Impact Analysis of Realistic Human Mobility over Wireless Network
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Publisher
Springer International Publishing
Link
http://link.springer.com/content/pdf/10.1007/978-3-030-44751-9_28
Reference42 articles.
1. Crawdad. CRAWDAD data set ncsu/mobilitymodels. Accessed 23 July 2009
2. Alsaeedy, A.A.R., Chong, E.K.P.: Mobility management for 5G IoT devices: Improving power consumption with lightweight signaling overhead. IEEE Internet Things J. 6(5), 8237–8247 (2019). https://doi.org/10.1109/JIOT.2019.2920628
3. Balasubramanian, V., Zaman, F., Aloqaily, M., Ridhawi, I.A., Jararweh, Y., Salameh, H.B.: A mobility management architecture for seamless delivery of 5G-IoT services. In: ICC 2019–2019 IEEE International Conference on Communications (ICC), pp. 1–7, May 2019. https://doi.org/10.1109/ICC.2019.8761658
4. Bolla, R., Repetto, M.: A comprehensive tutorial for mobility management in data networks. IEEE Commun. Surv. Tutor. 16(2), 812–833 (2014). https://doi.org/10.1109/SURV.2013.071913.00140
5. Chan, H., Liu, D., Seite, P., Yokota, H., Korhonen, J., (eds.): Requirements for Distributed Mobility Management. RFC 7333 (Informational), August 2014. https://doi.org/10.17487/RFC7333 . https://www.rfc-editor.org/rfc/rfc7333.txt
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