CMPSCI 791T Seminar: Mathematical Topics in Sensor Network Data Processing
Professors D. Ganesan, D. Towsley
1-3 credits
Wednesday 12:00-2:00, Room 142 Computer Science Building

Overview

The focus of the course will be on reading and discussing recent work on (mainly) theoretical issues relating to many aspects of data processing in sensor networks. Some of the broad areas that we will be reading papers on as part of this course will be:

  • Information Theory: Slepian-Wolf and other sensor data coding techniques, compression techniques for time and space, base-station assisted compression.
  • Machine Learning: Techniques for learning models of data within the network. Dealing with missing data, outliers, building online models of data, model-driven aggregation.
  • Bayesian Inference: information-driven routing
  • Estimation Theory: Distributed parameter estimation for various detection problems, estimating phenomena characteristics.
  • Data summarization and aggregation: aggregation techniques including solving functions on data in a distributed manner.
  • Statistical Signal Processing: Classifiers and their use in sensor data management
  • Prediction-based Techniques: Adaptive sampling, predictive synchronization
  • Computational Geometry: connectivity, coverage, routing

The course prerequisites includes a course on networking and reasonable statistics and/or probability sophistication. To register for this seminar, you need to fill out a course override form and get one of the instructors' signature.

Seminar Requirements

This course can be taken for either one credit or three credits. For one credit, students are expected to actively participate in class, and to present and lead the discussion of one paper (half of a class). For three credits, students will be asked to write detailed scribe notes on one of the above topics or undertake a research project.

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