Large scale sensor networks can often be viewed as distributed databases in which information querying is a fundamental operation.
This project aims to research a framework for efficient information discovery in sensor networks, based on a new paradigm called "active query forwarding". The basic principle of this approach is to consider queries as active entities which move efficiently through the network in search of desired information.
A multi-part methodology is employed in the project. Algorithms are designed for query guidance, semantics, adaptation, and optimization; these are then evaluated through both mathematical analysis and computational simulations. Important elements of the active querying framework are further validated on real experimental platforms.
The expected results consist of: (1) a scalable querying framework, (2) its constituent mechanisms, and (3) the related evaluation and validation studies. Together, these provide a fundamental building block for information management in next-generation sensor networks. Utilizing such building blocks efficiently can potentially improve sensor network lifetimes by orders-of-magnitude over existing approaches, without sacrificing information quality. The techniques developed in this project are useful for a wide range of large-scale sensor network applications, from industrial process control to structural health monitoring.
The results of the project are to be disseminated widely and in a timely manner through high quality publications, talks, and interactions with several industrial teams. The project is also closely integrated with the undergraduate and graduate networks curriculum at the University of Southern California, including new courses on sensor and ad-hoc networks and the training of several M.S./Ph.D. students.
