Multipoint Relays Selection Through Spatial Relation Expiration Time in Mobile Ad Hoc Networks

  • Ayoub AbdellaouiEmail author
  • Jamal Elmhamd
  • Halim Berradi
Conference paper
Part of the Lecture Notes in Intelligent Transportation and Infrastructure book series (LNITI)


To generate smart cities and to make it as future Internet, all physical and virtual objects around us must be able of communicating and connecting with each other. Wireless communication in Smart cities network has a scalability, stability and reachability limitations in terms of manageable network aptitude. This is due to the unpredictability of the environments that these communications act through. Mobile Ad hoc Networks (MANETs) introduced new opportunities for network aptitude and capability for smart cities and environments due to the easy implementation, to the fastest and the well-organized way to create communications. Conversely, the mobility of nodes during routing is still challenging in such mobile environments. To tolerate the instantaneous adaptation of the integration and the exploitation of MANETs in smart city environments, an original solution has been suggested. The solution, is a new mechanism to determine stable connected multipoint relay based on the spatial relation expiration time. The author explored the Spatial Relation Expiration Time (SRET) in MANETs using the spatial dependency between the neighbors. Spatial Relation Expiration Time is used to predict the remaining time that this spatial relation is available. The objective is to decrease principal effects that lead to more delay, more lost packets and more disconnecting of the network due to the unpredictable environment for smart city networks. The proposal has been evaluated by NS3 simulator and it confirmed good results for OLSR protocol.


Smart cities Manets Multipoint relay Spatial relation NS3 simulator 


  1. 1.
    Perkins, C.E., Bhagwat, P.: Highly dynamic destination-sequenced distance-vector routing (DSDV) for mobile computers. SIGCOMM Comput. Commun. Rev. 24(4), 234–244 (1994)CrossRefGoogle Scholar
  2. 2.
    Clausen, T., Jacquet, P.: Optimized Link State Routing Protocol (OLSR). RFC Editor (2003)Google Scholar
  3. 3.
    Perkins, C., Belding-Royer, E., Das, S.: Ad hoc on-demand distance vector (AODV) routing: RFC Editor (2003)Google Scholar
  4. 4.
    Johnson, D.B., Maltz, D., Broch, J.: DSR: the dynamic source routing protocol for multi-hop wireless ad hoc networks (2002)Google Scholar
  5. 5.
    Gantsou, D., Sondi, P., Hanafi, S.: Revisiting multipoint relay selection in the optimized link state routing protocol. Int. J. Commun. Netw. Distrib. Syst. 2, 4–15. Scholar
  6. 6.
    Maccari, L., Maischberger, M., Lo Cigno, R.: Where have all the MPRs gone? On the optimal selection of multi-point relays. Ad Hoc Netw. 77. Scholar
  7. 7.
    Abed, A.K., Oz, G., Aybay, I.: Influence of mobility models on the performance of data dissemination and routing in wireless mobile ad hoc networks. Comput. Electr. Eng. 40(2), 319–329 (2014). 2//CrossRefGoogle Scholar
  8. 8.
    Boushaba, A., Benabbou, A., Benabbou, R., Zahi, A., Oumsis, M.: Multi-point relay selection strategies to reduce topology control traffic for OLSR protocol in MANETs. J. Netw. Comput. Appl. 53, 91–102 (2015). 7//CrossRefGoogle Scholar
  9. 9.
    Kitasuka and S. Tagashira, Finding more efficient multipoint relay set to reduce topology control traffic of OLSR. In: 2013 IEEE 14th International Symposium on “A World of Wireless, Mobile and Multimedia Networks” (WoWMoM), Madrid, 2013, pp. 1–9.
  10. 10.
    Bai, F., Narayanan, S., Helmy, A.: IMPORTANT: a framework to systematically analyze the impact of mobility on performance of routing protocols for adhoc networks. In: IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428), San Francisco, CA, 2003, vol. 2, pp. 825–835.
  11. 11.
    Cavalcanti E.R., Spohn, M.A.: Enhancing OLSR protocol performance through improved detection of spatial dependence. In: 2014 IEEE Symposium on Computers and Communications (ISCC), Funchal, 2014, pp. 1–6.
  12. 12.
    Nishiyama, H., Ngo, T., Ansari, N., Kato, N.: On minimizing the impact of mobility on topology control in mobile ad hoc networks. IEEE Trans. Wirel. Commun. 11(3), 1158–1166 (2012)CrossRefGoogle Scholar
  13. 13.
    Sarmah, N., Yang, Y., Sharif, H., Qian, Y.: Performance analysis of MANET routing protocols by varying mobility, speed and network load. In: 2015 9th International Conference on Signal Processing and Communication Systems (ICSPCS), Cairns, QLD, 2015, pp. 1–6.
  14. 14.
    Riley, G.F., Henderson, T.R.: The ns-3 Network Simulator. In: Wehrle, K., Güneş, M., Gross, J. (eds.) Modeling and Tools for Network Simulation, pp. 15–34. Springer, Berlin, Heidelberg (2010)CrossRefGoogle Scholar
  15. 15.
    Fan, W., Shi, Y., Chen S., Zou, L.: A mobility metrics based dynamic clustering algorithm for VANETs. In: IET International Conference on Communication Technology and Application (ICCTA 2011), pp. 752–756, Beijing, 2011Google Scholar
  16. 16.
    Cavalcanti, E.R., Spohn, M.A.: Enhancing OLSR protocol performance through improved detection of Spatial Dependence. In: 2014 IEEE Symposium on Computers and Communications (ISCC), pp. 1–6, Funchal, 2014.
  17. 17.
    Sarma, N., Nandi, S.: Route stability based QoS routing in mobile ad hoc networks. Wirel. Pers. Commun. 54(1), 203–224 (2010). Scholar
  18. 18.
    Su, W., Gerla, M.: IPV6 flow handoff in ad-hoc wireless networks using mobility prediction. In: Proceedings of IEEE Global Communications Conference, Rio de Janeiro, Brazil, pp. 271–275, December 1999Google Scholar
  19. 19.
    Abbas Nayebi, Hamid Sarbazi-Azad, Analysis of link lifetime in wireless mobile networks, Ad Hoc Networks, 10(7), 1221–1237 (2012). ISSN: 1570-8705CrossRefGoogle Scholar
  20. 20.
    Korsnes, R., Ovsthus, K., Li, F.Y., Landmark, L., Kure, O.: Link lifetime prediction for optimal routing in mobile ad hoc networks. In: Proceedings MILCOM, vol. 2, pp. 1245–1251, 17–20 Oct 2005Google Scholar
  21. 21.
    Hua, E.Y., Haas, Z.J.: Mobile-projected trajectory algorithm with velocity-change detection for predicting residual link lifetime in MANET. IEEE Trans. Veh. Technol. 64(3), 1065–1078 (2015). Scholar
  22. 22.
    Zhang, X.M., Zou, F.F., Wang, E.B., Sung, D.K.: Exploring the dynamic nature of mobile nodes for predicting route lifetime in MANETS. IEEE Trans. Veh. Technol. 59(3), 1567–1572 (2010)CrossRefGoogle Scholar
  23. 23.
    Noureddine, H., Ni1, Q., Min, G., Al-Raweshidy, H.: A new link lifetime prediction method for greedy and contention-based routing in mobile ad hoc networks. In: Proceedings IEEE 10th International Conference Computer Information Technology, pp. 2662–2667, June 29–July 1, 2010Google Scholar
  24. 24.
    Lahouari, G.: Mobility prediction in mobile ad hoc networks using neural learning machines. Simul. Model. Pract. Theory 66, 104–121 (2016). CrossRefGoogle Scholar
  25. 25.
    Ouacha, A., Lakki, N., El Abbadi, J., Habbani, A., Bouamoud B., Elkoutbi, M.: Reliable MPR selection based on link lifetime-prediction method. In: 2013 10th IEEE International Conference on networking, Sensing and Control (ICNSC), Evry, 2013, pp. 11–16.
  26. 26.
    Cho, S., Park, H.: OLSR Protocol having a link reliability list in the MPR. In: 2010 the 12th International Conference on Advanced Communication Technology (ICACT), Phoenix Park, 2010, pp. 1231–1234Google Scholar
  27. 27.
    Batabyal, S., Bhaumik, P.: Mobility models, traces and impact of mobility on opportunistic routing algorithms: a survey. IEEE Commun. Surv. Tutor. 17(3), 1679–1707 (2015). Scholar
  28. 28.
    Sadagopan, N., Bai, F., Krishnamachari, B., Helmy, A.: PATHS: analysis of PATH duration statistics and their impact on reactive MANET routing protocols. In: Proceedings of the 4th ACM International Symposium on Mobile Ad Hoc Networking & Computing, pp. 245–256, Annapolis, Maryland, USA, 2003Google Scholar
  29. 29.
    Roy, R.R.: Mobility Model Characteristics, Handbook of Mobile Ad Hoc Networks for Mobility Models, pp. 23–32. Springer, Boston, MA, (2011)Google Scholar
  30. 30.
    Carneiro, G., Fortuna, P., Ricardo, M.: FlowMonitor: a network monitoring framework for the network simulator 3 (NS-3). In: Proceedings of the Fourth International ICST Conference on Performance Evaluation Methodologies and Tools, pp. 1–10, Pisa, Italy, 2009Google Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Laboratory LRGEERIT Team, ENSET, Mohamed V UniversityRabatMorocco
  2. 2.Laboratory SIMEM3s Team, ENSIAS, Mohamed V UniversityRabatMorocco

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