Alexander Olshevsky (Massachusetts Institute of Technology), " Efficient information aggregation strategies for distributed control and signal processing " (2010)

You are here

Inside Signal Processing Newsletter Home Page

Top Reasons to Join SPS Today!

1. IEEE Signal Processing Magazine
2. Signal Processing Digital Library*
3. Inside Signal Processing Newsletter
4. SPS Resource Center
5. Career advancement & recognition
6. Discounts on conferences and publications
7. Professional networking
8. Communities for students, young professionals, and women
9. Volunteer opportunities
10. Coming soon! PDH/CEU credits
Click here to learn more.

10 years of news and resources for members of the IEEE Signal Processing Society

Alexander Olshevsky (Massachusetts Institute of Technology), " Efficient information aggregation strategies for distributed control and signal processing " (2010)

Alexander Olshevsky (Massachusetts Institute of Technology), " Efficient information aggregation strategies for distributed control and signal processing", Advisor: Prof. John N. Tsitsiklis (2010)

This thesis is concerned with distributed control and coordination of networks consisting of multiple, potentially mobile, agents. This is motivated mainly by the emergence of large scale networks characterized by the lack of centralized access to information and time-varying connectivity. Control and optimization algorithms deployed in such networks should be completely distributed, relying only on local observations and information, and robust against unexpected changes in topology such as link failures. In this thesis, the author describes protocols to solve certain control and signal processing problems in this setting. It is demonstrated that a key challenge for such systems is the problem of computing averages in a decentralized way. Particularly, a number of distributed control and signal processing problems can be solved straightforwardly if solutions to the averaging problem are available. The rest of the thesis is concerned with algorithms for the averaging problem and its generalizations. The author (i) derives the fastest known averaging algorithms in a variety of settings and subject to a variety of communication and storage constraints (ii) proves a lower bound identifying a fundamental barrier for averaging algorithms (iii) proposes a new model for distributed function computation which reflects the constraints facing many large-scale networks, and nearly characterize the general class of functions which can be computed in this model.

For details, please access the full thesis or contact the author.

Table of Contents:

SPS on Twitter

SPS Videos


Signal Processing in Home Assistants

 


Multimedia Forensics


Careers in Signal Processing             

 


Under the Radar