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In this paper we address the problem of deciding whether a water source should be used by a water utility such that the production cost as well as the penalty cost due to water shortages is minimized while certain constraints are satisfied. The problem is modeled using composition of multiple open hybrid automata while the decision logic depends on certain parameters that need to be optimized. Subsequently, infinitesimal perturbation analysis (IPA) is used to optimize these parameters. The proposed approach is non-parametric in the sense that it does not depend on any assumptions on the stochastic processes that drive the system dynamics and it can be used online to continuously adjust the control parameters even when the input processes are not stationary. " 47 265 270

The dispersion of contaminants from sources (events) inside a building can compromise the indoor air quality and influence the occupants' comfort, health, productivity and safety. Such events could be the result of an accident, faulty equipment or a planned attack. Under these safety-critical conditions, immediate event detection should be guaranteed and the proper actions should be taken to ensure the safety of the people. In this paper, we consider an event as a fault in the process that disturbs the normal system operation. This places the problem of contaminant event monitoring in the fault diagnosis framework of detection and isolation. A main contribution of this work is the development of the state-space method, based on multi-zone building models, that enables the use of advanced fault diagnosis tools for contaminant event monitoring. Specifically, in this paper, we develop estimator schemes with adaptive thresholds for the detection and isolation of a single contaminant source under conditions of noise and modeling uncertainty. We demonstrate our proposed formulation using a 2-zone illustration example and a more realistic 14-zone building setting.

This work describes a model-based approach for contamination event detection in water distribution systems using chlorine mea- surements. The proposed method considers the known chlorine input injection signals, and uses multiple Monte-Carlo simulations which run in parallel to the real system, in order to compute at each time step, bounds of the expected chlorine concentration at the different chlorine sensing locations. The sensor measurements are then compared with the estimated bounds and according to a certain event logic, an event alarm flag is raised when these bounds are exceeded. The methodology is applied on a realistic benchmark network, taking into account uncertainties in the hydraulic dynamics.

Wireless sensor networks (WSNs) is a relatively new technology that has been proposed for several applications including wide area monitoring. Such applications may include stationary or mobile sensor platforms or they may include several stationary and some mobile-robotic sensor nodes that can move in the area in order to achieve certain objectives, e.g., monitor areas that are not adequately covered or assist in the transfer of data to prevent the energy depletion of certain critical nodes. Such networks that consist of both stationary and mobile nodes are referred to as mixed WSNs. This paper presents the development of an experimental testbed for mixed WSNs consisting of stationary and mobile sensor nodes that collaborate to improve the sensing coverage and event detection of the network in a given deployment area. The paper describes the hardware and infrastructure of the testbed as well as a case study for coverage control that was investigated using the testbed. We point out that the developed testbed can be used for the evaluation and validation of different algorithms for coverage control that involve collaboration between stationary and mobile sensors to improve the WSN's monitoring capabilities. In addition, it can also be used to investigate other objectives as well as other concepts (e.g., network control).

This paper presents a design methodology and some analytical results for distributed sensor fault detection and isolation (SFDI) of a class of nonlinear uncertain systems. The proposed architecture is based on the design of local SFDI modules, which monitor the healthy operation of a set of sensors and aim to detect the faults in this set, using a dedicated nonlinear observer scheme. The multiple sensor fault isolation procedure is further enhanced by deriving a combinatorial decision logic that processes information from local SFDI modules. The performance of the proposed diagnostic scheme is analyzed in terms of its robustness with respect to the modeling uncertainties and conditions for ensuring fault detectability and isolability.

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Motivated by the problem of Quality of Service (QoS) provisioning, this paper investigates a system with two classes of traffic and addresses the problem of dynamically controlling the buffer threshold used with the threshold policy, such that an objective function is optimized. For the analysis, a Stochastic Fluid Model (SFM) is used to derive unbiased sensitivity estimators of various metrics (workload, loss, throughput and packet expiration) with respect to the buffer threshold. These estimators are computed using information obtained from the sample path of the ''real'' discrete event system. Thus, one can use these estimators together with stochastic approximation techniques in order to maintain the system at a near-optimum point despite any changes in the incoming traffic or the transmission capacity of the network.