Increasingly, wireless sensor networks are seen as the key to revolutionizing many aspects of science and our lives. Their applications range from environmental monitoring and hazard warning, to surveillance and business asset management, to smart houses and health care automation. Such systems must operate under uncertainty in dynamic physical environments, through unreliable communication, and with severe resource constraints such as battery power. These challenges require new, holistic approaches that bring together a variety of disciplines, such as statistical modeling, signal processing, networking, distributed algorithms, probabilistic reasoning, and databases.
This course is intended for students interested in learning about sensor data processing. The course covers fundamental topics in wireless sensor networks such as platforms, tools, and abstractions; networking; infrastructure services; distributed data storage and processing; etc. The main emphasis will be placed on issues and challenges that arise in collecting, processing, managing, and interpreting correlated, noisy, and incomplete sensor data in an energy-efficient manner.
No textbook is required but one is recommended; there will also be a reading list drawn from recent research literature (see Readings). The course will consist of lectures by the instructor and technical paper presentations and discussions by the participants. The course will include a major project component that involves building sensor-based applications.
This course is open to graduate students and advanced undergraduates. Basic programming skills are required, though no knowledge about any specific language is assumed. Additional background in networking, databases, and statistics is useful but not required; students should be prepared to pick up some basic relevant concepts in such areas when necessary. Non-CS students are welcome; please consult the instructor about necessary background.
Instructor: Jun Yang
Time and Place
10:05am-11:20am on Tuesdays and Thursdays; D243 LSRC
No textbook is required, but the following is recommended: Wireless Sensor Networks: An Information Processing Approach. Feng Zhao and Leonidas Guibas. Morgan Kaufmann, 2004. This book has been placed on reserve at the Vesic Library.
In addition, there will be a reading list drawn from recent research literature. The list will be posted on the course Web site.
Web and Email
Most of the course materials, including the syllabus, lecture notes, reading list, programming FAQs, etc., will be available through the course Web page (http://www.cs.duke.edu/courses/spring07/cps296.1/).
The email address firstname.lastname@example.org reaches everybody in the class as well as the instructor. Only announcements, questions/answers, and comments of general interests should be sent to this address. Specific questions should be directed to the instructor. Please check your emails regularly, as important announcements and information will be sent via email.
Grading is done on an absolute, but adjustable scale. In other words, there is no curve. Anyone earning 90% or more of the total number of points available will receive a grade in the A range; 80% or more guarantees a grade in the B range; 70% or more guarantees a grade in the C range; 60% or more guarantees a grade in the D range. At the discretion of the instructor, the grading scale may slide down (i.e., the grades go higher), but it will not slide up.
Under the Duke Honor Code, you are expected to submit your own work in this course. On many occasions, it is useful to ask others for hints or help, or to search the Web for related resources (e.g., slides from the original authors of a paper you are presenting). Such activities are acceptable, but you must explicitly indicate any assistance you received. Any assistance received that is not given proper citation will be considered a violation of the Honor Code. In any event, you are responsible for understanding and being able to explain on your own all materials that you submit and present. The course staff will pursue aggressively all suspected cases of Honor Code violations, and they will be handled through official University channels.
|Last updated Sun Apr 29 11:21:08 EDT 2007|