Smart Cities

As of 2014, 54% of the earth’s population resides in urban areas, a percentage expected to reach 66% by 2050. This increase would amount to 2.5B people added to urban populations. At the same time, there are now 28 mega-cities (with 10M people) worldwide, accounting for 22% of the world’s urban population, and projections indicate more than 41 mega-cities by 2030. It stands to reason that the management and sustainability of urban areas has become one of the most critical challenges our societies face today.

Read an Introductory Article about Smart Cities

Recent Plenary Talks about Smart Cities:

“Smart Cities as Cyber-Physical Systems”
ACM/IEEE 6th Intl. Conference on Cyber-Physical Systems
April 2015 [ DOWNLOAD ]

“Smart Cities as Cyber Social Physical Systems”
55th IEEE Conference on Decision and Control (CDC),
December 2016 [ DOWNLOAD ]

There are several projects in our research group addressing issues in Smart Cities which we often view as an instance of a complex stochastic Cyber-Physical System (CPS). Among the multitude of functions a city supports, transportation dominates in terms of resource consumption, strain on the environment, and frustration of its citizens. For example, based on the 2011 Urban Mobility Report, the cost of commuter delays has risen by 260% over the past 25 years and 28% of U.S. primary energy is now used in transportation. Moreover, road congestion is responsible for about 20% of fuel consumption in urban areas and a report by INRIX estimates that the cumulative cost of traffic congestion in the U.S. will reach $2.8 trillion by 2030 – equal roughly to the entire annual tax revenue. Thus, much of our Smart City research has been focusing on transportation.

A Cyber-Physical Infrastructure for the “Smart City”

”Smart Parking”: An Optimal Parking Assignment and Reservation System

Effective Classification and Actionable Obstacle Detection from Roadway Data

A Smart-city Cloud-based Open Platform and Ecosystem (SCOPE)

Dynamic Resource Allocation for Energy-Efficient Urban Mobility

New Frontiers for SimEvents: Advanced Driver Assistance Systems (ADAS) and Automated Sensitivity Analysis

New Driving Models and Controllers for Connected Autonomous Vehicles

Simultaneous Optimization of Vehicle and Powertrain Operation Using Connectivity and Automation

A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments

Data Analytics and Network Optimization

The availability of ever increasing amounts of data from multitudes of sources is rapidly transforming the way we approach control and optimization problems. While model-driven methods remain at the heart of how we approach most problems, there is now an increasingly important data-driven component to be incorporated with existing methods, while new ones are continuously under development.

In the case of Cyber-Physical Systems and Multi-Agent Systems, the models involved are almost always network systems. In such networked settings, distributed optimization plays a crucial role not only to achieve scalability but also avoid the well-known pitfalls of centralized control and optimization schemes.

A large part of ongoing research in the CODES Lab has been focusing on data-driven optimization methods based on the theory of Perturbation Analysis (PA) which provides gradient estimates from directly observed data without requiring stochastic models that are difficult to build. One of the recent breakthroughs in the development of the PA theory is showing that it can be used with virtually arbitrary stochastic hybrid systems and it is characterized by intrinsic robustness properties with respect to unknown characteristics of the stochastic processes involve. This has led to a large class of Infinitesimal Perturbation Analysis (IPA) algorithms and an IPA Calculus of wide applicability.

A related part of our research aims at developing asynchronous event-driven distributed algorithms for network optimization with applications to a variety of Multi-Agent Systems. Many interactive applets and videos from networked robotic team experiments done in our laboratory can be found at CODES Lab.

Examples of projects in our research group which involve data analytics driven by “Big Data” are:

Vulnerability Assessment Tools for Complex Information Networks

A Coordinated Approach to Cyber-Situation Awareness Based on Traffic Anomaly Detection

Effective Classification and Actionable Obstacle Detection from Roadway Data

A Smart-city Cloud-based Open Platform and Ecosystem (SCOPE)

Simultaneous Optimization of Vehicle and Powertrain Operation Using Connectivity and Automation

A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments

Cyber-Physical Systems

The term Cyber-Physical System (CPS) is used to describe dynamic systems which combine components characterized by a physical state (e.g., the location, power level, and temperature of a mobile robot) with components (mostly digital devices empowered by software) characterized by an operational state or mode (e.g., on/off, transmitting/receiving). From a modeling point of view, physical states evolve according to time-driven dynamics commonly described through differential (or difference) equations, while operational states have event-driven dynamics where “events” may be controllable (e.g., a “turn on” command) or uncontrollable (e.g., a random failure). Imparting intelligence to a CPS implies the presence of multiple additional events that correspond to actions such as ““start moving”” for a mobile robot or ““change sampling rate” for a sensor. These physical and operational states generally interact to give rise to a hybrid dynamic system. For example, a sensor with autonomous control capabilities may switch to a data transmitting mode as a result of a particular physical state change (e.g., its residual energy drops below a certain threshold).

Smart Cities are an example of CPS currently attracting considerable attention, as 66% of the world’s population is projected to be concentrated in urban areas by 2050.

There are several projects in our research group which address issues in CPS:

A Cyber-Physical Infrastructure for the “Smart City”

Real-Time Optimization in Complex Stochastic Environments

Dynamic Resource Allocation for Energy-Efficient Urban Mobility

New Driving Models and Controllers for Connected Autonomous Vehicles

Simultaneous Optimization of Vehicle and Powertrain Operation Using Connectivity and Automation

A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments

Discrete Event and Hybrid Systems

The rapid evolution of computer technology has brought about the proliferation of new dynamic systems, mostly “man-made” and highly complex. Examples abound: computer networks, sensor networks and cyber-physical systems, automated manufacturing systems, traffic control systems, integrated command-control-information systems, etc

Historically, scientists and engineers have concentrated on studying and harnessing natural phenomena which are well modeled by the laws of gravity, classical and nonclassical mechanics, electromagnetics, physical chemistry, etc. Based on this fact, a vast body of mathematical tools and techniques has been developed to model, analyze, and control the systems around us. It is fair to say that the study of ordinary and partial differential equations currently provides the main infrastructure for system analysis and control.

On the other hand, much of the technology we have invented and rely on (especially where digital computers are involved) is event-driven: communication networks, manufacturing systems, or the execution of a computer program are common examples. Not only must these systems act as “event coordinators”, but they are also expected to swiftly react to unpredictable events, rapidly adapt to changing conditions, and guarantee their users satisfactory – if not optimal – performance. In short, all activity in these systems is due to asynchronous occurrences of discrete events, some controlled (like hitting a keyboard key) and some not (like a spontaneous equipment failure). This feature lends itself to the term Discrete Event System (DES). When systems combine both time-driven and event-driven behavior, we then deal with Hybrid Systems.

For more information, please see

Introductory Overview on Discrete Event Systems

Introductory Overview of Hybrid Systems from a Discrete Event System prespective

Event-Driven Control and Optimization

 

 

Multi-Agent Systems

The multi-agent system framework consists of a team of autonomous agents cooperating to carry out complex tasks within a given environment that is potentially highly dynamic, hazardous, and even adversarial. In general, these tasks entail exploration of the environment to discover or detect various “points of interest.”

Once detected, these points of interest become “targets” or “data sources” which need to be monitored. If the targets have dynamics and are mobile, then they also need to be tracked by the agents. Thus, the overall objective of the system may be time-varying and combines exploration, data collection, and tracking to define a “mission”, all in the presence of uncertainties in the processes involved and usually with far more targets than agents. This setting typically arises in mobile robotic applications and sensor networks, but it is surprisingly rich and encompasses a number of other, much less obvious, application domains.

The control and coordination of agents, whether they be autonomous robots, or sensor platforms, in dynamic, hazardous, and possibly adversarial environments is highly challenging since it involves multiple objectives and a considerable amount of information exchange with often severe communication limitations (e.g., in a wireless network, the agents must operate with limited energy resources). Experience has shown that, even in relatively simple problems, the use of ad hoc control policies frequently leads to poorly performing systems. This motivates the use of optimization methods to ensure that well-designed, rational policies are developed that can guarantee satisfactory, if not optimal, behavior. Naturally, such optimization problems rapidly get computationally intractable and their solution is rarely amenable to on-line scalable, distributed implementations.

The types of tasks performed by multi-agent systems include consensus, coverage control, and persistent monitoring. The persistent monitoring problem arises when agents must monitor a dynamically changing environment which cannot be fully covered by a stationary team of agents. Thus, persistent monitoring differs from traditional coverage tasks due to the perpetual need to cover a changing environment.

There are several projects in our research group which address issues in multi-agent systems:

Smart Adaptive Reliable Teams for Persistent Surveillance

Real-Time Optimization in Complex Stochastic Environments

Detection and tracking of multiple dynamic targets using cooperating networked agents

New Driving Models and Controllers for Connected Autonomous Vehicles

Simultaneous Optimization of Vehicle and Powertrain Operation Using Connectivity and Automation

A Dynamic Optimization Framework for Connected Automated Vehicles in Urban Environments