Storing and Visualizing Sensor Networks Data in Public Clouds

The objective of this sensor data project is to store sensor data in a public cloud and to convert raw sensor data into visual formats that can be easily understood by people. The eventual goal is to make the sensor networks friendlier to users and to create a flexible framework to meet the requirements of different applications.

This project is part of the “Sensor Cloud Integration” research in the Distributed Systems Laboratory, University of Washington Bothell. The “Sensor Cloud Integration” research targets frost protection as an example application. Temperature prediction is the main concern of farmers for protecting their crops from frost damage.

In this project, I have designed and implemented the Sensor Cloud Gateway. The existing Senor Server is a tool that gathers real time sensor data. Then, Sensor Cloud Gateway forwards that data to cloud databases. The input to the gateway is sensor data in text form. So, it can easily be adapted to handle many different sensor types. The output is forwarded to SQL Azure cloud database.

Sensor Cloud Gateway Architecture
Figure 1: Sensor Cloud Gateway Architecture

The Sensor Cloud Gateway utilizes the Sensor Server and Connector sensor frameworks, developed by the Distributed Systems Laboratory. Figure 1 shows the general design of Sensor Cloud Gateway architecture.

Figure 2 shows a report example that has data for hourly average temperature between two dates (3/15/2012 to 4/01/2012). The graph on the right shows average temperatures over the time range. The gadgets on the left show the overall minimum for each temperature sensor. Visualizations like this allow important features in the data to be understood much more readily than from the raw data.

Sensor Temperature Report
Figure 2: Sensor Temperature Report

During this final project in the MSCSS program, I have researched and used a lot of new technologies and tools. The current Sensor Cloud Gateway demonstrated that we could take data from our sensors, store them in the cloud, and create visualizations of that data using a variety of gadgets and graphs, which make analyzing the data much easier than the raw form.

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Project Info

Aysun Simitci

Aysun Simitci

Faculty Advisor
Munehiro Fukuda