Efficient Sensingin wireless and mobile systems<br />BhaskarKrishnamachari<br />Associate Professor<br />Director, Autonom...
Overview<br />Due to the unique challenges and limitations of wireless and mobile systems, sophisticated algorithms are ne...
Efficient processing of sensor data
Efficient collection of sensor data</li></li></ul><li>Mobile Sensing<br />Limited Energy<br />
Case Study 1:<br />Energy Efficient State Tracking<br />
Automated Activity Tracking<br />
Dynamic Sensor Management<br />EEMSS – Energy Efficient Mobile Sensing System<br />Work with Yi Wang, MuraliAnnavaram at U...
Performance<br />
Wireless Sensor Networks<br />Limited Energy<br />Limited Bandwidth<br />Unreliable Channels<br />
Case Study 2:<br />Indoor Localization<br />Work with KiranYedavalli, SuvilDeoraat USC<br />
Ecolocation<br />WiFi<br />GPS<br />
Basic idea: use the rank ordering of signals to different sensors to determine location. <br />
Ronald Tutor Hall, USC<br />
USC TutornetTestbed<br />
Evaluation<br />Raw location estimates<br />
Evaluation<br />Processed location track<br />
Case Study 3:Robust and High RateData Collection in WSN The Backpressure Collection Protocol (BCP)<br />
How BCP Works<br />Basic Idea: Send packets to neighbor with highest positive gradient.<br />Gradient = Queue Difference –...
BCP routes like water flows<br />
Throughput<br />60% improvement over traditional routing<br />
External Interference<br />
Summary<br />Progress in developing mobile and wireless sensing systems will critically depend on the design of sophistica...
Thanks<br />BhaskarKrishnamachari<br />University of Southern California<br />
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Bhaskar Krishnamachari - Presentation at Emerging Communications Conference & Awards (eComm 2011)

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Bhaskar Krishnamachari - Presentation at Emerging Communications Conference & Awards (eComm 2011)

  1. 1.
  2. 2. Efficient Sensingin wireless and mobile systems<br />BhaskarKrishnamachari<br />Associate Professor<br />Director, Autonomous Networks Research Group<br />University of Southern California<br />
  3. 3. Overview<br />Due to the unique challenges and limitations of wireless and mobile systems, sophisticated algorithms are needed for<br /><ul><li>Efficient activation of sensors
  4. 4. Efficient processing of sensor data
  5. 5. Efficient collection of sensor data</li></li></ul><li>Mobile Sensing<br />Limited Energy<br />
  6. 6. Case Study 1:<br />Energy Efficient State Tracking<br />
  7. 7. Automated Activity Tracking<br />
  8. 8. Dynamic Sensor Management<br />EEMSS – Energy Efficient Mobile Sensing System<br />Work with Yi Wang, MuraliAnnavaram at USC + folks at Nokia Research, CMU<br />
  9. 9. Performance<br />
  10. 10. Wireless Sensor Networks<br />Limited Energy<br />Limited Bandwidth<br />Unreliable Channels<br />
  11. 11. Case Study 2:<br />Indoor Localization<br />Work with KiranYedavalli, SuvilDeoraat USC<br />
  12. 12. Ecolocation<br />WiFi<br />GPS<br />
  13. 13.
  14. 14.
  15. 15. Basic idea: use the rank ordering of signals to different sensors to determine location. <br />
  16. 16. Ronald Tutor Hall, USC<br />
  17. 17. USC TutornetTestbed<br />
  18. 18. Evaluation<br />Raw location estimates<br />
  19. 19. Evaluation<br />Processed location track<br />
  20. 20. Case Study 3:Robust and High RateData Collection in WSN The Backpressure Collection Protocol (BCP)<br />
  21. 21. How BCP Works<br />Basic Idea: Send packets to neighbor with highest positive gradient.<br />Gradient = Queue Difference – Link Quality Metric<br />Work with Scott Moeller, AvinashSridharan at USC<br />
  22. 22. BCP routes like water flows<br />
  23. 23. Throughput<br />60% improvement over traditional routing<br />
  24. 24. External Interference<br />
  25. 25. Summary<br />Progress in developing mobile and wireless sensing systems will critically depend on the design of sophisticated algorithms enabling them to operate efficiently. <br />
  26. 26. Thanks<br />BhaskarKrishnamachari<br />University of Southern California<br />
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