Page 35 - ITU KALEIDOSCOPE, ATLANTA 2019

ICT for Health: Networks, standards and innovation

Table 1 – Backbone network topology vs sparse network topology

Network performance Reduced network Backbone
Node degree 3.81 4.03
Coefficient of variation (link margin-(dBm)) 2.83 3.86
Shortest distance (km) 12.88 12.31
Path multiplicity 2 1

9, the network backbone decreased towards a convergence. 7. CONCLUSION
However, the decrease is slower and hence the backbone size
converges to a higher number of nodes. In this paper, design challenges expected to be met when
designing mesh networks using opportunistic access to the
Considering the effect of shortest distance between nodes, white space frequencies were explored and discussed. Dense
Figure 11 shows a different trend. The size of backbone network topology was highlighted as one of the design
increased in general until it converges to a maximum. challenges that network planners and designers in white space
frequencies will face and the paper focused on addressing
this challenge. A link-based topology reduction algorithm
The conclusions drawn from the three graphs depicting has been developed to reduce a dense mesh network topology
impact of the design parameters on the backbone size are designed in white space frequencies into sparse mesh network
as follows: the backbone size is affected by change of each of topology and a network optimization function based on
the three parameters. These results also reveal that the node three metrics has been developed to introduce hierarchical
degree has a much higher positive influence on the backbone backbone-based network topology from the sparse network
size, leading to smaller backbones, which can allow networks topology. Performance evaluation on the designs were carried
to scale while keeping the size of the backbone constant and out and the results show that the designs can guide network
smaller. engineers to select the most relevant performance metrics
during a network feasibility study in white space frequencies,
aimed at guiding the implementation process.

REFERENCES
[1] P. Cui, Y. Dong, H. Liu, D. Rajan, E. Olinick, and
J. Camp, “Whitemesh: Leveraging white spaces in
wireless mesh networks,” In 2016 14th International
Symposium on Modeling and Optimization in Mobile,
Ad Hoc, and Wireless Networks (WiOpt), pp. 1-7. IEEE,
2016.
[2] L. Uscher-Pines, K. Bouskill, J. Sousa, M. Shen, and
S.H. Fischer. Experiences of Medicaid Programs and
Health Centers in Implementing Telehealth. RAND,

Figure 10 – Impact of β on backbone size 2019.
[3] The App Association, “Tuning into Telehealth: How
TV White Spaces Can Help Mississippi Tackle the
Diabetes Epidemic,” 2017. http://actonline.org/2017/
07/20/tuning-into-telehealth-how-tv-white-spaces-
can-help-mississippi-tackle-the-diabetes-epidemic/.
[4] A. Chavez, R. Littman-Quinn, K. Ndlovu, and C.L.
Kovarik, “Using TV white space spectrum to practise
telemedicine: A promising technology to enhance
broadband internet connectivity within healthcare
facilities in rural regions of developing countries,”
Journal of telemedicine and telecare 22, no. 4 (2016):
260-263.

[5] J. Zhao, H. Zheng, and G. Yang, “Distributed
coordination in dynamic spectrum allocation networks,”
Figure 11 – Impact of λ on backbone size In First IEEE International Symposium on New

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