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Computer Science
Distributed Systems

Opportunistic Routing in Wireless Networks

Profile image of Tara JavidiTara Javidi

2016, Foundations and Trends® in Networking

https://doi.org/10.1561/1300000021
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17 pages

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Abstract

Wireless multi-hop networks have become an important part of many modern communication systems. Opportunistic routing aims to overcome the deficiencies of conventional routing on wireless multi-hop networks, by specifically utilizing wireless broadcast opportunities and receiver diversity. Opportunistic routing algorithms, which are specifically optimized to incorporate into the routing decisions a model of wireless transmission, take advantage of scheduling, multiuser , and receiver diversity gains and result in significant reduction in the expected cost of routing per packet. The ability of the algorithm to take advantage of the aspects of wireless transmission, however, depends on the scalability and the additional overhead associated with the opportunistic routing as well as the availability of side information regarding wireless channel statistics, topology, etc. This manuscript sheds light on the performance gains associated with incorporating into the routing strategy the nature of wireless transmission and devises algorithms and solutions to realize these gains in a scalable, practical, and low cost manner. This manuscript first provides an overview of various opportunistic distance-vector algorithms that have been developed to incorporate wireless transmission and routing opportunities. Furthermore, an optimal opportunistic distance metric is proposed whose construction follows from a dynamic programming characterization of the problem. The performance of the optimal routing is then examined against the performance of several other known routing algorithms. To allow for a scalable and distributed solution, the distributed computation of this optimal distance-metric is provided. The performance of a distributed implementation of the optimal opportunistic routing algorithm is also examined via simulation. In addition to the construction of the opportunistic schemes in centralized and distributed fashions, this manuscript also addresses how learning the wireless medium can be efficiently incorporated in the structure of routing algorithm. Finally, this manuscript examines the dynamic congestion-based distance metric and its performance against other congestion aware solutions in the literature.

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References (56)

  1. Qualnet. http://web.scalable-networks.com/, 2015. [Online; accessed 3- March-2015].
  2. Omnet++. http://omnetpp.org/, 2016. [Online; accessed 7-May-2016].
  3. L. Kalampoukas R. Dube A. Shaikh, A. Varma. Routing stability in congested networks: Experimentation and analysis. ACM SIGCOMM Computer Communication Review, 30:163-174, October 2000.
  4. J. S. Baras and H. Mehta. A probabilistic emergent routing algorithm for mobile adhoc networks. In WiOpt03: Modeling and Optimization in Mobile, Ad Hoc and Wireless Net-works, Sophia-Antipolis, France, 2003.
  5. John W. Bates. Packet routing and reinforcement learning: Estimating shortest paths in dynamic graphs. unpublished manuscript, 1995.
  6. D. Bertsekas. Distributed dynamic programming. IEEE Trans. Autom. Control, AC-27(3):610-616, June 1982.
  7. D. Bertsekas and R. Gallagher. Data Networks. Prentice-Hall, 1992.
  8. Dimitri P. Bertsekas. Dynamic Programming and Optimal Control, Vol. II. Athena Scientific, 3rd edition, 2007.
  9. Dimitri P. Bertsekas and John N. Tsitsiklis. Parallel and Distributed Computation: Numerical Methods. Athena Scientific, 1997.
  10. A. Bhorkar and T. Javidi. No regret routing in wireless ad-hoc networks. Asilomar, 2010. References
  11. A.A. Bhorkar, M. Naghshvar, T. Javidi, and B.D. Rao. Exploring and exploiting routing opportunities in wireless ad-hoc networks. In Decision and Control, 2009 held jointly with the 2009 28th Chinese Control Con- ference. CDC/CCC 2009. Proceedings of the 48th IEEE Conference on, pages 4834-4839, Dec. 2009.
  12. S. Biswas and R. Morris. Exor: Opportunistic multi-hop routing for wireless networks. ACM SIGCOMM Computer Communication Review, 35:3344, October 2005.
  13. U. Black. IP routing protocols: RIP, OSPF, BGP, PNNI and Cisco routing protocols. Prentice Hall PTR, Upper Saddle River, NJ, USA, 2000.
  14. J. Boyan and M. Littman. Packet routing in dynamically changing net- works: A reinforcement learning approach. In NIPS, page 671, 1994.
  15. L. Breiman. Probability. Philadelphia, Pennsylvania: Society for Indus- trial and Applied Mathematics, 1992.
  16. Samuel P.M. Choi and Dit-Yan Yeung. Predictive q-routing: A memory- based reinforcement learning approach to adaptive traffic control. In In Advances in Neural Information Processing Systems, 1996.
  17. D. S. J. De Couto, D. Aguayo, J. Bicket, and R. Morris. A high through- put path metric for multi-hop wireless routing. In ACM Mobicom, 2003.
  18. Y. Cui and E. M. Yeh. Enhancing the delay performance of dynamic backpressure algorithms. In Proc. Asimolar Conf., 2013.
  19. S. S. Dhillon and P. Van Mieghem. Performance analysis of the antnet algorithm. computer networks. 2007.
  20. J. Doble. Introduction to Radio Propagation for Fixed and Mobile Com- munications. Artech House, Boston, 1996.
  21. S. Gollakota and D. Katabi. Zigzag decoding: Combating hidden termi- nals in wireless networks. In ACM SIGCOMM, 2008.
  22. P. Gupta and T. Javidi. Towards throughput and delay optimal routing for wireless ad-hoc networks. In Proc. Asimolar Conf., 2007.
  23. P. Gupta and T. Javidi. Towards throughput and delay optimal routing for wireless ad-hoc networks. In Asilomar Conference, pages 249-254, November 2007.
  24. L. Huang, S. Moeller, M.J. Neely, and B. Krishnamachari. LIFO- backpressure achieves near optimal utility-delay tradeoff. In Proc. of 9th Intl. Symposium on Modeling and Optimization in Mobile, Ad Hoc, and Wireless Networks (WiOpt), 2011.
  25. S. Biswas J. Bicket, D. Aguayo and R. Morris. Architecture and evalua- tion of an unplanned 802.11b mesh network. In Proceedings of the 11th annual international conference on Mobile computing and networking , Cologne, Germany, August 28-September 02, 2005.
  26. J. M. Jaffe and F. Moss. A responsive distributed routing alorithm for computer networks. IEEE Transactions on Communications, 30, July 1982.
  27. S. Jain and S. R. Das. Exploiting path diversity in the link layer in wireless ad hoc networks. World of Wireless Mobile and Multimedia Networks, pages 22-30, June 2005.
  28. T. Javidi and D. Teneketzis. Sensitivity Analysis for Optimal Routing in Wireless Ad Hoc Networks in Presence of Error in Channel Quality Estimation. IEEE Transactions on Automatic Control, pages '1303-1316, August 2004.
  29. J.N. Tsitsiklis. Asynchronous Stochastic Approximation and Q-learning. Proceedings of the 32nd IEEE Conference on Decision and Control, 1:'395-400, Dec 1993.
  30. et al K. Stamatiou. A delay-minimizing routing strategy for wireless multi-hop networks. IEEE WiOpt, 7:1-6, June 2009.
  31. Shailesh Kumar and Risto Miikkulainen. Dual Reinforcement Q-Routing: An On-line adaptive routing algorithm. In Smart Engineering Systems: Neural Networks, Fuzzy Logic, Data Mining, and Evolutionary Program- ming, 2000.
  32. M. Kurth, A. Zubow, and J. P. Redlich. Cooperative Opportunistic Rout- ing Using Transmit Diversity in Wireless Mesh Networks . In INFOCOM, pages 1310-1318, April 2008.
  33. S. Shakkottai L. Ying and A. Reddy. On combining shortest-path and back-pressure routing over multihop wireless networks. IEEE/ACM Trans. Networking, 19(3):841-854, June 2011.
  34. P. Larsson. Selection diversity forwarding in a multihop packet radio network with fading channel and capture. ACM SIGMOBILE Mobile Computing and Communications Review, 2(4):4754, October 2001.
  35. E. Leonardi, M. Mellia, M. A. Marsan, and F. Neri. Optimal scheduling and routing for maximum network throughput. IEEE/ACM Transactions on Networking, 15(6):1541-1554, December 2007.
  36. C. Lott and D. Teneketzis. Stochastic routing in ad hoc wireless net- works. Proceedings of the 39th IEEE Conference on Decision and Con- trol, 3:2302-2307 vol.3, 2000. References
  37. C. Lott and D. Teneketzis. Stochastic routing in ad-hoc networks. IEEE Transactions on Automatic Control, 51:52-72, January 2006.
  38. M. J. Neely. Optimal Backpressure Routing for Wireless Networks with Multi-Receiver Diversity. In Conference on Information Sciences and Systems (CISS), March 2006.
  39. M. L. Puterman. Markov Decision Processes: Discrete Stochastic Dy- namic Programming. New York: John Wiley & Sons, 1994.
  40. H. Zhuang M. Naghshvar and T. Javidi. A general class of throughput optimal routing policies in multi-hop wireless networks. IEEE Trans. on Information Theory, 58(4):2175 -2193, April 2012.
  41. E. Modiano M.J. Neely and C. E. Rohrs. Dynamic power allocation and routing for time varying wireless networks. INFOCOM 2003, 1:745-755, March 2003.
  42. M. J. Neely and R. Urgaonkar. Optimal backpressure routing for wire- less networks with multi-receiver diversity. Ad Hoc Networks (Elsevier), 7:862-881, July 2009.
  43. T. Javidi P. Tehrani, Q. Zhao. Opportunistic routing under unknown stochastic models. Proc. of IEEE Workshops on Computational Advances in Multi-Channel Sensor Array Processing (CAMSAP), December 2013.
  44. R. Parr and S. Russell. Reinforcement learning with hierarchies of ma- chines. In In Advances in Neural Information Processing Systems, volume 10. MIT Press, 1997.
  45. P. Purkayastha and J. S. Baras. Convergence of ant routing algorithm via stochastic approximation and optimization. In IEEE Conf. on Decision and Control, pages 340-354, 2007.
  46. E. M. Royer and C. K. Toh. A review of current routing protocols for ad-hoc mobile wireless networks. IEEE Pers. Communications, 6:46-55, April 1999.
  47. S. Russel and P. Norvig. Artificial Intelligence: A Modern Approach. Upper Saddle River, NJ: Prentice Hall, 2nd edition, 2003.
  48. S. Resnick. A Probability Path. Birkhuser, Boston, 1998.
  49. S. Sarkar and S. Ray. Arbitrary throughput versus complexity tradeoffs in wireless networks using graph partitioning. IEEE Transactions on Automatic Control, 53(10):2307-2323, November 2008.
  50. B. Smith and B. Hassibi. Wireless erasure networks with feedback. arXiv:0804.4298v1, 2008.
  51. W. Stallings. Wireless Communications and Networks. Prentice-Hall, 2004.
  52. L. Tassiulas and A. Ephremides. Stability properties of constrained queueing systems and scheduling policies for maximum throughput in multihop radio networks. IEEE Transactions on Automatic Control, 37(12):1936-1949, August 1992.
  53. Y. Xi and E. M. Yeh. Throughput optimal distributed control of stochas- tic wireless networks. In WiOpt, April 2006.
  54. Y. Yi and S. Shakkottai. Hop-by-Hop Congestion Control Over a Wireless Multi-Hop Network. IEEE/ACM Transactions on Networking, 15(1):133-144, February 2007.
  55. L. Ying and S. Shakkottai. On throughput-optimal scheduling with de- layed channel state feedback. In Information Theory and Applications Workshop, 2008.
  56. M. Zorzi and R. R. Rao. Geographic random forwarding (geraf) for ad hoc and sensor networks: Multihop performance. IEEE Transactions on Mobile Computing, 2(4), 2003.

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