"The emergence of networked embedded systems and sensor/actuator networks has facilitated the development of advanced monitoring and control applications, where a large amount of sensor data is collected and processed in real-time in order to activate the appropriate actuators and achieve the desired control objectives. However, in situations where a fault arises in some of the components (e.g., sensors, actuators, communication links), or an unexpected event occurs in the environment, this may lead to a serious degradation in performance or, even worse, to an overall system failure. There is a need to develop a systematic framework to enhance the reliability, fault-tolerance and sustainability of complex distributed dynamical systems through the use of fault-adaptive monitoring and control methods. The work proposed here will contribute to the development of such a framework with emphasis on applications related to critical infrastructure systems (e.g., power, water, telecommunications and transportation systems). It will provide an innovative approach based on the use of networked intelligent agent systems, where the state of the infrastructure is monitored and controlled by a network of sensors and actuators with cooperating agents for fault diagnosis and fault tolerant control. A hierarchical fault diagnosis architecture will be developed, with neighbouring fault diagnosis agents cooperating at a local level, while transmitting their information, as needed, to a regional monitoring agent, responsible for integrating in real-time local information into a large-scale “picture” of the health of the infrastructure. A key motivation is to exploit spatial and temporal correlations between measured variables using learning methods, and to develop the tools and design methodologies that will prevent relatively “small” faults or unexpected events from causing significant disruption or complete system failures in complex distributed dynamical systems."