Monitoring Bridge Safety with Skyline Algorithms for Wireless Sensor Networks

The proposed research seeks to develop and test Skyline algorithms (i.e., algorithms that allow efficient evaluation of multiple user preferences) to determine the health of bridges (i.e., stress) in the Mahoning County. The design and evaluation of the algorithm will be implemented using data collected from a lightweight and reliable wireless sensor network and the National Bridge Inventory (NBI) database. Possible structural metrics will be studied to predict the reliability and safety of bridges using NBI parameters and recent literature on structural monitoring, in order to save human lives, avoid costly failure, prevent unnecessary reconstructions, and minimize disruptions of traffic. This research will expand the existing Center for Technology and Management Education (CTME) project of collecting bridge data with the design of a state of the art algorithm that can select accurate results from a large pool of data using multiple criteria. This can allow the structural health assessment and prediction of bridges for safety and longevity. The successful completion of the project will follow an experimental approach and based on literature on Skyline (question 5). The approach we are going to follow is: (A) Bibliographic review. We will be studying in depth the related work and analyzing existing or similar ideas that can be useful in defining metrics used by skyline to monitor the health of bridges (i.e., relationship between load and deflection of the structure). (B) Develop a proposal to review the problem of progressive skyline query for <strong>Wireless Sensor Network</strong> (WSN). Study and define of strategies and mechanisms to ensure progressive change in data processing and assembly techniques update skyline. (C) Design and develop the algorithms that implement the solution proposed in B). (D) Experimental study: Elaborate and create the test cases. Collect different characteristics that measure the quality of the solution. Generate a comparative analysis against previous work.