Water consumption remains a top concern of San Diego citizens. A 2010 survey found that 80% of respondents were concerned with water supply (1). San Diegans already pay more per gallon for water compared to the national average (2)(3), and are under constant pressure to save water. In March 2016, an average San Diegan consumed 52.51 gallons/day, and the entire city used 3.94 billion gallons that month. As a city that imports 80 to 90% of its potable water (5), those rates will not be indefinitely sustainable. Expanding out to all of California, the state suffers in its fifth year of drought, barely aided by the El Niño weather patterns. The state is still missing two to three years worth of precipitation, and the projected water demand in 2050 shows a deficit in supply (10). These troubling statistics clearly indicate that a cultural shift to a more water efficient community is necessary to secure the future of our water supplies.

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What it does

The key to saving our environment is a fight against complacency. Our application promotes a competitive conservation efforts by informing our user of their current water usage relative to their neighbors and their past interactions. With a breakdown of water usage through pie chart, people can understand the ways in which they consume and better follow a usage pattern acceptable to their own standards. By maintaining a usage history, users are rewarded by their improving habits and become aware of the way they live. Android and iOS Apps. -User inputs water consumption: 1 Manually: record type of use, calculate amount of water uses. 2 IoT Sensors: connect flow rate sensors to report accurate usage -Breakdown of usage per category (ie. showers, washing machine, sprinklers, etc.)
-Visualize in a pie chart -View consumption history -Calculate cost of water per category: Price of water is $4.24/HCF (12). Convert to dollars per gallon: 1HCF=748gallons. Total price = (# of gallons)(1HCF/748gallons)($4.24/HCF) -Compare to other users -Map showing average consumption of users per zip code -Provide recommendations to reduce usage

How we built it

The main challenges which we faced were the integration of an unknown backend provided by Terradata as well as various data visualization libraries. We developed the app on an existing front end to save development time. There are some extraneous methods but we elected to keep a slightly larger package size in favor of reliability and future expandability. We followed an agile development processes; once we had completed ideation, we proceeded to outline general Uses Cases and User Stories. This was followed by a quick sketch of our screen sequence diagrams which outlined our goals and workload for the next two days. We did not emphasize a database schema or use requirements simply because the backend was unknown. Since the requirements were constantly in change we decided to focus on an iterative goal oriented process. New functionality was modularly introduced after unit testing before the final product was modified for consistency. For android, we used open source api’s for graphing visuals. It uses animated pie charts to display relevant data about your water usage in an intuitive way. Also, we implemented a google-maps api into the application with pins representing houses in the surrounding area which allow you to compare your water usage. There is also functionality for the addition of sensors to connect to our application. For example, a raspberry pi water flow sensor can be added to the app.

Challenges we ran into

-Database interaction as suggested by Teradata proved challenging and difficult to interface. Considering that the majority of data is currently static, the overhead associated with generating the REST api may have been more cumbersome than convenient -Re-use of former Android frameworks was less smooth than anticipated. Conflicts in dependencies when building gradle arose with our selected graphing and mapping packages -Our Raspberry Pi was unable to connect to internet, so we couldn’t transmit the data from our sensor to our app. If our device was able to connect properly, we could have added the capability to read the data into our app.

Accomplishments that we're proud of

Beautiful looking android and IOS applications. We accomplished a significant amount of work in the timeframe of the hackathon. Usually, these a polished application takes weeks to complete. Successfully used our 3D printed parts to create a water flow sensor that is interfaced with a raspberry pi.

What we learned

App Development requires much more time than imagined. Functions which are usually considered trivial can be time consuming to deploy under hackathon conditions. General purpose framework proved less helpful than we supposed. In addition, we gained a better understanding of REST given the Teradata API.

What's next for One Drop App

-Predict water & money savings based on suggested usage changes. Ex: person says they will shower 5 min fewer per day; calculate water and money saved per month or year -Trend spotting across different season or months -Provide smart recommendations on most effective changes to reduce consumption -Detect leaks from static monitoring -Set "usage goals" -Connect to new AMI water meters: Network connected water meters; user can read their water meter online, compared to old meters which require a city worker to read number. San Diego currently piloting 10,000 AMI meters in industrial/multifamily housing, 1,000 meters on private Residences in Rancho Bernardo -Offer incentives for efficient water use; city could reward people whose AMI meters report a reduction water usage per person in household

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