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.
Similar content being viewed by others
References
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.
Keshav, T. (2006). Location management in wireless cellular networks. https://www.cse.wustl.edu/~jain/cse574-06/ftp/cellular_location/index.html.
Zhang, J. (2002). Location management in cellular networks. New York: Wiley.
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).
Lim, C. P., & Dehuri, S. (2009). Innovations in swarm intelligence. New York: Springer.
Beni, G. (2005). From swarm intelligence to swarm robotics. In Swarm robotics, pp. 1–9.
Bonabeau, E., et al. (1999). Swarm intelligence: From natural to artificial systems. Oxford: Oxford University Press.
Dorigo, M., & Stützle, T. (2003). The ant colony optimization metaheuristic: Algorithms, applications, and advances. In Handbook of metaheuristics. Springer, pp. 250–285.
Schoonderwoerd, R., et al. (1997). Ant-based load balancing in telecommunications networks. Adaptive Behavior, 5(2), 169–207.
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.
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.
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.
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.
Liu, C. (2013). A two-step vertical handoff decision algorithm based on dynamic weight compensation. In Proceedings of the ICC 2013.
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.
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.
Saleh, A. I. M. (2016). A Hybrid mobility prediction (HMP) strategy for PCS networks. Pattern Analysis and Applications, 19(1), 173–206.
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.
Claes, R., & Holvoet, T. (2010). Maintaining a distributed symbiotic relationship using delegate multiagent systems. In Simulation conference (WSC), proceedings of the 2010 winter. IEEE.
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.
Chellappa, R., et al. (2003). The sectorized mobility prediction algorithm for wireless networks. In Proceedings of the ICT.
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.
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.
Daoui, M., et al. (2008). Mobility prediction based on an ant system. Computer Communications, 31(14), 3090–3097.
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.
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.
Chen, B., & Chen, L. (2014). A link prediction algorithm based on ant colony optimization. Applied Intelligence, 41(3), 694–708.
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.
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.
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.
Li, P., et al. (2017). Energy optimization of ant colony algorithm in wireless sensor network. International Journal of Distributed Sensor Networks, 13(4), 1550147717704831.
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.
Zhang, W. (2012). Mobility robustness optimization in femtocell networks based on ant colony algorithm. IEICE Transactions on Communications, E95-B(4), 1455–1458.
Zhang, C. (2017). Fog radio access networks: Mobility management, interference mitigation and resource optimization. IEEE Wireless Communications.
Zhang, C. (2017). Network slicing based 5G and future mobile networks: Mobility, resource management, and challenges. IEEE Communications Magazine, 55(8), 138–145.
Shanghai. (2009). A novel self-optimizing handover mechanism for multi-service provisioning in LTE-advanced. In Proceedings of IEEE ICRCCS’09, pp. 221–224.
Heng, H. Z., et al. (2013). Mobility robustness optimization in self-organizing LTE femtocells networks. EURASIP Journal on Wireless Communications and Networking.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-017-1590-2