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Open AccessArticle

A Statistical Performance Analysis of Named Data Ultra Dense Networks

1
Department of Electronics & Computer Engineering, Hongik University, Sejong City 30016, Korea
2
College of Science and Technology, Hongik University, Sejong City 30016, Korea
3
Department of Software and Communications Engineering, Hongik University, Sejong City 30016, Korea
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(18), 3714; https://doi.org/10.3390/app9183714
Received: 28 June 2019 / Revised: 30 August 2019 / Accepted: 3 September 2019 / Published: 6 September 2019
Named data networking (NDN) is a novel communication paradigm that employs names rather than references to the location of the content. It exploits in-network caching among different nodes in a network to provide the fast delivery of content. Thus, it reduces the backhaul traffic on the original producer and also eliminates the need for a stable connection between the source (consumer) and destination (producer). However, a bottleneck or congestion may still occur in very crowded areas, such as shopping malls, concerts, or stadiums, where thousands of users are requesting information from a device that resides at the edge of the network. This paper provides an analysis of content delivery in terms of the interest satisfaction rate (ISR) in ultra-dense network traffic situations and presents a final and an adequate statistical model based on multiple linear regression (MLR) to enhance ISR. A four-way factorial design was used to generate the dataset by performing simulations in ndnSIM. The results show that there is no significant interaction between four predictors: number of nodes (NN), number of interests (NI) per second, router bandwidth (RB), and router delay (RD). Moreover, the NI has a negative effect, and log(RB) has a positive effect on the ISR. The NN less than 10 has a significantly higher effect on the ISR compared with other nodes’ densities. View Full-Text
Keywords: named data networking; ultra-dense networks; Internet of Things; four-way factorial design; main and interaction effects; multiple linear regression named data networking; ultra-dense networks; Internet of Things; four-way factorial design; main and interaction effects; multiple linear regression
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Rehman, M.A.U.; Kim, D.; Choi, K.; Ullah, R.; Kim, B.S. A Statistical Performance Analysis of Named Data Ultra Dense Networks. Appl. Sci. 2019, 9, 3714.

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