Skip to main content
SpringerLink
Log in

CTR: Carry Time-Based Routing for Increasing Delivery Ratio in Mobile Social Networks

  • Published:
Wireless Personal Communications Aims and scope Submit manuscript

Abstract

Mobile social network is a kind of social networks through which the nodes have some social features such as: gender, job, friendship relations, centrality, and so on. The nodes mobility, leads to avoid forming connected routes between the source and the destination all the times. Therefore, routing is one of the main challenges in these networks. In such networks for routing store–carry–forward pattern is used. In this method each node produces a packet or receives it from another node, carries it until it finds an appropriate opportunity, and delivers it to the encountered node when an opportunity occurs. During the time interval through which the node tries to find a good opportunity and carries the packet is called carry time. Most proposed routing methods have had some suggestions on store step or forward step. It seems that we can improve routing in these networks through considering carry time by the nodes. This means that the longer carry time of a node shows that it is not an appropriate candidate to redistribute the packet. In this paper a routing algorithm based on carry time (CTR) is proposed. In this method carry time by the nodes is saved for each packet. The proposed method was presented considering three states of single-copy and only based on carry time, single copy and based on carry time and the probability of encountering the nodes, and multi copy and quota based on carry time and encountering the nodes. The simulation results show that proposed method optimized regarding delivery ratio and the average delay compared to the related works.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Similar content being viewed by others

References

  1. Ellison, N. B. (2007). Social network sites: Definition, history, and scholarship. Journal of Computer-Mediated Communication,13(1), 210–230.

    Article  MathSciNet  Google Scholar 

  2. Kayastha, N., Niyato, D., Wang, P., & Hossain, E. (2011). Applications, architectures, and protocol design issues for mobile social networks: A survey. Proceedings of the IEEE,99(12), 2130–2158.

    Article  Google Scholar 

  3. Tien, C. H., & Yen, C. S. (2016). A study of the application strategies of mobile social media—The LINE official account in university and college marketing and public relations. US China Education Review,6(4), 233–240.

    Google Scholar 

  4. Qiu, T., Chen, B., Sangaiah, A. K., Ma, J., & Huang, R. (2017). A survey of mobile social networks: Applications, social characteristics, and challenges. IEEE Systems Journal,99, 1–16.

    Google Scholar 

  5. Yuan, P., & Liu, P. (2015). Data fusion prolongs the lifetime of mobile sensing networks. Journal of Network and Computer Applications,49, 51–59.

    Article  Google Scholar 

  6. Mhaske, P., & Londhe, D. D. (2015). A survey on optimal opportunistic routing in mobile social network. Networking and Communication Engineering,7(2), 51–54.

    Google Scholar 

  7. Kimura, T., Matsuda, T., & Takine, T. (2015). Location-aware store–carry–forward routing based on node density estimation. IEICE Transactions on Communications,98(1), 99–106.

    Article  Google Scholar 

  8. Kaiwartya, O., & Kumar, S. (2015). Guaranteed geocast routing protocol for vehicular adhoc networks in highway traffic environment. Wireless Personal Communications,83(4), 2657–2682.

    Article  Google Scholar 

  9. Cao, Y., Kaiwartya, O., Aslam, N., Han, C., Zhang, X., Zhuang, Y., et al. (2018). A trajectory-driven opportunistic routing protocol for VCPS. IEEE Transactions on Aerospace and Electronic Systems,54(6), 2628–2642.

    Article  Google Scholar 

  10. Lindgren, A., Doria, A., & Schelén, O. (2003). Probabilistic routing in intermittently connected networks. ACM SIGMOBILE Mobile Computing and Communications Review,7(3), 19–20.

    Article  Google Scholar 

  11. Wu, J., Xiao, M., & Huang, L. (2013). Homing spread: Community home-based multi-copy routing in mobile social networks. In Proceedings IEEE INFOCOM (pp. 2319–2327).

  12. Derakhshanfard, N., Sabaei, M., & Rahmani, A. M. (2016). Sharing spray and wait routing algorithm in opportunistic networks. Wireless Networks,22(7), 2403–2414.

    Article  Google Scholar 

  13. Derakhshanfard, N., Sabaei, M., & Rahmani, A. M. (2017). CPTR: Conditional probability tree based routing in opportunistic networks. Wireless Networks,23(1), 43–50.

    Article  Google Scholar 

  14. Hui, P., & Crowcroft, J. (2007). How small labels create big improvements. In Fifth Annual IEEE International Conference on Pervasive Computing and Communications Workshops (PerComW'07) (pp 65–70). IEEE.

  15. Yang, S., Yang, X., Zhang, C., & Spyrou, E. (2010). Using social network theory for modeling human mobility. IEEE Network,24(5), 6–13.

    Article  Google Scholar 

  16. Hui, P., Crowcroft, J., & Yoneki, E. (2010). Bubble rap: Social-based forwarding in delay-tolerant networks. IEEE Transactions on Mobile Computing, 10(11), 1576–1589.

    Article  Google Scholar 

  17. Qureshi, K. N., Abdullah, A. H., Kaiwartya, O., Ullah, F., Iqbal, S., & Altameem, A. (2016). Weighted link quality and forward progress coupled with modified RTS/CTS for beaconless packet forwarding protocol (B-PFP) in VANETs. Telecommunication Systems 1–16.

  18. Hassan, A. N., Kaiwartya, O., Abdullah, A. H., Sheet, D. K., & Raw, R. S. (2018). Inter vehicle distance based connectivity aware routing in vehicular adhoc networks. Wireless Personal Communications,98(1), 33–54.

    Article  Google Scholar 

  19. Hassan, A. N., Abdullah, A. H., Kaiwartya, O., Cao, Y., & Sheet, D. K. (2018). Multi-metric geographic routing for vehicular ad hoc networks. Wireless Networks,24(7), 2763–2779.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Engineering, Tabriz Branch, Islamic Azad University, Tabriz, Iran

    Simin Abdolhosseinzadeh & Nahideh Derakhshanfard

Authors
  1. Simin Abdolhosseinzadeh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Nahideh Derakhshanfard
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Nahideh Derakhshanfard.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Abdolhosseinzadeh, S., Derakhshanfard, N. CTR: Carry Time-Based Routing for Increasing Delivery Ratio in Mobile Social Networks. Wireless Pers Commun 110, 1271–1282 (2020). https://doi.org/10.1007/s11277-019-06785-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11277-019-06785-1

Keywords

Search

Navigation