Computer Science Department
Privacy Issues in Smart Grid Data Sharing
Prediction is a critical aspect of any company. For an energy cooperative, predicting future energy demands, energy generation, and problems in energy transmission or production is important. Predicting the future requires data. The goal of this project is to enable this wide data collection and transmission back to the server so that predictions can be made. The accuracy of the predictions depends largely on the quality and amount of data. Most of the data collection are performed through sensors gathering information such as ambient information, power quality, and harmonics. Sensor data collection: Effective sensor data collection is a challenge because of the different types of sensors, different data formats and different sensor locations. Other challenges are the extensibility and modularity of the sensor hardware because the type of data collected can and will likely change in the future. Replacing the whole sensor infrastructure every few years is not desirable. The solution proposed is to design and deploy a new sensor infrastructure that will integrate all the sensors and aggregate all the data collection.
Dr. Crick in Partnership Awarded $6 Million NSF Award
Dr. Christopher Crick, in partnership with other colleagues at OSU and other universities, has been awarded a $6 million NSF Award. The award will be an OSU-led collaboration to develop weather research UAVs.
Integrating disease-correlated ambient information into reliable and privacy-preserving pervasive health monitoring
Many diseases are strongly correlated with and affected by ambient environment, such as temperature and ultraviolet. Some of these diseases like skin cancer are common and can be serious to result in death. Each year more than two million new cases of skin cancer are diagnosed in the U.S. Meanwhile, according to CDC WONDER, Oklahoma has the highest mortality rate for melanoma in the U.S. In order to enable better control and treatment of these diseases, it is desirable to integrate correlated ambient information into pervasive health monitoring systems, for both medical research and health-care services. In this project, the team will design and develop the first pervasive health monitoring system that integrates disease-correlated ambient factors. In addition, it will also surpass all existing ones by providing reliable, efficient and privacy-preserving data collection and transmission. At the completion of this project, it not only will greatly enhance the preventive, proactive and patient-centered treatment to many chronic diseases, but also can facilitate the research on discovering new correlations between ambient factors and disease development.
The Computer Science Department has a three-pronged mission: