In chemical processes, online measurements of all the process variables and parameters required for process control, monitoring and optimization are seldom available. The use of soft sensors or observers is, therefore, highly significant as they can estimate unmeasured state variables from available process measurements. However, for reliable estimation by a soft sensor, the process measurements have to be placed at locations that allow reconstruction of process variables by the soft sensors. This dissertation presents a new technique for computing an optimal measurement structure for state and parameter estimation of stable nonlinear systems. The methodology can compute locations for individual sensors as well as networks of sensors where a trade-off between process information, sensor cost, and information redundancy is taken into account. The novel features of the approach are (1) that the nonlinear behavior that a process can exhibit over its operating region can be taken into account, (2) that the technique is applicable for systems described by lumped or by distributed parameter models, (3) that the technique reduces to already established methods, if the system is linear and only some of the objectives are examined, (4) that the results obtained from the procedure can be easily interpreted, and (5) that the resulting optimization problem can be decomposed, resulting in a significant reduction of the computational effort required for its solution. The other issue addressed in this dissertation is designing soft sensors for a given measurement structure. In case of high-dimensional systems, the application of conventional soft sensor or observer designs may not always be practical due to the high computational requirements or the resulting observers being too sensitive to measurement noise. To address these issues, this dissertation presents reduced-order observer design techniques for state estimation of high-dimensional chemical processes. The motivation behind these approaches is that subspaces, which are close to being unobservable, cannot be correctly reconstructed in a realistic setting due to measurement noise and inaccuracies in the model. The presented approaches make use of this observation and reconstruct the parts of the system where accurate state estimation is possible.

Recently, wireless sensor networks have attracted a lot of attention. Such environments may consist of many inexpensive nodes, each capable of collecting, storing, and processing environmental information, and communicating with base station nodes through wireless links. In this paper, we survey a fundamental problem in wireless sensor networks, the energy consumption problem, which reflects how well a sensor field is deployed. Therefore, a critical aspect of applications with wireless sensor networks is network lifetime. Furthermore, one of the fundamental issues in sensor networks is the coverage problem, which reflects how well a sensor network is monitored or tracked by sensors. We formulate this problem as a decision problem, whose goal is to determine whether every point in the service area of the sensor network is covered by at least k sensors, where k is a given parameter. In this paper, we propose an energy-efficient method based on Genetic Algorithms to deal with the deployment problem of wireless sensor networks such that it provides target-location and surveillance services.

In chemical processes, online measurements of all the process variables and parameters required for process control, monitoring and optimization are seldom available. The use of soft sensors or observers is, therefore, highly significant as they can estimate unmeasured state variables from available process measurements. However, for reliable estimation by a soft sensor, the process measurements have to be placed at locations that allow reconstruction of process variables by the soft sensors. This dissertation presents a new technique for computing an optimal measurement structure for state and parameter estimation of stable nonlinear systems. The methodology can compute locations for individual sensors as well as networks of sensors where a trade-off between process information, sensor cost, and information redundancy is taken into account. The novel features of the approach are (1) that the nonlinear behavior that a process can exhibit over its operating region can be taken into account, (2) that the technique is applicable for systems described by lumped or by distributed parameter models, (3) that the technique reduces to already established methods, if the system is linear and only some of the objectives are examined, (4) that the results obtained from the procedure can be easily interpreted, and (5) that the resulting optimization problem can be decomposed, resulting in a significant reduction of the computational effort required for its solution. The other issue addressed in this dissertation is designing soft sensors for a given measurement structure. In case of high-dimensional systems, the application of conventional soft sensor or observer designs may not always be practical due to the high computational requirements or the resulting observers being too sensitive to measurement noise. To address these issues, this dissertation presents reduced-order observer design techniques for state estimation of high-dimensional chemical processes. The motivation behind these approaches is that subspaces, which are close to being unobservable, cannot be correctly reconstructed in a realistic setting due to measurement noise and inaccuracies in the model. The presented approaches make use of this observation and reconstruct the parts of the system where accurate state estimation is possible.

We study an informative path planning problem where the goal is to minimize the time required to learn a spatial field. Specifically, our goal is to ensure that the mean square error between the learned and actual fields is below a predefined value. We study three versions of the problem. In the placement version, the objective is to minimize the number of measurement locations. In the mobile robot version, we seek to minimize the total time required to visit and collect measurements from the measurement locations. A multi-robot version is studied as well where the objective is to minimize the time required by the last robot to return back to a common starting location called depot. By exploiting the properties of Gaussian Process regression, we present constant-factor approximation algorithms that ensure the required guarantees. In addition to the theoretical results, we also compare the empirical performance using a real-world dataset with other baseline strategies. / M. S. / We solve the problem of measuring a physical phenomenon accurately using a team of robots in minimum time. Examples of such phenomena include the amount of nitrogen present in the soil within a farm and concentration of harmful chemicals in a water body etc. Knowing accurately the extent of such quantities is important for a variety of economic and environmental reasons. For example, knowing the content of various nutrients in the soil within a farm can help the farmers to improve the yield and reduce the application of fertilizers, the concentration of certain chemicals inside a water body may affect the marine life in various ways. In this thesis, we present several algorithms which can help robots to be deployed efficiently to quantify such phenomena accurately. Traditionally, robots had to be teleoperated. The algorithms proposed in this thesis enable robots to work more autonomously.

<p>In this work an investigation is performed in whether the algorithms Simultaneous Perturbation Stochastic Approximation (SPSA) and Virtual Force Algorithm (VFA) are suitable for real-time strategic placement of sensors in a dynamic environment. An evaluation of these algorithms is conducted and compared to Simulated Annealing (SA), which has been used before in similar applications.</p><p>For the tests, a computer based model of the sensors and the environment in which they are used, is implemented. The model handles sensors, moving objects, specifications for the area the sensors are supposed to monitor, and all interaction between components within the model.</p><p>It was the belief of the authors that SPSA and VFA are suited for this kind of problem, and that they have advantages over SA in complex scenarios. The results shows this to be true although SA seems to perform better when it comes to smaller number of sensors to be placed</p>

In this work an investigation is performed in whether the algorithms Simultaneous Perturbation Stochastic Approximation (SPSA) and Virtual Force Algorithm (VFA) are suitable for real-time strategic placement of sensors in a dynamic environment. An evaluation of these algorithms is conducted and compared to Simulated Annealing (SA), which has been used before in similar applications. For the tests, a computer based model of the sensors and the environment in which they are used, is implemented. The model handles sensors, moving objects, specifications for the area the sensors are supposed to monitor, and all interaction between components within the model. It was the belief of the authors that SPSA and VFA are suited for this kind of problem, and that they have advantages over SA in complex scenarios. The results shows this to be true although SA seems to perform better when it comes to smaller number of sensors to be placed

With the spread of COVID-19, occupancy estimation wason the rise to regulatepeople’smovement in public areasto further decrease the spreadof the diseaseand prepare for future pandemics.Much theory existson the problem of sensor placement, but this theory is mainly treated as a mathematical problem and often not applicable to real-life situationsdue to important real-life factors not being considered. Placing sensors is adifficult task,and it is hard knowingwhere to beginplacing themand what type of sensors to use. The purpose of thisthesis was to help with the problem onplacing sensors by investigatingmethods in a simulated environmentto create guidelines for placing sensors in public areasand evaluatethese placements through a formula.The results are compared to previous researchto prove that it is important to cover different types of hotspots within a building,and where these hotspots canbe foundin general buildings.To truly achieve good sensor placement involves several different things but what is most importantis to be able to track the movement of people, with the number of sensors being as low as possiblewhile still having good coverage.The formula proved to have both benefits and drawbacks, such asthe drawback ofnot consideringwhat kind of sensor is being evaluated. The results should be validatedin a more truthful simulation orin real-lifeto be able to be fully dependent on.

Water distribution system (WDS) is very critical to human health and societal welfare. Maintaining the quality of the water so that potable water gets distributed to consumers has always been a challenge in the water industry. Deterioration of water quality can happen either accidentally or deliberately and the widespread geography of the water system makes it even more vulnerable to contamination. In this respect, researchers and utilities have some response action to flush out the contaminants when they are detected. But not all networks have reliable sensors to detect the contamination and lack of guidelines for sensor deployment has made the situation even more serious. Given this context, framework for decision-making in the case of WDN against contamination is a much-needed approach. Understanding the capability of the water system to handle the contamination event could provide ample insight on how to better protect the system and how to handle if the contamination does enter the system. In this regard, this study explores the concept of resilience to define the system performance when a disruption occurs, which in this case is the intrusion of contaminants. Resilience of a system can be viewed from different perspectives, each highlighting different aspect of the system. With this insight, the objective of this research is to characterize the resilience of the water system against contamination for multiple aspects of performance or functionalities and use that concept to further elucidate the decision-making process. Hydraulic and quality simulation to emulate the contamination intrusion in WDN is performed by using EPANET-MATLAB Toolkit which has the needed package for both EPANET and EPANET-MSX. EPANET-MSX is widely used for simulating multiple intrusions in the system. The result from the MATLAB simulation gives the quality at each node which is then used to draw the performance time-series curve. Resilience is then computed for each of the performance metrics using the area under the curve method. This study makes a comparison study for multi-dimensional resilience and describes in detail the need of considering the attributes of resilience which are resistance, loss rate, recovery rate, failure duration, and recovery ability. To perceive the concept of resilience with respect to the failure scenarios, a sensitivity analysis was performed for four failure contexts namely, intrusion time, intrusion duration, intruded contaminated mass, and the number of intrusion nodes. Furthermore, a system measure is defined to aggregate different individual resilience to overcome the challenge of multi-objective decision-making. Application of both integrated and multi-dimensional resilience was conducted for optimal sensor placement in the network to maximize the resilience of the whole system. The goal of this thesis is to introduce the multi-dimensional resilience concept as a tool for decision-making based on multiple aspects of system performance by characterizing the WDS resilience and water quality sensor optimization based on different aspects of system functionality under contaminant intrusion events.

In pneumatic pressure control applications the influence of tubes that connect the valve with the control volume ist mainly neglected. This can lead to stability and robustness issues and limit either control performance or tube length. Modeling and considering tube behavior in controller design procedure allows longer tubes while maintaining the required performance and robustness properties without need for manual tuning. The author\'s previously published Simplified Fluid Transmission Line Model and the proposed model-based controller design enable the specification of a desired pressure trajectory in the control volume while the pressure sensor is mounted directly at the valve. Thus wiring effort is reduced as well as cost and the chance of cable break or sensor disturbance. In order to validate the simulated results the proposed control scheme is implemented on a real-time system and compared to a state-of-the-art pressure regulating valve

<div>The problem of optimally placing sensors can often be formulated as a facility location problem. In the literature of operations research, facility location problems are mathematical optimization problems where one or more facilities must be placed in relation to a given number of demand points or customers. Within the context of sensor placement, for example, this translates to placing wireless communication nodes that connect to a set of users or placing smoke detectors to adequately cover a region for safety assurances. However, while the classical facility location problem has been extensively studied, its direct applicability to and effectiveness for the optimal sensor placement problem can be diminished when real-world uncertainties are considered. In addition, the physics of the underlying systems in optimal sensor placement problems can directly impact the effectiveness of facility location formulations. Extensions to existing location formulations that are tailored for the system of interest are necessary to ensure optimal sensor network design.</div><div><br></div><div>This dissertation focuses on developing and applying problem-specific optimal sensor placement methods under uncertainty in sensor performance. With the classical discrete facility location problems as a basis, our models are formulated as mixed-integer linear and nonlinear programs that, depending on the specific application, can also be in the form of a stochastic program, a robust optimization framework, or require probability distributions for uncertain parameters. We consider optimal placement problems from three different areas, particularly the optimal placement of data concentrators in Smart Grid communications networks, the optimal placement of flame detectors within petrochemical facilities, and the optimal selection of infectious disease detection sites across a nation. For each application, we carefully consider the underlying physics of the system and the uncertainties and then develop extensions of previous sensor placement formulations that effectively handle these qualities. In addition, depending on the degree of nonlinear complexity of the problem, specific relaxations and iterative solution strategies are developed to improve the ability to find tractable solutions. All proposed models are implemented in Pyomo, a Python-based optimization modeling language, and solved with state-of-the-art optimization solvers, including IPOPT, Gurobi, and BARON for nonlinear, mixed-integer, and mixed-integer nonlinear programs, respectively. Numerical results show that our tailored formulations for the problems of interest are effective in handling uncertainties and provide valuable sensor placement design frameworks for their respective industries. Furthermore, our extensions for placement of sensors under probabilistic failure are appropriately general for application in other areas.<br><b></b><i></i><u></u><br></div>