Skip to main content
U.S. flag

An official website of the United States government

Official websites use .gov
A .gov website belongs to an official government organization in the United States.

Secure .gov websites use HTTPS
A lock ( ) or https:// means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.

A 3D Topology Optimization Scheme for M2M Communications

Published

Author(s)

Yalong Wu, Wei Yu, Jin Zhang, David W. Griffith, Nada T. Golmie

Abstract

Without efficient topology management, M2M communications will likely asymmetrically congest gateways and eNodeBs in 3rd Generation Partnership Project (3GPP) Long-Term Evolution (LTE) and Long-Term Evolution Advanced (LTE-A) networks, especially when M2M devices are massively deployed to support diverse applications. To address this issue, in this paper we propose a 3D Topology Optimization (3D-TO) scheme to obtain the optimal placement of M2M gateways and eNodeBs. By taking advantage of the fact that most M2M devices rarely move, 3D-TO can specify optimal gateway positions for each M2M application, which consists of multiple M2M devices. This is achieved through global optimization, based on the distances between gateways and M2M devices. Utilizing the optimization process, 3D-TO likewise determines optimal eNodeB positions for each M2M application, based on the distances between eNodeBs and M2M devices optimal gateways. In our investigation, we perform a theoretical analysis and extensive simulation to demonstrate the effectiveness of our proposed 3D-TO scheme in M2M communications, with regard to throughput, delay, path loss, and packet loss ratio.
Proceedings Title
19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence,
Networking and Parallel/Distributed Computing (SNPD 2018)
Conference Dates
June 27-29, 2018
Conference Location
Busan, KR

Citation

Wu, Y. , Yu, W. , Zhang, J. , Griffith, D. and Golmie, N. (2018), A 3D Topology Optimization Scheme for M2M Communications, 19th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD 2018), Busan, KR, [online], https://doi.org/10.1109/SNPD.2018.8441113, https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=924578 (Accessed December 12, 2024)

Issues

If you have any questions about this publication or are having problems accessing it, please contact reflib@nist.gov.

Created June 28, 2018, Updated October 12, 2021