To date, I have created two applications to run on iOS6. The first was for an assignment, and is a relatively simple calculator app. The second is the basis for the end-user control and feedback applications that I mention in my research on Intelligent and Adaptive Building Systems.
The Mulit-Calculator app was my first iOS app. It behaves like any other simple calculator, but has a toggle switch that can put it into “accounting” mode where a previous result can be calculated into the next mathematical operation. Any negative results in this mode appear in red text. The app can also me switched to the “tip” function where the tip for a bill of a user determined amount can be calculated by altering the percentage that the tip will be of the original amount with a slider.
The Room Report app is a decidedly more complicated iOS application. Rooms are created by the user, then Room Reports consisting of the user’s perceived room qualities are created and sorted by date. The application interface, and front-end functionality are relatively simple. However, beneath it all is a persistent SQLite store and Core Data Model. This data storage framework will be the framework for future applications that I will be developing for the purpose of building control and feedback.
Core Data Model Diagram showing aBi-Directional To-Many relationship from rooms to reports.
Energy consumption by buildings has increased to the point that it has overtaken the industrial and transportation sectors. This research seeks to develop Intelligent Adaptive Building Systems as a means of offsetting energy consumption, as well as improving numerous building performance metrics for all stakeholders. To achieve this, several existing technologies are married to form a contextually aware agent based network consisting of many semi-autonomous components. Through this research, two prototypes have been developed. The first served largely as a learning tool, setting the foundation for subsequent versions by necessitating lessons in physical computing and computer programming. A second generation prototype was developed which furthered insight into these lessons, and led to discoveries related to the potential of actuators and sensors, device networking, data mining, and agent based networks. Details about the first two prototypes, their successes, and failures will be presented. With these lessons in hand, it is now necessary to develop a third generation of prototypes to: further the understanding of adaptive facade capabilities and feasibility, intelligent computer behavior development, building performance metrics, and human interfacing methods. By developing several, rather than one 3rd generation prototype, the ability to draw comparisons between technologies and components will be vastly improved. This paper will focus on what form these prototypes should take, and what can be learned from them.
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