Abstract
In Internet of Things, the resource distribution is random in space, which leads to the poor precision ratio of the cluster resource indexing of Internet of Things, so in order to improve the information fusion and dispatching ability of Internet of Things, it is necessary to optimize the resource indexing of Internet of Things. Therefore, an algorithm for cluster resource indexing of Internet of Things based on improved ant colony algorithm is proposed in this paper. Directed graph models are used to construct a distribution structure model of cluster resource indexing nodes of Internet of Things, carry out semantic association feature extraction in the cluster resource storage information flow of Internet of Things. And the improved ant colony algorithm is used to crawl and capture cluster information in Internet of Things. According to the ant colony trajectory information, the velocity and position of the cluster resource indexing of Internet of Things are updated, and the balanced ant colony algorithm is used to carry out the global search and local search to resources and initialize the clustering center, and the target function of the cluster resource indexing of Internet of Things is constructed and the optimization parameter is solved with the constraint condition of the minimum variance of the whole fitness. The strong ability of global optimization of the ant colony algorithm is used to realize resource indexing optimization. Simulation results show that the improved algorithm can quickly realize resource index convergence, effectively escape local minimum points, and has strong global search ability and relatively high resource indexing precision ratio.
Similar content being viewed by others
References
Zhou, Q., Yi, P., Men, H.S.: Virtual network function backup method based on resource utility maximization. J. Comput. Appl. 37(4), 948–953 (2017)
Staff, C., Azzopardi, J., Layfield, C., et al.: Search results clustering without external resources. In: International Workshop on Database and Expert Systems Applications. IEEE Computer Society, pp. 276–280 (2015)
Sun, Y., Dong, W., Chen, Y.: An improved routing algorithm based on ant colony optimization in wireless sensor networks. IEEE Commun. Lett. 99, 1–10 (2017)
Kamaei, Z., Bakhshi, H., Masoumi, B.: Improved harmony search algorithm with ant colony optimization algorithm to increase the lifetime of wireless sensor networks. Dis. Colon Rectum 40(10), 1170–1176 (2015)
Bliman, P.A., Ferrari-Trecate, G.: Average consensus problems in networks of agents with delayed communications. Automatica 44(8), 1985–1995 (2013)
Kotagi, V.J., Thakur, R., Mishra, S., et al.: Breathe to save energy: assigning downlink transmit power and resource blocks to LTE enabled IoT networks. IEEE Commun. Lett. 20(8), 1607–1610 (2016)
Li, F.G., Wei, Y.Y., Yang, L.: Computing resource optimization in heterogeneous Hadoop cluster based on harmony search algorithm. Comput. Eng. Appl. 50(9), 98–102 (2014)
Liu, B., Tan, X.M., Cao, W.B.: Dynamic resource alposition strategy in spark streaming. J. Comput. Appl. 37(6), 1574–1579 (2017)
Zhang, M., Cheng, K., Yang, X.B.: Multigranulation rough set based on weighted granulations. Control Decis. 30(2), 222–228 (2015)
Semasinghe, P., Maghsudi, S., Hossain, E.: Game theoretic mechanisms for resource management in massive wireless IoT systems. IEEE Commun. Mag. 55(2), 121–127 (2017)
Wang, P., Lin, H.T., Wang, T.S.: An improved ant colony system algorithm for solving the IP traceback problem. Inf. Sci. 326, 172–187 (2016)
Hu, J., Hu, X.D., Chen, J.X.: Big data hybrid computing mode based on spark. Comput. Syst. Appl. 24(4), 214–218 (2015)
Sun, W., Yuan, D., Ström, E.G., et al.: Cluster-based radio resource management for D2D-supported safety-critical V2X communications. IEEE Trans. Wirel. Commun. 15(4), 2756–2769 (2016)
Arkian, H.R., Atani, R.E., Diyanat, A., et al.: A cluster-based vehicular cloud architecture with learning-based resource management. J. Supercomput. 71(4), 1401–1426 (2015)
Oh, S.M., Shin, J.S.: An efficient small data transmission scheme in the 3GPP NB-IoT system. IEEE Commun. Lett. 21(3), 660–663 (2017)
He, L., Ding, Z.Y., Jia, Y.: Category candidate search in large scale hierarchical classification. Chin. J. Comput. 37(1), 41–49 (2014)
Saichon, S., Fernald, A.G., Adams, R.M., et al.: The research of work search method choice applying the cluster analysis. Eng. Econ. 37(48), 6–11 (2015)
Zhou, Q., Liu, R.: Strategy optimization of resource scheduling based on cluster rendering. Clust. Comput. 19(4), 1–9 (2016)
Jing, P.J., Shen, H.B.: MACOED: a multi-objective ant colony optimization algorithm for SNP epistasis detection in genome-wide association studies. Bioinformatics 31(5), 634–641 (2015)
Acknowledgements
This work was supported by National Natural Science Foundation of China under Grant No. 71673077.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Hong, Y., Chen, L. & Mo, L. Optimization of cluster resource indexing of Internet of Things based on improved ant colony algorithm. Cluster Comput 22 (Suppl 3), 7379–7387 (2019). https://doi.org/10.1007/s10586-017-1496-x
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10586-017-1496-x