Search Results for « Optimal placement »

Using Drone Cells to optimize Radio Access Networks is an exemplary way to enhance the capabilities of terrestrial Radio Access Networks. Drones fitted with communication and relay modules can act as Drone Cells. The multi Drone Cell placement problem is solved using an adapted Dispersive Flies Optimization alongside with other meta-heuristic algorithms such as Particle Swarm Optimization and Differential Evolution. A home-brewed simulator has been used to test the effectiveness of the different implemented algorithms. Specific environment respective parameter tuning have been explored to better highlight possible advantages of one algorithm over other in any particular environment. Algorithmic diversity has been explored, leading to several modifications and improvements in the implemented models. The results show that using tuned parameters there is a performance uplift in coverage probability when compared to the default meta-heuristic parameters while still remaining within the constraints implied by the problem’s requirements and resource limitation. The paper concludes by offering a study between multiple meta-heuristic approaches and comparison between the methods, investigating the impact of parameter-tuning as well as analyzing the impact of intermittent restart for algorithms persistent diversity.

In this paper, we present a novel approach in water loss research combining two different topics: The optimal placement of pressure sensors to localize leaks in water distribution systems and Serious Gaming—games that are not only entertaining but that are also serving another purpose. The goal was to create a web interface, through which gamers could place sensors in a water distribution system model, in order to improve these sensor positions after they had been evaluated by a suitable algorithm. Two game objectives are to be pursued by the players: reaching a specified net coverage while not using more than a maximum number of sensors. For this purpose, an existing optimal sensor placement algorithm was extended and implemented, together with two hydraulic models taken from literature. The resulting Serious Game was then tested and rated in a case study. The results showed that human players are able to reach solutions that are similar regarding net coverage to those obtained by optimization, within in a short amount of time. Furthermore, it was shown that the implementation of the ideal sensor placement problem as a Serious Game motivates the players to get better and better results, while also providing them with an enjoyable gaming experience.

This study presents a methodology for determining the optimal placement of sensors along the height of buildings to minimize uncertainty in reconstructing structural response at non-instrumented floors. Recent advancements in sensing technology have expanded the application of sensor data in earthquake and structural engineering, including model validation, post-event damage assessment, and structural health monitoring. However, to lower the costs of sensor installation and maintenance—particularly at the regional scale—it is essential to strategically place sensors to maximize the value of the collected data. Because the optimal sensor configuration depends on the specific objectives of an instrumentation project, there is no universal solution to the sensor placement problem. In this study, we focus on identifying sensor locations that allow for accurate interpolation of structural responses at non-instrumented floors with minimal prediction uncertainty. This objective supports the primary goal of the California Strong Motion Instrumentation Program (CSMIP), which is to collect structural response data with the highest possible accuracy and the lowest uncertainty. The proposed method is limited to stationary excitations (e.g., ambient vibrations or distant earthquakes) and to buildings with uniform mass and stiffness distributions along their height. Under these assumptions, we use a Gaussian Process Regression (GPR) model to quantify response prediction uncertainty and minimize the total uncertainty across the building height by placing sensors at the most informative locations. The GPR model is based on a simple shear-flexural beam representation, which effectively approximates the building using very few parameters—parameters that can be estimated from limited building information. The method is verified and validated using both simulated and real data. Finally, we propose a table that can be used by strong motion networks to facilitate more quantitative decision-making regarding sensor placement.

Our current digital landscape relies heavily on existing data centers to store and process information for online applications accessed by millions of users. Any failure of these operations significantly affects productivity as well as significant operations of an organization. Additionally, data centers are high energy consumers. Climate change and the scarcity of energy resources due to political or local constraints emphasize the need for us to deeply analyze the strategic placement of these data centers to understand factors contributing to risks and opportunities in data center placement.