Location and Mobility-Aware Routing for Improving Multimedia Streaming Performance in MANETs


Device mobility is an issue that affects both Mobile ad hoc networks (MANETs) and opportunistic networks. While the former employs conventional routing techniques with some element of mobility management, opportunistic networking protocols often use mobility as a means of delivering messages in intermittently connected networks. If nodes are able to determine the future locations of other nodes with reasonable accuracy then they could plan ahead and take into account and even benefit from such mobility. In an ad hoc network, devices form a network amongst themselves and forward packets for each other without infrastructure. Ad hoc networks could be deployed in a disaster scenario to enable communications between responders and base camp to provide telemedicine services. However, most ad hoc routing protocols cannot meet the necessary standards for streaming multimedia because they do not attempt to manage quality of service (QoS). Node mobility adds an additional layer of complexity leading to potentially detrimental effects on QoS. Geographic routing protocols use physical locations to make routing decisions and are typically lightweight, distributed, and require only local network knowledge. They are thus less susceptible to the effects of mobility, but are not impervious. Location-prediction can be used to enhance geographic routing, and counter the negative effects of mobility, but this has received relatively little attention. Location prediction in combination with geographic routing has been explored in previous literature. Most of these location prediction schemes have made simplistic assumptions about mobility. However more advanced location prediction schemes using machine learning techniques have been used for wireless infrastructure networks. These approaches rely on the use of infrastructure and are therefore unsuitable for use in opportunistic networks or MANETs. To solve the problem of accurately predicting future location in non-infrastructure networks, we investigate the prediction of continuous numerical coordinates using artificial neural networks. Simulation using three different mobility models representing human mobility has shown an average prediction error of <1 m in normal circumstances.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2


  1. 1.

    Aschenbruck, N., Ernst, R., Gerhards-Padilla, E., & Schwamborn, M. (2010). BonnMotion: A mobility scenario generation and analysis tool. In Proceedings of the 3rd international ICST conference on simulation tools and techniques, pp. 51:1—51:10.

  2. 2.

    Boldrini, C., Conti, M., Jacopini, J., & Passarella, A. (2007). HiBOp: A history based routing protocol for opportunistic networks. In World of w ireless, mobile and multimedia networks , 2007. WoWMoM 2007. IEEE international symposium on a, pp. 1–12.

  3. 3.

    Cadger, F., Curran, K., Santos, J., & Moffett, S. (2011). An Analysis of the Effects of Intelligent Location Prediction Algorithms on Greedy Geographic Routing in Mobile Ad-Hoc Networks. In Proceedings of the 22nd Irish Conference on artificial intelligence and cognitive s cience, Derry.

  4. 4.

    Cadger, F., Curran, K., Santos, J., & Moffett, S. (2012). MANET location prediction using machine learning algorithms. In Y. Koucheryavy, L. Mamatas, I. Matta, & V. Tsaoussidis (Eds.), Wired/wireless internet communication (pp. 174–185). Berlin Heidelberg: Springer.

    Chapter  Google Scholar 

  5. 5.

    Camp, T., Boleng, J., & Davies, V. (2002). A survey of mobility models for ad hoc network research. Wireless Communications and Mobile Computing, 2(5), 483–502.

    Article  Google Scholar 

  6. 6.

    Capka, J. & Boutaba, R. (2004). Mobility prediction in wireless networks using neural networks. Management of multimedia networks and services, pp. 320–333.

  7. 7.

    Chou, C., Ssu, K., & Jiau, H. (2008). Dynamic route maintenance for geographic forwarding in mobile ad hoc networks. Computer Networks, 52(2), 418–431.

    Article  Google Scholar 

  8. 8.

    Chen, Q., Kanhere, S., Hassan, M., & Lan, K.-C. (2006). Adaptive position update in geographic routing. In 2006 IEEE international conference on communications, vol. 0, no. c, pp. 4046–4051.

  9. 9.

    Hong, X., Gerla, M., Pei, G. & Chiang, C.-C. (1999). A group mobility model for ad hoc wireless networks. In Proceedings of the 2nd ACM international workshop on modeling , analysis and simulation of wireless and mobile systems, pp. 53–60.

  10. 10.

    Igel, C., & Husken, M. (2000). Improving the RPROP learning algorithm. In Proceedings of the second international symposium on neural computat ion (NCC 2000).

  11. 11.

    Johnson, D. B., & Maltz, D. A. (1996). Dynamic source routing in ad hoc wireless networks. In Mobile c omputing, pp. 153–181.

  12. 12.

    Karp, B., & Kung, H. (2000). GPSR: greedy perimeter stateless routing for wireless networks. In Proceedings of the 6th annual international conference on Mobile computing and networking, pp. 243–254.

  13. 13.

    Kiess, W. (2012). HLS patch for ns-2.29 and ns-2.33 [Online]. http://www.cn.uni-duesseldorf.de/alumni/kiess/software/hls-ns2-patch

  14. 14.

    Li, J., & Shatz, S. M.(2008). Toward using node mobility to enhance Greedy-forwarding in geographic routing for mobile ad hoc networks. In The international workshop on mobile device and urban sensing (MODUS 2008), (pp. 1–8), St. Louis, MO.

  15. 15.

    Liang, B., & Haas, Z. J. (1999). Predictive distance-based mobility management for PCS networks. In INFOCOM ’99. Eighteenth Annual joint conference of the IEEE computer and communications societies . Proceedings. IEEE, vol. 3, pp. 1377–1384.

  16. 16.

    Lilien, L., Gupta, A., Member, S., & Yang, Z. (2006). Opportunistic networks for emergency preparedness and response. In Performance, Computing, and Communications Conference Proceedings, IPCCC 2007, (p. 588–593) New York, USA

  17. 17.

    Lilien, L., Kamal, Z. H., Bhuse, V., & Gupta, A. (2006). Opportunistic networks: the concept and research challenges in privacy and security. In International workshop on research challenges in security and privacy for mobile and wireless networks (WSPWN 2006), (p. 134–147) Miami, Florida.

  18. 18.

    Lindgren, A., Doria, A., & Schelén, O. (2003). Probabilistic routing in intermittently connected networks. SIGMOBILE Mobile Computing and Communications Review, 7, 19–20.

    Article  Google Scholar 

  19. 19.

    Musolesi, M., & Mascolo, C. (2006). A community based mobility model for ad hoc network research. In Proceedings of the 2nd international workshop on multi-hop ad hoc networks: From theory to reality (pp. 31–38) ACM, New York, NY, USA.

  20. 20.

    Nguyen, H. A., Giordano, S., & Puiatti, A. (2007). Probabilistic routing protocol for intermittently connected mobile ad hoc networks (PROPICMAN), IEEE AOC 2007, Helsinki.

  21. 21.

    Nguyen, H. A., & Giordano, S. (2008). Spatiotemporal routing algorithm in opportunistic networks. In World of wireless, mobile and multimedia net works, 2008. WoWMoM 2008. 2008 international s ymposium on a, pp. 1–6.

  22. 22.

    Nguyen, H. A., & Giordano, S. (2009). Routing in opportunistic networks. International Journal of Ambient Computing and Intelligence (IJACI), 1, 19–38.

    Article  Google Scholar 

  23. 23.

    Prasad, P. S., & Agrawal, P. (2010). Movement prediction in wireless networks using mobility traces. In Consumer communications and networking conference (CCNC), 7th IEEE, pp. 1–5.

  24. 24.

    Riedmiller, M., & Braun, H. (1993). A direct adaptive method for faster backpropagation learning: The RPROP algorithm. In Neural Networks, 1993., IEEE international conference on, vol. 1, pp. 586–591.

  25. 25.

    Sarle, W.S., ed. (1997), Neural network FAQ, part 1 of 7: Introduction, periodic posting to the Usenet newsgroup comp.ai.neural-nets, URL: ftp://ftp.sas.com/pub/neural/FAQ.html. Last Accessed 10 February 2013.

  26. 26.

    Shah, & Nahrstedt, K. (2002). Predictive location-based QoS routing in mobile ad hoc networks. In Proceedings of IEEE international conference on communications. ICC, no. 1, pp. 1022–1027.

  27. 27.

    Son, D., Helmy, A., & Krishnamachari, B. (2004). The effect of mobility-induced location errors on geographic routing in mobile ad hoc and sensor networks: Analysis and improvement using mobility prediction. IEEE Transactions on Mobile Computing, 3(3), 233–245.

    Article  Google Scholar 

  28. 28.

    Spyropoulos, T., Psounis, K., Raghavendra, C.S. (2005). Spray and wait: An efficient routing scheme for intermittently connected mobile networks. In Proceedings of the 2005 ACM SIGCOMM workshop on delay -tolerant Networking (pp. 252–259) ACM, New York, NY, USA.

  29. 29.

    Stojmenovic, I., Russell, M., & Vukojevic, B. (2002). Depth first search and location based localized routing and QoS routing in wireless networks. In Parallel processing, 2000. Proceedings. 2000 international conference on, pp. 173–180.

  30. 30.

    Vahdat, A., & Becker, D. (2000). Epidemic routing for partially connected ad hoc networks.

  31. 31.

    Wang, Y., Jain, S., Martonosi, M., & Fall. K. (2005). Erasure-coding based routing for opportunistic networks. In Proceedings of the 2005 ACM SIGCOMM workshop on delay-tolerant networking, (pp. 229–236) ACM, New York, NY, USA.

Download references

Author information



Corresponding author

Correspondence to Kevin Curran.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Cadger, F., Curran, K., Santos, J. et al. Location and Mobility-Aware Routing for Improving Multimedia Streaming Performance in MANETs. Wireless Pers Commun 86, 1653–1672 (2016). https://doi.org/10.1007/s11277-015-3012-z

Download citation


  • Geographical routing
  • Manets
  • Ad hoc networks
  • Routing
  • QoS