A Comprehensive Approach for Data Quality and Provenance in Sensor Networks

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Research Areas: Assured Identity and Privacy

Principal Investigator: Sonia Fahmy

Sensor networks are being increasingly deployed in a large variety of domains, ranging from critical IT infrastructures to computational medicine. Sensor networks allow real-time gathering of large amounts of data that can be mined and analyzed for taking critical actions. As such, sensor networks are a key component of any decision-making infrastructure. A critical issue in this context is the trustworthiness of the data being collected.  Data integrity and quality decide the trustworthiness of data.  Without integrity, any information extracted from the available data cannot be trusted.  Data integrity can be undermined not only by errors introduced by users, measurement devices and applications, but also by malicious subjects who may inject inaccurate data with the goal of deceiving the users of the data.  Therefore, it is critical that data integrity issues,
including how to measure data quality and how to use data provenance for integrity assessment, be investigated for multi-sensor data integration, situation assessment, and numerous other functions. A fundamental tradeoff exists between data quality and the cost to gather and protect this data, e.g., in terms of sensor node energy.
The objective of the proposed research is to design and develop a comprehensive approach to the problem of assessing integrity of continuous data streams in sensor networks, taking into account cost and energy constraints.

Personnel

Other PIs: Elisa Bertino

Students: Salmin Sultana S. M. Iftekharul Alam