Abstract
Internet of Things (IoT) provides the opportunity to access devices at any time by connecting hundreds of billions of devices. However, routing among the massive number of devices can be a huge challenge. The increasing network size and dynamics of IoT introduce unmanageable routing overhead caused by the maintaining and query of the routing table. As a future network architecture, Information-Centric Networking (ICN) is raised as a promising networking model for IoT. Thus, in this paper, we leverage the ICN and edge computing paradigm to design a routing scheme for IoT. First, we design a hybrid addressing scheme to assign virtual coordinates. The hybrid addressing not only provides a high routing success ratio but also has low address description complexity (i.e., has a short address). Then, we propose a hyperbolic edge routing scheme based on the virtual coordinate. We evaluate our proposal via an extensive simulation. Simulation results show that the proposed routing scheme has a low path stretch and high routing success ratio. Compared with the existing routing schemes, our proposal can reduce the delay and hop count, while achieving a high cache hit ratio.









Similar content being viewed by others
References
Al-Fuqaha, A., Guizani, M., Mohammadi, M., Aledhari, M., & Ayyash, M. (2015). Internet of things: a survey on enabling technologies, protocols, and applications. IEEE Communication Surveys and Tutorials., 17, 2347–2376. https://doi.org/10.1109/COMST.2015.2444095
Liu, D., Cao, Z., Hou, M., Rong, H., & Jiang, H. (2020). Pushing the limits of transmission concurrency for low power wireless networks. ACM Transactions on Sensor Networks, 16, 1–29. https://doi.org/10.1145/3406834
Cisco: Cisco Annual Internet Report (2018–2023) White Paper, https://www.cisco.com/c/en/us/solutions/collateral/executive-perspectives/annual-internet-report/white-paper-c11-741490.html, last accessed 2020/07/09.
Arshad, S., Azam, M. A., Rehmani, M. H., & Loo, J. (2019). Recent Advances in information-centric networking-based internet of things (ICN-IoT). IEEE Internet of Things Journal, 6, 2128–2158. https://doi.org/10.1109/JIOT.2018.2873343
Xylomenos, G., Ververidis, C. N., Siris, V. A., Fotiou, N., Tsilopoulos, C., Vasilakos, X., Katsaros, K. V., & Polyzos, G. C. (2014). A survey of information-centric networking research. IEEE Communcations Surveys and Tutorials, 16, 1024–1049. https://doi.org/10.1109/SURV.2013.070813.00063
Xiao, Z., Dai, X., Jiang, H., Wang, D., Chen, H., Yang, L., & Zeng, F. (2020). Vehicular task offloading via heat-aware mec cooperation using game-theoretic method. IEEE Internet of Things Journal, 7, 2038–2052. https://doi.org/10.1109/JIOT.2019.2960631
Boguñá, M., Papadopoulos, F., & Krioukov, D. (2010). Sustaining the Internet with hyperbolic mapping. Nature Communications, 1, 62. https://doi.org/10.1038/ncomms1063
Lehman, V., Gawande, A., Zhang, B., Zhang, L., Aldecoa, R., Krioukov, D., Wang, L.: An experimental investigation of hyperbolic routing with a smart forwarding plane in NDN. In: 2016 IEEE/ACM 24th International Symposium on Quality of Service (IWQoS). pp. 1–10. IEEE, Beijing, China (2016). https://doi.org/10.1109/IWQoS.2016.7590394.
Voitalov, I., Aldecoa, R., Wang, L., & Krioukov, D. (2017). Geohyperbolic routing and addressing schemes. SIGCOMM Computer Communication Review, 47, 11–18. https://doi.org/10.1145/3138808.3138811
Li, J., Zeng, F., Xiao, Z., Jiang, H., Zheng, Z., Liu, W., & Ren, J. (2020). Drive2friends: Inferring social relationships from individual vehicle mobility data. IEEE Internet of Things Journal, 7, 5116–5127. https://doi.org/10.1109/JIOT.2020.2974669
Papadopoulos, F., Psomas, C., & Krioukov, D. (2015). Network mapping by replaying hyperbolic growth. IEEE/ACM Transactions on Networking, 23, 198–211. https://doi.org/10.1109/TNET.2013.2294052
Papadopoulos, F., Aldecoa, R., & Krioukov, D. (2015). Network geometry inference using common neighbors. Physical Review E, 92, 022807. https://doi.org/10.1103/PhysRevE.92.022807
Blasius, T., Friedrich, T., Krohmer, A., & Laue, S. (2018). Efficient embedding of scale-free graphs in the hyperbolic plane. IEEE/ACM Transactions on Networking, 26, 920–933. https://doi.org/10.1109/TNET.2018.2810186
Li, X., Li, D., Wan, J., Liu, C., & Imran, M. (2018). Adaptive Transmission optimization in SDN-based industrial internet of things with edge computing. IEEE Internet of Things Journal, 5, 1351–1360. https://doi.org/10.1109/JIOT.2018.2797187
Huy, T., Prasad, C., Dmitrii, C., Shizeng, Y., Qing, L., Fan, G., & Kannappan, P. (2018). Energy-aware mobile edge computing and routing for low-latency visual data processing. IEEE Transactions on Multimedia., 20, 2562–2577.
Poularakis, K., Llorca, J., Tulino, A. M., Taylor, I., & Tassiulas, L. (2020). Service placement and request routing in mec networks with storage, computation, and communication constraints. IEEE/ACM Transactions on Networking, 28, 1047–1060. https://doi.org/10.1109/TNET.2020.2980175
Xu, X., Xue, Y., Qi, L., Yuan, Y., Zhang, X., Umer, T., & Wan, S. (2019). An edge computing-enabled computation offloading method with privacy preservation for internet of connected vehicles. Future Generation Computer Systems., 96, 89–100. https://doi.org/10.1016/j.future.2019.01.012
Li, X., Zhou, Z., Guo, J., Wang, S., & Zhang, J. (2019). Aggregated multi-attribute query processing in edge computing for industrial IoT applications. Computer Networks, 151, 114–123. https://doi.org/10.1016/j.comnet.2019.01.022
Zhou, Z., Wu, Q., & Chen, X. (2019). Online orchestration of cross-edge service function chaining for cost-efficient edge computing. IEEE Journal on Selected Areas in Communications, 37, 1866–1880. https://doi.org/10.1109/JSAC.2019.2927070
Kaur, K., Garg, S., Aujla, G. S., & Kumar, N. (2018). Edge computing in the industrial internet of things environment: Software-defined-networks-based edge-cloud interplay. IEEE Communications Magazine, 56(2), 44–51.
Liang Wang, Waltari, O., Kangasharju, J.: MobiCCN: Mobility support with greedy routing in Content-Centric Networks. In: 2013 IEEE Global Communications Conference (GLOBECOM). pp. 2069–2075. IEEE, Atlanta, GA (2013). https://doi.org/10.1109/GLOCOM.2013.6831380.
Cvetkovski, A., Crovella, M.: Low-stretch greedy embedding heuristics. In: 2012 Proceedings IEEE INFOCOM Workshops. pp. 232–237. IEEE, Orlando, FL, USA (2012). https://doi.org/10.1109/INFCOMW.2012.6193497.
Grassi, G., Pesavento, D., Pau, G., Zhang, L., Fdida, S.: Navigo: Interest forwarding by geolocations in vehicular Named Data Networking. In: 2015 IEEE 16th International Symposium on A World of Wireless, Mobile and Multimedia Networks (WoWMoM) (2015).
Azgin, A., Ravindran, R., Wang, G.: On-demand mobility support with anchor chains in information centric networks. In: 2017 IEEE International Conference on Communications (ICC). pp. 1–7. IEEE, Paris, France (2017). https://doi.org/10.1109/ICC.2017.7997210.
Rao, A., Ratnasamy, S., Papadimitriou, C., Shenker, S., Stoica, I.: Geographic Routing without Location Information. 13.
Kleinberg, R.: Geographic Routing Using Hyperbolic Space. In: IEEE INFOCOM 2007 - 26th IEEE International Conference on Computer Communications. pp. 1902–1909. IEEE, Anchorage, AK, USA (2007). https://doi.org/10.1109/INFCOM.2007.221.
Yang, W., Qin, Y., Yi, Z., Yang, Y.: Content-Based Hyperbolic Routing and Push Mechanism in Named Data Networking. In: ICC 2019 - 2019 IEEE International Conference on Communications (ICC). pp. 1–6. IEEE, Shanghai, China (2019). https://doi.org/10.1109/ICC.2019.8762055.
Ester, M., Kriegel, H.-P., Sander, J., Xu, X.: A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise. In: Proceedings of the Second International Conference on Knowledge Discovery and Data Mining. pp. 226–231 (1996).
Campello, R. J. G. B., Moulavi, D., & Sander, J. (2013). Density-Based Clustering Based on Hierarchical Density Estimates. In J. Pei, V. S. Tseng, L. Cao, H. Motoda, & G. Xu (Eds.), Advances in Knowledge Discovery and Data Mining (pp. 160–172). Berlin Heidelberg, Berlin, Heidelberg: Springer.
Ertöz, L., Steinbach, M., Kumar, V.: Finding Clusters of Different Sizes, Shapes, and Densities in Noisy, High Dimensional Data. In: Proceedings of the 2003 SIAM International Conference on Data Mining. pp. 47–58. Society for Industrial and Applied Mathematics (2003). https://doi.org/10.1137/1.9781611972733.5.
Ankerst, M., Breunig, M. M., Kriegel, H.-P., & Sander, J. (1999). OPTICS: Ordering points to identify the clustering structure. ACM SIGMOD Record., 28, 49–60. https://doi.org/10.1145/304181.304187
Böhm, C., Plant, C.: HISSCLU: A Hierarchical Density-Based Method for Semi-Supervised. In: Proceedings of the 11th international conference on Extending database technology: Advances in database technology. pp. 440–451 (2008).
Zhang, L., Afanasyev, A., Burke, J. A., Jacobson, V. L., Claffy, K. C., Crowley, P. J., Papadopoulos, C., Wang, L., & Zhang, B. (2014). Named data networking. ACM SIGCOMM Computer Communication Review, 44, 66–73.
Hoque, A.K.M.M., Amin, S.O., Alyyan, A., Zhang, B., Zhang, L., Wang, L.: NISR: named-data link state routing protocol. In: Proceedings of the 3rd ACM SIGCOMM workshop on Information-centric networking - ICN ’13. p. 15. ACM Press, Hong Kong, China (2013). https://doi.org/10.1145/2491224.2491231.
Marche, C., Atzori, L., Pilloni, V., & Nitti, M. (2020). How to exploit the social internet of things: Query generation model and device profiles’ dataset. Computer Networks., 174, 107248. https://doi.org/10.1016/j.comnet.2020.107248
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Yang, W., Qin, Y. & Wu, B. A hyperbolic routing scheme for information-centric internet of things with edge computing. Wireless Netw 27, 4567–4579 (2021). https://doi.org/10.1007/s11276-021-02751-7
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11276-021-02751-7