entire network.  Each system comprising the network uses only local information obtained either by sensing or communication, to determine its role towards the global objective.

This general framework specializes into a variety of applications that bring specific challenges. Teams of robots, such as drones, are a natural domain of application: stable coordinated motion, self-localization, platooning, an optimal deployment are all results of this research. The study of collective motions and flocking phenomena, however, extends further to cover examples such as animal groups, human crowds, and vehicular traffic. Another related topic is distributed estimation in sensor networks. Research in this area will contribute to the development of the Internet-of-Things (see T. Samad, “Control systems and the internet of things,” IEEE Contr. Syst. Mag., 2016). The working group is also interested in the control of dynamical processes that evolve over networks, such as diffusion processes and epidemics (see C. Nowzari, V. M. Preciado, and G. J. Pappas, “Analysis and control of epidemics,” IEEE Contr. Syst. Mag., 2016). Finally, opinion dynamics in social systems have also recently attracted our attention. We believe tools from systems theory will help to unlock the underlying principles of opinion formation and optimize the management of social networks.

Networked Sensing and Sensor Networks

Networked sensing systems arise in a number of surveillance, monitoring and medical applications. A recent trend in this context is on designing (hierarchical) networks of sensor systems for real-time and cost-effective decision making. The main compelling reason is that it is too slow or costly to collect full sensor data on every object of interest, either for training, or during real-time operation. Thus, it is important to develop technologies that identify the right set of information to collect data automatically, based on the most recent collected information. The objective is to make sequential decisions of whether or not to acquire information from a sensor in the network while satisfying budget constraints. Such control problems are currently gaining attention both in both model-based and data-driven settings, drawing upon tools from a number of areas including stochastic control, machine learning and reinforcement learning.

In particular, one can map concepts from control theory into the setting of a supervised prediction theory. The goal is to learn a policy that determines a sequence of sensor measurement actions that simultaneously maximize prediction accuracy while satisfying an average resource budget. This departs from a traditional machine learning setting and introduces new exciting challenges. For instance, features are accompanied by costs (e.g., extraction time in search engines or true monetary values in medical diagnosis) and their amortized sum is constrained at test-time. Also, different search strategies in prediction can have widely varying computational costs (e.g., binary search, linear search, dynamic programming). While the possible number of potential sequences grows combinatorially with the number of sensors, in many practical situations, the number of such paths is limited and can be compactly represented as a directed acyclic graph with each node corresponding to a subset of sensors that have been previously acquired. The global decision policy decomposes to decision functions at every node that either select the next sensor or make a prediction using previously acquired information. Such techniques have been applied to problems in computer vision, medical diagnoses and explosives detection.

Infrastructure Networks

Infrastructure networks, such as roads for vehicle traffic, electricity grids, water or gas pipes, are networks where edges in the graph have a physical counterpart (roads, wires, pipes); flows and densities in the network are governed by physical laws, which must be taken into account in the design and operation of the network. The network structure suggests to devise distributed algorithms, capable of using information from a neighborhood only, to take local control decisions in such a way to ensure a global desired behaviour. For example, the green duration of a traffic light might be adjusted as a function of traffic density in nearby roads only, but aiming at an overall fluidification of the traffic for the entire city. Game theory provides useful tools for the design of such distributed algorithms, as has been successfully shown in the case of smart grids.

Resilience has become a key issue in the design of complex infrastructure networks. While performing well under normal operation, networked systems can exhibit fragility in response to certain disruptions that may lead to system breakdown and cascading failures. Here, resilience refers to the ability of these systems to plan and prepare for, absorb, respond to, and recover from disasters and adapt to new conditions (see Disaster Resilience: a National Imperative. The National Academies Press, 2012). This notion has been popularized and studied by network science and engineering researchers mostly in static settings. A key challenge for our community is to develop a concept that can be effectively used as a guideline for the design of infrastructure networks involving dynamics.

Cybersecurity and Privacy

Emerging networked control systems, such as smart grids, intelligent transportation systems and smart buildings, will make extensive use of off-the-shelf networking and computing to make them “smarter”. However, wide-spread networking combined with unattended operation of a myriad of devices provides numerous potential entry points for malicious entities. Any successful attack on a safety-critical system, such as power grids, may significantly hamper the economy and even lead to the loss of human life. The Stuxnet incident and the recent cyber attack on the Ukrainian power grid provide a clear warning of the potential damage caused by attacking a control system.

From a control theoretical point of view, an attack on a networked control system can be modeled as an unknown input to the system (integrity attacks), as a sudden change of the system model (physically sabotage the system), as packets drop (denial of service attack) or via an undesired disclosure of information (eavesdropping attack). Control system attacks can be characterized according to: 1) the adversary's knowledge of the system model; 2) the adversary's disclosure resources (the knowledge of the real time sensory and control data); 3) the adversary's capability to disrupt the system's operation (see A. Teixeira et al., “Secure control systems: A quantitative risk management approach,” IEEE Contr. Syst. Mag., 2015).

By leveraging the knowledge of the system as well as the attack model, one can develop control theoretic tools to address security and privacy problems arising from those emerging systems. Currently, major research challenges in this field include: 1) performance analysis of the systems when under attack; 2) attack detection and isolation; 3) resilient information fusion and control algorithm design; 4) data privacy.

Learning, Dynamics and Behaviors in Social Systems

New technologies for online interaction and personalized sensing and tracking devices are transforming the way individuals make decisions and gaining a critical role on our societies, ranging from politics (campaigns, elections, social unrests), to consumer choices (adoption of new technologies and products), to user behaviors in critical infrastructure networks (transport, energy). As ever more data are collected and exchanged every day, detailing our behaviors, preferences, and relationships, the last years have witnessed an unprecedented interest from the social and economic sciences for quantitative analysis and mathematical modeling. Of special interest to our CSS community is the recognition that the foundational problem of sociology is the coordination and control of social systems. (see N.E. Friedkin, “The problem of social control and coordination of complex systems in sociology: A look at the community cleavage problem,” IEEE Contr. Syst. Mag., 2015). In fact, a good deal of novel research efforts have been started by the mathematical systems and control engineering communities aimed at the fundamental understanding of different (dynamical) phenomena in social systems.

The focus area of the working group on Learning, Dynamics, and Behaviors in Social Systems is on the development of sound mathematical foundations and computational tools from systems modeling, optimization and control synthesis, game theory, as well as data processing for social systems. Topics of interest to this working group include: empirical and theoretical analysis of opinion dynamics, information flows, communication, influence, games, learning, and cascades in social systems; robustness analysis of social systems modeling; social systems identification and scalable algorithms for inference with social data; design of incentive mechanisms for steering social behaviors towards desired outcomes; architectures for the exchange of information, social interaction, and crowdsourcing.