Extending broadband Internet access to unserved and underserved populations is one of the core pillars of telecommunication development. Differing physical and economic environments in which service providers must operate is aimed at making it possible to deploy and successfully operate ICT network infrastructures. Putting in place the right and appropriate tools to foster infrastructure deployment is vital to promoting digital inclusion through universal access to fast, reliable online technologies and services. This article as a part of the Broadband Connectivity Toolkit Methodologies Series describes methodology for choosing the best network topology for the multiple objects network (network of localities, schools, hospitals etc.). The described algorithm implies the idea of larger NPV (cost of ownership) for optimal path between two or more objects (localities). The initial data for whole algorithm are geographical coordinates (longitude & latitude) of investigated objects; geographical coordinates of the nearest backbone point (if available); required capacity for investigated objects; and costs of infrastructure facilities deployment and maintenance.

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Methodology for Choosing the Best Network Topology for the Multiple Objects Network

  • Vadym Kaptur,
  • Volodymir Baliar,
  • Olena Mazurkiewicz

摘要

Extending broadband Internet access to unserved and underserved populations is one of the core pillars of telecommunication development. Differing physical and economic environments in which service providers must operate is aimed at making it possible to deploy and successfully operate ICT network infrastructures. Putting in place the right and appropriate tools to foster infrastructure deployment is vital to promoting digital inclusion through universal access to fast, reliable online technologies and services. This article as a part of the Broadband Connectivity Toolkit Methodologies Series describes methodology for choosing the best network topology for the multiple objects network (network of localities, schools, hospitals etc.). The described algorithm implies the idea of larger NPV (cost of ownership) for optimal path between two or more objects (localities). The initial data for whole algorithm are geographical coordinates (longitude & latitude) of investigated objects; geographical coordinates of the nearest backbone point (if available); required capacity for investigated objects; and costs of infrastructure facilities deployment and maintenance.