Topology Control and Routing in Cognitive Radio Mobile Ad Hoc Networks



Recent research activities about cognitive radio (CR) are mainly focusing on opportunistic spectrum access and spectrum utilization. However, CR technology will have significant impacts on upper layer performance such as topology control and routing in wireless networks, especially in mobile ad hoc networks (MANETs). The dynamic spectrum availability issue imposes more challenges on routing in CR-MANETs. Since the spectrum availability is affected by primary user activities and the mobility of cognitive users, cognitive routing is required to be forward looking rather than reactive. To this end, a topology control and routing framework is presented in this chapter, where cognitive routing is enabled by topology control. In the framework, topology control serves as a middleware and a cross-layer module residing between routing and CR module. Prediction techniques can be used to construct a smart network topology, which provisions cognition capability to routing. Particularly, we present a distributed prediction-based cognitive topology control (PCTC) scheme to demonstrate the framework and verify its feasibility.


Cognitive Radio Medium Access Control Layer Link Prediction Topology Control Cognitive User 


  1. 1.
    Abbagnale, A., Cuomo, F.: Gymkhana: a connectivity-based routing scheme for cognitive radio ad hoc networks. In: INFOCOM IEEE Conference on Computer Communications Workshops (2010), pp. 1–5. San Diego, CA (2010)Google Scholar
  2. 2.
    Akyildiz, I.F., Lee, W., Chowdhury, K.R.: CRAHNs: cognitive radio ad hoc networks. Ad Hoc Netw. 7(5), 810–836 (2009)CrossRefGoogle Scholar
  3. 3.
    Alavi, B., Pahlavan, K.: Modeling of the TOA-based distance measurement error using UWB indoor radio measurements. IEEE Commun. Lett. 10(4), 275–277 (2006)CrossRefGoogle Scholar
  4. 4.
    Benedetto, M.D., Nardis, L.D.: Cognitive routing models in UWB networks. In: 3rd International Conference on Cognitive Radio Oriented Wireless Networks and Communications (CrownCom 2008), pp. 1–6. Singapore (2008)Google Scholar
  5. 5.
    Burkhart, M., von Rickenbach, P., Wattenhofer, R., Zollinger, A.: Does topology control reduce interference? In: Proc. 5th ACM Int. Symposium on Mobile Ad Hoc Networking and Computing. Roppongi Hills, Tokyo, Japan (2004)Google Scholar
  6. 6.
    Butun, I., Talay, A.C., Altilar, D.T., Khalid, M., Sankar, R.: Impact of mobility prediction on the performance of cognitive radio networks. In: 2010 Wireless Telecommunications Symposium (WTS), pp. 1–5. Tampa, FL (2010)Google Scholar
  7. 7.
    Cacciapuoti, A.S., Calcagno, C., Caleffi, M., Paura, L.: CAODV: routing in mobile ad-hoc cognitive radio networks. In: 2010 IFIP Wireless Days (WD), pp. 1–5. Venice, Italy (2010)Google Scholar
  8. 8.
    Cheng, C., Jain, R., van den Berg, E.: Location prediction algorithms for mobile wireless systems. in Wireless internet handbook: technologies, standards, and systems, B. Furht and M. Ilyas, Eds. CRC Press, Inc. Boca Raton, FL (2003). Ch. 11, 245–263Google Scholar
  9. 9.
    Chiang, M., Low, S., Calderbank, A., Doyle, J.: Layering as optimization decomposition: a mathematical theory of network architectures. IEEE Commun. Lett. 95(1), 255–312 (2007)Google Scholar
  10. 10.
    Chou, C.T., Sai Shankar, N., Kim, H., Shin, K.G.: What and how much to gain by spectrum agility? IEEE J. Sel. Areas Commun. 25(3), 576 (2007)CrossRefGoogle Scholar
  11. 11.
    Chowdhury, K., Felice, M.D.: SEARCH: a routing protocol for mobile cognitive radio ad-Hoc networks. In: IEEE Sarnoff Symposium (SARNOFF’09), pp. 1–6. Princeton, NJ (2009)Google Scholar
  12. 12.
    Dai, F., Wu, J.: Mobility-sensitive topology control in mobile ad hoc networks. IEEE Trans. Parallel Distrib. Syst. 17(6), 522–535 (2006)MathSciNetCrossRefGoogle Scholar
  13. 13.
    De Nardis, L., Guirao, M.D.: Mobility-aware design of cognitive radio networks: challenges and opportunities. In: Proc. 5th Int. Conf. Cognitive Radio Oriented Wireless Networks & Communications (CROWNCOM), pp. 169–177. Cannes, Italy (2010)Google Scholar
  14. 14.
    Ding, L., Melodia, T., Batalama, S., Matyjas, J., Medley, M.: Cross-Layer routing and dynamic spectrum allocation in cognitive radio ad hoc networks. IEEE Trans. Veh. Tech. 59(4), 1969–1979 (2010)CrossRefGoogle Scholar
  15. 15.
    Gelenbe, E.: Steps toward self-aware networks. Commun. ACM 52(7), 66–75 (2009)CrossRefGoogle Scholar
  16. 16.
    Gelenbe, E., Lent, R., Nunez, A.: Self-aware networks and QoS. IEEE Commun. Lett. 92(9), 1478–1489 (2004)Google Scholar
  17. 17.
    Guan, Q., Ding, Q., Jiang, S.: A minimum energy path topology control algorithm for wireless multihop networks. In: Proc. IWCMC. Leipzig, Germany (2009)Google Scholar
  18. 18.
    Guan, Q., Ding, Q., Jiang, S.: A minimum energy path topology control algorithm for wireless multihop networks. In: Proc. Int. Conf. on Wireless Comm. and Mobile Computing (ICWCMC). Leipzig, Germany (2009)Google Scholar
  19. 19.
    Guan, Q., Jiang, S., Ding, Q.L., Wei, G.: Impact of topology control on capacity of wireless ad hoc networks. In: Proc. IEEE ICCS’08, pp. 588–592. Guangzhou, China (2008)Google Scholar
  20. 20.
    Guan, Q., Yu, F., Jiang, S., Wei, G.: Prediction-based topology control and routing in cognitive radio mobile ad hoc networks. IEEE Trans. Veh. Tech. 59(9), 4443–4452 (2010)CrossRefGoogle Scholar
  21. 21.
    Haykin, S.: Cognitive radio: brain-empowered wireless communications. IEEE J. Sel. Areas Commun. 23(2), 201–220 (2005)CrossRefGoogle Scholar
  22. 22.
    Hou, Y., Shi, Y., Sherali, H.: Spectrum sharing for multi-hop networking with cognitive radios. IEEE J. Sel. Areas Commun. 26(1), 146–155 (2008)CrossRefGoogle Scholar
  23. 23.
    Irwin, R., DaSilva, L.: Channel assignment based on routing decisions (CARD): Traffic-dependent topology control for multi-channel networks. In: IEEE ICC’09 Workshops, pp. 1–5. Dresden, Germany (2009)Google Scholar
  24. 24.
    Jain, K., Padhye, J., Padmanabhan, V.N., Qiu, L.: Impact of interference on multi-hop wireless network performance. In: Proc. 9th Annual Int. Conf. Mobile Computing and Networking. San Diego (2003)Google Scholar
  25. 25.
    Jiang, H., Lai, L., Fan, R., Poor, V.: Optimal selection of channel sensing order in cognitive radio. IEEE Trans. Wireless Commun. 8(1), 297–307 (2009)CrossRefGoogle Scholar
  26. 26.
    Jiang, S., He, D., Rao, J.: A prediction-based link availability estimation for routing metrics in MANETs. IEEE/ACM Trans. Netw. 13(6), 1302–1312 (2005)CrossRefGoogle Scholar
  27. 27.
    Johansson, T., Carr-Motyăková, L.: Reducing interference in ad hoc networks through topology control. In: Proc. Joint Workshop on Foundations of Mobile Computing. Cologne, Germany (2005)Google Scholar
  28. 28.
    Johnson, D.B., Maltz, D.A., Hu, Y.C.: The dynamic source routing protocol for mobile ad hoc networks (DSR). IETF Draft, draft-ietf-manet-dsr-09.txt (2003)Google Scholar
  29. 29.
    Khalife, H., Ahuja, S., Malouch, N., Krunz, M.: Probabilistic path selection in opportunistic cognitive radio networks. In: Proc. GLOBECOM 2008. New Orleans, LA (2008)Google Scholar
  30. 30.
    Khalife, H., Malouch, N., Fdida, S.: Multihop cognitive radio networks: to route or not to route. IEEE Netw. 23(4), 20–25 (2009)CrossRefGoogle Scholar
  31. 31.
    Komali, R., Thomas, R., Dasilva, L., Mackenzie, A.: The price of ignorance: distributed topology control in cognitive networks. IEEE Trans. Wireless Commun. 9(4), 1434–1445 (2010)CrossRefGoogle Scholar
  32. 32.
    Li, L., Halpern, J., Bahl, P., Wang, Y.M., Wattenhofer, R.: A cone-based distributed topology-control algorithm for wireless multi-hop networks. IEEE/ACM Trans. Netw. 13(1), 147–159 (2005)CrossRefGoogle Scholar
  33. 33.
    Li, Z., Yu, F.R., Huang, M.: A distributed consensus-based cooperative spectrum sensing in cognitive radios. IEEE Trans. Veh. Tech. 9(4), 1370–1379 (2010)MathSciNetGoogle Scholar
  34. 34.
    Liang, Y.C., Zeng, Y., Peh, E.C.Y., Hoang, A.T.: Sensing-throughput tradeoff for cognitive radio networks. IEEE Trans. Wireless Commun. 7(4), 1326–1337 (2008)CrossRefGoogle Scholar
  35. 35.
    Mitola, J., Maguire, G.: Cognitive radio: making software radios more personal. IEEE Personal Comm. 6(4), 13–18 (1999)CrossRefGoogle Scholar
  36. 36.
    Perkins, C.E., Belding-Royer, E.M., Das, S.R.: Ad hoc on-demand distance vector (AODV) routing. IETF Draft, draft-ietf-manet-aodv-13.txt (2003)Google Scholar
  37. 37.
    Perkins, C.E., Bhagwat, P.: Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. In: Proc. ACM SIGCOMM’94. London (1994)Google Scholar
  38. 38.
    Rodoplu, V., Meng, T.H.: Minimum energy mobile wireless networks. IEEE J. Sel. Areas Commun. 17(8), 1333–1344 (1999)CrossRefGoogle Scholar
  39. 39.
    Salamch, H., Krunz, M., O.Younis: Throughput-oriented MAC protocol for opportunistic cognitive radio networks. Tech. Rep. UA-ECE-2007-2, University of Arizona (2007)Google Scholar
  40. 40.
    Santi, P.: Topology control in wireless ad hoc and sensor networks. ACM Comput Survey 37(2), 164–194 (2005)CrossRefGoogle Scholar
  41. 41.
    Santi, P.: Topology control in wireless ad hoc and sensor networks. ACM Comput Surveys (CSUR) 37(2), 164–194 (2005)CrossRefGoogle Scholar
  42. 42.
    Sesia, S., Toufik, I., Baker, M.: LTE, The UMTS Long Term Evolution: From Theory to Practice. Wiley, NY (2009)CrossRefGoogle Scholar
  43. 43.
    Shih, C., Liao, W.: Exploiting route robustness in joint routing and spectrum allocation in Multi-Hop cognitive radio networks. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–5. Sydney, Australia (2010)Google Scholar
  44. 44.
    Si, P., Yu, F., Ji, H., Leung, V.: Optimal Cooperative Internetwork Spectrum Sharing for Cognitive Radio Systems with Spectrum Pooling. IEEE Trans. Veh. Tech. 59(4), 1760–1768 (2010)CrossRefGoogle Scholar
  45. 45.
    Su, H., Zhang, X.: Cross-Layer Based Opportunistic MAC Protocols for QoS Provisionings Over Cognitive Radio Mobile Wireless Networks. IEEE J. Sel. Areas Commun. 26(1), 118–129 (2008)CrossRefGoogle Scholar
  46. 46.
    Tang, J., Xue, G., Zhang, W.: Interference-aware topology control and QoS routing in multi-channel wireless mesh networks. In: Proc. ACM MobiHoc’05. Urbana-Champaign, IL (2005)Google Scholar
  47. 47.
    Thomas, R., Komali, R., MacKenzie, A., DaSilva, L.: Joint power and channel minimization in topology control: A cognitive network approach. In: IEEE ICC ’07, pp. 6538–6543. Glasgow, Scotland (2007)Google Scholar
  48. 48.
    Thomas, R.W., DaSilva, L.A., MacKenzie A.B.: Cognitive networks. In: Proc. IEEE DySPAN’05. Baltimore, M.D (2005)Google Scholar
  49. 49.
    Tseng, Y.C., Ni, S.Y., Chen, Y.S., Sheu, J.P.: The broadcast storm problem in a mobile ad hoc network. Wireless Netw. 8(2/3), 153–167 (2002)CrossRefGoogle Scholar
  50. 50.
    Venkateswaran, A., Sarangan, V., Gautam, N., Acharya, R.: Impact of mobility prediction on the temporal stability of MANET clustering algorithms. In: Proc. 2nd ACM int. workshop on Performance evaluation of wireless ad hoc, sensor, and ubiquitous networks. Montreal, QC (2005)Google Scholar
  51. 51.
    Wattenhofer, R., Zollinger, A.: XTC: A practical topology control algorithm for ad-hoc networks. In: Proc. 18th Int. Parallel and Disttributed Processing Symp.(IPDPS’04). Santa Fe, NM (2004)Google Scholar
  52. 52.
    Wen, Y., Liao, W.: On QoS routing in wireless Ad-Hoc cognitive radio networks. In: IEEE Vehicular Technology Conference (VTC 2010-Spring), pp. 1–5. Taipei, Taiwan (2010)Google Scholar
  53. 53.
    Wysocki, T., Jamalipour, A.: MAC framework for intermittently connected cognitive radio networks. In: Proc. IEEE Personal, Indoor, and Mobile Radio Communications (PIMRC). Tokyo, Japan (2009)Google Scholar
  54. 54.
    Xin, C., Xie, B., Shen, C.: A novel layered graph model for topology formation and routing in dynamic spectrum access networks. In: Proc. IEEE DySPAN’05, pp. 308–317. Baltimore, MD (2005)Google Scholar
  55. 55.
    Xu, Y., Sheng, M., Zhang, Y.: Traffic-Aware routing protocol for cognitive network. In: IEEE Vehicular Technology Conference Fall (VTC 2010-Fall), pp. 1–5. Ottawa, CA (2010)Google Scholar
  56. 56.
    Yang, K., Tsai, Y.: Link stability prediction for mobile ad hoc networks in shadowed environments. In: Proc. IEEE GLOBECOM. San Francisco, CA (2006)Google Scholar
  57. 57.
    Yu, F.R., Huang, M., Tang, H.: Biologically inspired consensus-based spectrum sensing in mobile ad hoc networks with cognitive radios. IEEE Netw. 24(3), 26–30 (2010)CrossRefGoogle Scholar
  58. 58.
    Yuan, Z., Song, J.B., Han, Z.: Interference minimization routing and scheduling in cognitive radio wireless mesh networks. In: IEEE Wireless Communications and Networking Conference (WCNC), pp. 1–6. Sydney, Australia (2010)Google Scholar
  59. 59.
    Zhou, X., Lin, L., Wang, J., Zhang, X.: Cross-layer routing design in cognitive radio networks by colored multigraph model. Wireless Pers Commun. 9(1), 123–131 (2009)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Quansheng Guan
    • 1
  • F. Richard Yu
    • 2
  • Shengming Jiang
    • 3
  1. 1.School of Electrical and Information EngineeringSouth China University of TechnologyGuangzhouChina
  2. 2.School of Information Technology, Department of Systems and Computer EngineeringCarleton UniversityOttawaCanada
  3. 3.School of Electrical and Information EngineeringSouth China University of TechnologyGuangzhouChina

Personalised recommendations