During the past 10 years, construction was the leading industry of occupational fatalities when compared to other goods producing industries in the US. This is partially attributed to ineffective safety management strategies, specifically lack of automated construction equipment and worker monitoring. Currently, worker safety performance is measured and recorded manually, assessed subjectively, and the resulting performance information is infrequently shared among selected or all project stakeholders. Accurate and emerging remote sensing technology provides critical spatio-temporal data that has the potential to automate and advance the safety monitoring of construction processes. This doctoral research focuses on pro-active safety utilizing radio-frequency location tracking (Ultra Wideband) and real-time three-dimensional (3D) immersive data visualization technologies. The objective of the research is to create a model that can automatically analyze the spatio-temporal data of the main construction resources (personnel, materials, and equipment), and automatically measure, assess, and visualize worker's safety performance. The research scope is limited to human-equipment interaction in a complex construction site layout where proximities among construction resources are omnipresent. In order to advance the understanding of human-equipment proximity issues, extensive data has been collected in various field trials and from projects with multiple scales. Computational algorithms developed in this research process the data to provide spatio-temporal information that is crucial for construction activity monitoring and analysis. Results indicate that worker's safety performance of selected activities can be automatically and objectively measured using the developed model. The major contribution of this research is the creation of a proximity hazards assessment model to automatically analyze spatio-temporal data of construction resources, and measure, evaluate, and visualize their safety performance. This research will significantly contribute to transform safety measures in construction industry, as it can determine and communicate automatically safe and unsafe conditions to various project participants located on the field or remotely.

With the enhancement of the Radio signal communication systems, Wi-Fi technology become a &ldquo / de facto&rdquo / standard used in Campus areas such as hospitals and universities. Besides being used as a data communication method, Received Signal Strength Indicator (RSSI) is also used as a location tracking method. There are lots of studies enhancing the RSSI based location tracking. In this thesis we tried to generate a test environment as close to a real Wi-Fi network scenario as possible. Our aim is to implement a simple moving client among different wireless local area networks, which is tracked across the internet by a stationary client. We also assumed that there is a Network Address Translation (NAT) at both LAN internet edges.

The work deals with recording and tracking of the objects in a certain area. It also puts mind to web services, mapping applications and modern standards, which seeks to compare and choose the best for this project. Then explore the existing systems and discusses their pros and cons, which specifies the requirements and propose solutions to information system built of selected technologies in order to create a friendly user interface. The proposed solution implements using modern PHP frameworks based on the MVP design pattern, and finally deals with verification of functionality and outline future developments.

Navigation through large and unfamiliar facilities with labyrinths of corridors and rooms is difficult and often results in a person being lost. Additionally, locating a specific utility within a facility is often a tough task. The hypothesis tested in this research is that integrating real-time automated sensing technology and a Building Information Model will provide real time visualization that can assist in localization and navigation of a facility. The scope of this research is facility maintenance management during the Operation and Maintenance (O&M) phase of a facility. The thesis demonstrates how the integration of passive Radio Frequency Identification (RFID) tracking technology and Building Information Modeling (BIM) can assist in facilities maintenance management. The objectives of this research included 1) developing a framework that utilizes the integration of commercially-available RFID and a BIM model; 2) evaluating the framework for real-time resource location tracking within an indoor environment; and 3) developing an algorithm for real-time localization and visualization in a BIM model. A prototype application has been developed that simultaneously connects the RFID readers, a database, and a BIM model. The goal of this system is to have a real-time localization accuracy of 3 meters at 95% confidence. Testing was conducted in laboratory conditions, and the results show that the system error was within the 3 meters goal.

Ubiquitous computing application developers have limited options for a practical activity and location sensing technology that is easy-to-deploy and cost-effective. In this dissertation, I have developed a class of activity monitoring systems called infrastructure mediated sensing (IMS), which provides a whole-house solution for sensing activity and the location of people and objects. Infrastructure mediated sensing leverages existing home infrastructure (e.g, electrical systems, air conditioning systems, etc.) to mediate the transduction of events. In these systems, infrastructure activity is used as a proxy for a human activity involving the infrastructure. A primary goal of this type of system is to reduce economic, aesthetic, installation, and maintenance barriers to adoption by reducing the cost and complexity of deploying and maintaining the activity sensing hardware. I discuss the design, development, and applications of various IMS-based activity and location sensing technologies that leverage the following existing infrastructures: wireless Bluetooth signals, power lines, and central heating, ventilation, and air conditioning (HVAC) systems. In addition, I show how these technologies facilitate automatic and unobtrusive sensing and data collection for researchers or application developers interested in conducting large-scale in-situ location-based studies in the home.