Skip to main content
SpringerLink
Log in

Ant colony prediction by using sectorized diurnal mobility model for handover management in PCS networks

  • Published:
Wireless Networks Aims and scope Submit manuscript

Abstract

Recently, mobile phones are extremely used in lifestyle. Historical records of mobile users (MUs) play an important role in predicting future movements of new visitors of the underlying registration area. Handover (handoff) is one of important quality of service (QoS) parameter that affects the continuity of the call when MUs move from a cell to its neighbors in the same registration area (RA). In this paper, a novel ant based Algorithm, has been introduced, which is called Ant Prediction Algorithm (APA). The main target of APA is to reduce handover impact on the performance of personal communication service (PCS) networks. To accomplish such aim, APA tries to minimize the number of dropped calls by predicting the long-term movement of MUs based on the Sectored Diurnal Mobility Model (SDMM). APA consists of two Parts, namely; (i) the Ant Prediction Engine (APE), which relies on the movement history of the other MUs to predict the future movement of the considered MU, and (ii) the SDMM design, which predicts the exact future sector and cell of the considered MU. Simulations have been presented to validate the proposed scheme in terms of prediction accuracy and handoff blocking probability.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

References

  1. Saleh, A. I. (2010). A new strategy for managing user’s locations in PCS networks using hot spots topology: discussion and analysis. International Journal of Mobile Network Design and Innovation, 3(3), 169–190.

    Article  Google Scholar 

  2. Keshav, T. (2006). Location management in wireless cellular networks. https://www.cse.wustl.edu/~jain/cse574-06/ftp/cellular_location/index.html.

  3. Zhang, J. (2002). Location management in cellular networks. New York: Wiley.

    Book  Google Scholar 

  4. Nayyar, A., & Singh, R. (2014). A comprehensive review of ant colony optimization (ACO) based energy-efficient routing protocols for wireless sensor networks. International Journal of Wireless Networks and Broadband Technologies (IJWNBT), 3(3).

  5. Lim, C. P., & Dehuri, S. (2009). Innovations in swarm intelligence. New York: Springer.

    Book  MATH  Google Scholar 

  6. Beni, G. (2005). From swarm intelligence to swarm robotics. In Swarm robotics, pp. 1–9.

  7. Bonabeau, E., et al. (1999). Swarm intelligence: From natural to artificial systems. Oxford: Oxford University Press.

    MATH  Google Scholar 

  8. Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In Handbook of metaheuristics. Springer, pp. 250–285.

  9. Schoonderwoerd, R., et al. (1997). Ant-based load balancing in telecommunications networks. Adaptive Behavior, 5(2), 169–207.

    Article  Google Scholar 

  10. Roy, B., et al. (2012). Ant colony based routing for mobile ad-hoc networks towards improved quality of services. Journal of Emerging Trends in Computing and Information Sciences, 3(1), 10–14.

    Google Scholar 

  11. Kumar, S. B., & Myilsamy, G. (2013). Ant-colony-based algorithm for multi-target tracking in mobile sensor networks. International Journal of Computer Applications, 64(2), 16–20.

    Article  Google Scholar 

  12. Nayyar, A., & Singh, R. (2017). Simulation and performance comparison of ant colony optimization (ACO) routing protocol with AODV, DSDV, DSR routing protocols of wireless sensor networks using NS-2 simulator. American Journal of Intelligent Systems, 7(1), 19–30.

    Google Scholar 

  13. Shah, P. A., et al. (2013). An enhanced procedure for mobile IPv6 route optimization to reduce handover delay and signaling overhead. In International multi topic conference. Springer.

  14. Liu, C. (2013). A two-step vertical handoff decision algorithm based on dynamic weight compensation. In Proceedings of the ICC 2013.

  15. Zhang, H., Ma, W., Jiang, C., & Li, W. (2011) Signaling cost evaluation of handover management schemes in LTE-advanced femtocell. In IEEE VTC, Budapest, pp. 1–5.

  16. Zhang, H., Jiang, C., Cheng, J., & Leung, V. (2015). Cooperative interference mitigation and handover management for heterogeneous cloud small cell networks. IEEE Wireless Communications, 22(3), 92–99.

    Article  Google Scholar 

  17. Saleh, A. I. M. (2016). A Hybrid mobility prediction (HMP) strategy for PCS networks. Pattern Analysis and Applications, 19(1), 173–206.

    Article  MathSciNet  Google Scholar 

  18. Lu, L., et al. (2011). A dynamic ant colony optimization for load balancing in MRN/MLN. In Asia communications and photonics conference and exhibition, Optical Society of America.

  19. Claes, R., & Holvoet, T. (2010). Maintaining a distributed symbiotic relationship using delegate multiagent systems. In Simulation conference (WSC), proceedings of the 2010 winter. IEEE.

  20. Claes, R., et al. (2011). A decentralized approach for anticipatory vehicle routing using delegate multiagent systems. IEEE Transactions on Intelligent Transportation Systems, 12(2), 364–373.

    Article  Google Scholar 

  21. Chellappa, R., et al. (2003). The sectorized mobility prediction algorithm for wireless networks. In Proceedings of the ICT.

  22. Sadhukhan, S. K., et al. (2010). A novel direction-based diurnal mobility model for handoff estimation in cellular networks. In India conference (INDICON), 2010 annual IEEE. IEEE.

  23. Lin, Y.-B., et al. (2013). Predicting human movement based on telecom’s handoff in mobile networks. IEEE Transactions on Mobile Computing, 12(6), 1236–1241.

    Article  Google Scholar 

  24. Daoui, M., et al. (2008). Mobility prediction based on an ant system. Computer Communications, 31(14), 3090–3097.

    Article  Google Scholar 

  25. Liu, T., et al. (1998). Mobility modeling, location tracking, and trajectory prediction in wireless ATM networks. IEEE Journal on Selected Areas in Communications, 16(6), 922–936.

    Article  Google Scholar 

  26. Martinez-Zeron, E., et al. (2014). Method to improve airborne pollution forecasting by using ant colony optimization and neuro-fuzzy algorithms. International Journal of Intelligence Science, 4(04), 81.

    Article  Google Scholar 

  27. Chen, B., & Chen, L. (2014). A link prediction algorithm based on ant colony optimization. Applied Intelligence, 41(3), 694–708.

    Article  Google Scholar 

  28. Davoodi, M., & Mesgari, M. (2015). GIS-based route finding using ant colony optimization and urban traffic data from different sources. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 40(1), 129.

    Article  Google Scholar 

  29. El Fachtali, I., et al. (2016). Vertical handover decision algorithm using ants’ colonies for 4G heterogeneous wireless networks. Journal of Computer Networks and Communications, 2016, 4.

    Article  Google Scholar 

  30. Hamza, A. A., Saleh, A. I., & Mostafa, M. (2016). A mixed movement predictor (MMP) for handling handover problem in PCS networks. Ciencia e Tecnica Vitivinicola Journal, 31(5), 24–45.

    Google Scholar 

  31. Li, P., et al. (2017). Energy optimization of ant colony algorithm in wireless sensor network. International Journal of Distributed Sensor Networks, 13(4), 1550147717704831.

    Article  Google Scholar 

  32. Su, D. (2010). A self-optimizing mobility management scheme based on cell ID information in high velocity environment. In Proceedings of the IEEE ICCNT, pp. 285–288.

  33. Zhang, W. (2012). Mobility robustness optimization in femtocell networks based on ant colony algorithm. IEICE Transactions on Communications, E95-B(4), 1455–1458.

    Article  Google Scholar 

  34. Zhang, C. (2017). Fog radio access networks: Mobility management, interference mitigation and resource optimization. IEEE Wireless Communications.

  35. Zhang, C. (2017). Network slicing based 5G and future mobile networks: Mobility, resource management, and challenges. IEEE Communications Magazine, 55(8), 138–145.

    Article  Google Scholar 

  36. Shanghai. (2009). A novel self-optimizing handover mechanism for multi-service provisioning in LTE-advanced. In Proceedings of IEEE ICRCCS’09, pp. 221–224.

  37. Heng, H. Z., et al. (2013). Mobility robustness optimization in self-organizing LTE femtocells networks. EURASIP Journal on Wireless Communications and Networking.

Download references

Author information

Authors and Affiliations

  1. Computer Engineering and Systems Department, Faculty of Engineering, Mansoura University, P.O. Box 35516, Mansoura, Egypt

    Ahmed I. Saleh, Mohamed S. Elkasas & Alyaa A. Hamza

Authors
  1. Ahmed I. Saleh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mohamed S. Elkasas
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Alyaa A. Hamza
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Alyaa A. Hamza.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Saleh, A.I., Elkasas, M.S. & Hamza, A.A. Ant colony prediction by using sectorized diurnal mobility model for handover management in PCS networks. Wireless Netw 25, 765–775 (2019). https://doi.org/10.1007/s11276-017-1590-2

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11276-017-1590-2

Keywords

Search

Navigation