Course Title:	EE 295a: Wireless Sensor Network Design
Class Information:	Fall 2009 Class time: 1730-1845 T R Class location: Votey 361 Project space: Perkins 303 and Votey 328/328a This course is also being taught this semester at Northern Arizona University.
Instructor Information:	Dr. Jeff Frolik 357 Votey Phone: 802.656.0732 jfrolik@uvm.edu http://www.cems.uvm.edu/~jfrolik/
Office Hours:	I have an open door policy and am around most the time; otherwise email for an appointment
Prerequisite:	EE 174, Graduate Standing in Engineering or CS, or Permission of Instructor.
Course Description:	This course is part of an NSF Course, Curriculum, and Laboratory Improvement project called muse (multi-university systems education) that uses wireless sensor networks (WSN) as a motivational vehicle for students to learn the design of complex engineered systems. WSNs are an ideal example of complex engineered systems since they integrate sensing, computation, communication and control, and their design involves communication theory, analog and radio-frequency circuitry, signal processing, control theory, and the hardware and software design of embedded, networked computing systems. In contrast to traditional, subdiscipline-specific courses, this course will emphasize the interaction amongst subdisciplines. In addition, this course is about helping you develop a layered model for complex engineered systems along with understanding the interactions between layers that determine system performance (e.g., fidelity, delay, and energy efficiency).Such skills can be referred to as systems thinking. For more information, go to the course web site: http://www.uvm.edu/~muse/ This course complements traditional EE communication systems courses (EE 174 here at UVM) in two ways. First, it will provide an overview of the all the building blocks of a wireless network, including antennas, radio frequency circuitry, and networking protocols. It is thus much broader than EE 174, which focuses on the fundamentals of a point-to-point communication link that may be wired or wireless. Second, this course also involves hands-on experiments that use the muse project's CLIO sensor node platform and AWR Visual System Simulator.
Course Objective:	Develop in-depth technical understanding of the multiple subdisciplines required for the design of WSNs. Promote understanding of systems thinking --- the ability to integrate knowledge from the subdisciplines in the engineering of WSNs.
Text:	None Materials will be provided through course website and BlackBoard. You are, however, required to purchase a Texas Instruments eZ430-RF2500 Wireless Development Tool (see here). You will also need to download the AWR Visual System Simulator software package.

Grading:

Participation: 10%
Exam I: 15%
Exam II: 15%
Experiments: 25%
Project Proposal: 10%
Final Project: 25%

Grade Scale:

A [90, 100]
B [80, 90]
C [70, 80]
D [60, 70]
F [0, 60]
breaks within above ranges are used to set +/-

Course Instructional Modules:	Motivation [MOT] (0.5 weeks) Introduction [INT] (0.5 weeks) System Engineering Applied to WSN [SEA] (1 week) Transducers [TDX] (1 week) A/D Conversion [ADC] (1 week) Radio Frequency Hardware [RFH] (2 weeks) The Wireless Communication Channel [WCC] (2 week) Communication Theory as Applied to WSN [CTA] (2 weeks) Sensor Network Architectures [SNA] (2 weeks) Managing the Sensor: Embedded Computing [EMC] (2 weeks) Bringing It All Together [FIN] (1 week)
General:	As noted, all course materials will be provided through course website and/or BlackBoard. Expect the first exam to be given around mid-October and the second at the end of November. At least one weeks notice will be given. Exams will have a comprehensive component. On exams, it is expected that the methodology needed to obtain a solution will be presented; just presenting a correct final answer will not garner full credit. Throughout the semester, the instructor will give students feedback on how they are progressing the course. This course will present much of its content through online videos. It is expected that students will watch these videos prior to coming to class. Class time will be used to elaborate on concepts and on hands-on experimentation. The class will also employ a wiki to develop written content for the course. The wiki for each course module will evolve in three steps. First, the instructor will post questions which the students should ponder while watching the videos. Individual students will be assigned to answer these questions throughout the semester. Second, students can use the wiki to post follow-up questions after watching the videos. These follow-up questions may be answered by the instructor and/or fellow students. Third, upon completing the module, the wiki will be reformatted as a FAQ to complement the videos. Finally, the wiki will be also used to post student generated summary notes for each in class lecture.
Experiments:	A series of short experiments will be conducted by student individuals throughout the semester. These will involve the Texas Instruments ez 430 (required purchase), the muse CLIO board (provided) and/or AWR's Visual System Simulator (download: here, wiki: here). Materials for these experiments will be posted as needed throughout the semester.
Project:	Student teams will explore new concepts or develop new experiments or designs with the ez430, CLIO and/or VSS. The project deliverables consist of a demonstration of either hardware or a simulation, documented by a written summary and a video. A detailed project proposal is expected in early November.
Participation:	Class attendance is expected and will be checked. Furthermore, involvement in wiki development, completion of module surveys and teamwork in projects will be considered in determining the participation grade.
Graduate Students:	As a 200-level course, the class body is a mix of graduate and undergraduate students. Graduate students will receive additional/different exam questions and/or have additional/different Project requirements.
Plagiarism:	Any students found giving and/or receiving assistance on Exams will receive a failing grade for the course. However, students are encouraged to work together and to exchange ideas when working on their experiments and presentations. Students must be sure to reference their work properly, including all web sources. UVM's policy on honesty is clearly defined and can be found at http://www.uvm.edu/~uvmppg/ppg/acad/other/honesty.htm
ADA:	Students with disabilities should contact the instructor as soon as possible regarding necessary accommodations.
ABET Matrix: 0 – no contribution 1 – very low level 5 - very high level	Outcome #1: an ability to apply knowledge of mathmematics, science, and engineering to the analysis of electrical engineering problems; Contribution: 4 Outcome #2: an ability to design and conduct scientific and engineering experiments, as well as to analyze and interpret data; Contribution: 4 Outcome #3: an ability to plan, specify, design, implement, and operate a system, component, or process to meet desired needs; Contribution: 3 Outcome #4: an ability to function on multidisciplinary teams; Contribution: 1 Outcome #5: an ability to identify, formulate, and solve electrical engineering problems; Contribution: 3 Outcome #6: an understanding of professional, legal, and ethical responsibility; Contribution: 1 Outcome #7: an ability to convey technical material through formal written work products which satisfy accepted standards for writing style; Contribution: 3 Outcome #8: an ability to convey technical material through oral presentation and interaction with an audience; Contribution: 3 Outcome #9: broad education and knowledge of contemporary issues necessary to understand the impact of electrical engineering solutions in a global and societal context; Contribution: 3 Outcome #10: a recognition and appreciation of the need for, and ability to engage in life-long learning and critical thinking; Contribution: 2 Outcome #11: an ability to use modern engineering techniques, skills, and tools, including computer-based tools, necessary for analysis and design; Contribution: 3