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Optimal real-time sampling rate assignment for wireless sensor networks

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Published:01 May 2006Publication History
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Abstract

How to allocate computing and communication resources in a way that maximizes the effectiveness of control and signal processing, has been an important area of research. The characteristic of a multi-hop Real-Time Wireless Sensor Network raises new challenges. First, the constraints are more complicated and a new solution method is needed. Second, a distributed solution is needed to achieve scalability. This article presents solutions to both of the new challenges. The first solution to the optimal rate allocation is a centralized solution that can handle the more general form of constraints as compared with prior research. The second solution is a distributed version for large sensor networks using a pricing scheme. It is capable of incremental adjustment when utility functions change. This article also presents a new sensor device/network backbone architecture---Real-time Independent CHannels (RICH), which can easily realize multi-hop real-time wireless sensor networking.

References

  1. Akkaya, K. and Younis, M. 2005. A survey of routing protocols in wireless sensor networks. Elsevier Ad Hoc Network Journal 3, 3.]]Google ScholarGoogle Scholar
  2. Bertsekas, D. 1995. Nonlinear Programming. Athena Scientific, Belmont, MA.]]Google ScholarGoogle Scholar
  3. Bertsekas, D. and Tsitsiklis, J. 1989. Parallel and the Distributed Computation. Prentice Hall.]] Google ScholarGoogle Scholar
  4. Bolot, J.-C., Turletti, T., and Wakeman, I. 1994. Scalable feedback control for multicast video distribution in the internet. In SIGCOMM. 58--67.]] Google ScholarGoogle Scholar
  5. Braginsky, D. and Estrin, D. 2002. Rumor routing algorithm for sensor networks. In International Conference on the Distributed Computing Systems.]]Google ScholarGoogle Scholar
  6. Buttazzo, G. C. 1997. Hard Real-Time Computing Systems: Predictable Scheduling Algorithms and Applications. Kluwer Academic Publishers.]] Google ScholarGoogle Scholar
  7. Caccamo, M., Zhang, L., Sha, L., and Buttazzo, G. 2002. An implicit prioritized access protocol for wireless sensor networks. In Proceedings of IEEE Real-Time Systems Symposium'02.]] Google ScholarGoogle Scholar
  8. DRCL. 2004. Drcl j-sim {online}. Available at: http://www.j-sim.org.]]Google ScholarGoogle Scholar
  9. Exit Consulting. 2004. Gps synchronization clock---model 200. Available at: http://www.gpsclock.com/specs.html.]]Google ScholarGoogle Scholar
  10. Getting, I. A. 1993. Perspective/navigation-the global positioning system. Spectrum, IEEE 30, 36--38, 43--47.]]Google ScholarGoogle Scholar
  11. Ghosh, S., Rajkumar, R., Hansen, J., and Lehoczky, J. 2003. Scalable resource allocation for multi-processor qos optimization. In Proceedings of the 23rd IEEE International Conference on the Distributed Computing Systems (ICDCS 2003). Providence, RI.]] Google ScholarGoogle Scholar
  12. Giannecchini, S., Caccamo, M., and Shih, C. 2004. Collaborative resource allocation in wireless sensor networks. In IEEE Euromicro Conference on Real-Time Systems. Catania, Italy.]] Google ScholarGoogle Scholar
  13. He, T., Stankovic, J. A., Lu, C., and Abdelzaher, T. 2003. Speed: A stateless protocol for real-time communication in sensor networks. In International Conference on the Distributed Computing Systems (ICDCS 2003). Providence, RI.]] Google ScholarGoogle Scholar
  14. Heinzelman, W., Kulik, J., and Balakrishnan, H. 1999. Adaptive protocols for information dissemination in wireless sensor networks. In 5th ACM/IEEE Mobicom Conference.]] Google ScholarGoogle Scholar
  15. Karp, B. and Kung, H. 2000. Greedy perimeter stateless routing for wireless networks. In Sixth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom 2000). Boston, MA.]] Google ScholarGoogle Scholar
  16. Kelly, F. 1997. Charging and rate control for elastic traffic. European Tran. on Telecommunications 8.]]Google ScholarGoogle Scholar
  17. Kelly, F., Maulloo, A., and Tan, D. 1998. Rate control in communication networks: shadow prices, proportional fairness and stability. J. Oper. Res. Soc. 49, 237--252.]]Google ScholarGoogle Scholar
  18. Kurose, J. F. and Simha, R. 1989. A microeconomic approach to optimal resource allocation in the distributed computer systems. IEEE Trans. Comput. 38, 5, 705--717.]] Google ScholarGoogle Scholar
  19. Lee, C., Lehoczky, J., Siewiorek, D., Rajkumar, R., and Hansen, J. 1999. A scalable solution to the multi-resource qos problem. In Proceedings of the IEEE Real-Time Systems Symposium.]] Google ScholarGoogle Scholar
  20. Low, S. H. and Lapsley, D. E. 1999. Optimization flow control, i: Basic algorithm and convergence. IEEE/ACM Tran. Netw. 7, 6 (Dec.), 861--875.]] Google ScholarGoogle Scholar
  21. Luenberger, D. 1984. Linear and Nonlinear Programming. Addison-Wesley, Reading, Massachusetts.]]Google ScholarGoogle Scholar
  22. Muqattash, A. and Krunz, M. 2003. Cdma-based mac protocol for wireless ad hoc networks. In Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc 2003). Annapolis, Maryland, USA, 153--164.]] Google ScholarGoogle Scholar
  23. Nesterov, Y. and Nemirovsky, A. 1994. Interior Point Polynomial Methods in Convex Programming. SIAM.]]Google ScholarGoogle Scholar
  24. Price, R. and Green, Jr., P. E. 1958. A communication technique for multipath channels. Proceedings of the IRE 46, 555--570.]]Google ScholarGoogle Scholar
  25. Qualcomm. 2004a. Csm2000 cell site modem {online}. Available at: http://www.cdmatech.com/solutions/products/csm2000.jsp.]]Google ScholarGoogle Scholar
  26. Qualcomm. 2004b. Csm5000 cell site modem {online}. Available at: http://www.cdmatech.com/solutions/products/csm5000.jsp.]]Google ScholarGoogle Scholar
  27. Qualcomm. 2004c. Csm5500 cell site modem {online}. Available at: http://www.cdmatech.com/solutions/products/csm5500.jsp.]]Google ScholarGoogle Scholar
  28. Rajkumar, R., Lee, C., Lehoczky, J., and Siewiorek, D. 1997. A resource allocation model for qos management. In Proceedings of IEEE Real-Time Systems Symposium.]] Google ScholarGoogle Scholar
  29. Rudin, W. 1976. Principles of Mathematical Analysis. McGraw-Hill Inc.]]Google ScholarGoogle Scholar
  30. Seto, D., Lehoczky, J. P., Sha, L., and Shin, K. G. 1996. On task schedulability in real-time control systems. In Proceedings of IEEE Real-Time Systems Symposium'96.]] Google ScholarGoogle Scholar
  31. Sha, L., Liu, X., Caccamo, M., and Buttazzo, G. 2000. Online control optimization using load driven scheduling. In Proceedings of the 39th IEEE Conference on Decision and Control.]]Google ScholarGoogle Scholar
  32. Stoica, I., Abdel-Wahab, H., Jeffay, K., Baruah, S., Gehrke, J., and Plaxton, C. G. 1996. A Proportional Share Resource Allocation Algorithm for Real-Time, Time-Shared Systems. In IEEE Real-Time Systems Symposium.]] Google ScholarGoogle Scholar
  33. Viterbi, A. J. 1995. CDMA: Principles of Spread Spectrum Communication. Prentice Hall.]] Google ScholarGoogle Scholar
  34. Waldspurger, C. A. and Weihl, W. E. 1994. Lottery scheduling: Flexible proportional-share resource management. In Operating Systems Design and Implementation. 1--11.]] Google ScholarGoogle Scholar
  35. Xu, Y., Heidemann, J., and Estrin, D. 2001. Geography-informed energy conservation for ad hoc routing. In International Conference on Mobile Computing and Networking. Rome, Italy.]] Google ScholarGoogle Scholar
  36. Ye, Y. 1997a. Interior Point Algorithms: Theory and Analysis. Wiley.]] Google ScholarGoogle Scholar
  37. Ye, Y. 1997b. User's guide of copl_lc, computational optimization program library: Linearly constrained convex programming. Available at: http://www.stanford.edu/~yyye/Col.html.]]Google ScholarGoogle Scholar
  38. Zhao, J. and Govindan, R. 2003. Understanding packet delivery performance in dense wireless sensor networks. In First international conference on Embedded networked sensor systems. LA, CA.]] Google ScholarGoogle Scholar
  39. Zhou, G., He, T., Krishnamurthy, S., and Stankovic, J. A. 2004. Impact of radio irregularity on wireless sensor networks. In Proceedings of the 2nd international conference on Mobile systems, applications, and services (MobiSys '04). ACM Press, 125--138. Boston, MA, USA.]] Google ScholarGoogle Scholar

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