Bridges Structural Health Monitoring and Deterioration Detection Synthesis of Knowledge and Technology

INE/AUTC 10.0

A Prototype Toolkit For Evaluating Indoor Environmental Quality In Commercial Buildings

Measurement of building environmental parameters is often complex, expensive, and not easily proceduralized in a manner that covers all commercial buildings. Evaluating building indoor environmental quality performance is therefore not standard practice. This project developed a prototype toolkit that addressed existing barriers to widespread indoor environmental quality performance evaluation. A toolkit with both hardware and software elements was designed for practitioners around the indoor environmental quality requirements of the American Society of Heating, Refrigeration and Air Conditioning Engineers / Chartered Institution of Building Services / United States Green Building Council Performance Measurement Protocols. This unique toolkit was built on a wireless mesh network with a web-based data collection, analysis, and reporting application. The toolkit provided a fast, robust deployment of sensors, real-time data analysis, Performance Measurement Protocol-based analysis methods and a scorecard and report generation tools. A web-enabled Geographic Information System-based metadata collection system also reduced field-study deployment time. The toolkit was evaluated through three case studies, which were discussed in this report

A new wireless underground network system for continuous monitoring of soil water contents

A new stand-alone wireless embedded network system has been developed recently for continuous monitoring of soil water contents at multiple depths. This paper presents information on the technical aspects of the system, including the applied sensor technology, the wireless communication protocols, the gateway station for data collection, and data transfer to an end user Web page for disseminating results to targeted audiences. Results from the first test of the network system are presented and discussed, including lessons learned so far and actions to be undertaken in the near future to improve and enhance the operability of this innovative measurement approac

Online Learning Algorithm for Time Series Forecasting Suitable for Low Cost Wireless Sensor Networks Nodes

Time series forecasting is an important predictive methodology which can be applied to a wide range of problems. Particularly, forecasting the indoor temperature permits an improved utilization of the HVAC (Heating, Ventilating and Air Conditioning) systems in a home and thus a better energy efficiency. With such purpose the paper describes how to implement an Artificial Neural Network (ANN) algorithm in a low cost system-on-chip to develop an autonomous intelligent wireless sensor network. The present paper uses a Wireless Sensor Networks (WSN) to monitor and forecast the indoor temperature in a smart home, based on low resources and cost microcontroller technology as the 8051MCU. An on-line learning approach, based on Back-Propagation (BP) algorithm for ANNs, has been developed for real-time time series learning. It performs the model training with every new data that arrive to the system, without saving enormous quantities of data to create a historical database as usual, i.e., without previous knowledge. Consequently to validate the approach a simulation study through a Bayesian baseline model have been tested in order to compare with a database of a real application aiming to see the performance and accuracy. The core of the paper is a new algorithm, based on the BP one, which has been described in detail, and the challenge was how to implement a computational demanding algorithm in a simple architecture with very few hardware resources.Comment: 28 pages, Published 21 April 2015 at MDPI's journal "Sensors

Data driven wireless network design : a multi-level modeling approach

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