Examining categorization of Telecom Circles in India using unsupervised k-means clustering on techno-economic indicators

Jha, Ashutosh; Saha, Debashis

doi:10.1007/s40622-019-00225-6

Cited by 5 publications

(5 citation statements)

References 26 publications

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“…We have classified the literature related to Indian telecom into six broad classes, such as (i) role of Telecom Regulatory (Prasad et.al., 2009 , Srivastava & Sinha, 2001 , Sridhar & Prasad, 2011 , Dokeniya, 1999 , Jain, 2006 ), (ii) Impact of Telecom policy on growth of mobile telephony (Athreya ( 1996 )), (iii) Diffusion of mobile phones vis-à-vis fixed lines (Gupta, and Jain, 2012 , K. Singh, 2008 , Sridhar, ( 2010 ), Biancini, 2011 ), (iv) Factors impacting the adoption of mobile telephony in rural areas (Gupta and Jain, 2014 , Gupta & Jain, 2014 ), (v) Modeling the demand of mobile and fixed lines (Narayana, 2009 , 2010, 2011 ) and Pricing of services for mobile telephony (Das & Srinivasan, 1999 ), (vi) recent studies like re-segmentation of the mobile circles, forecasting the growth of 3G, 4G and mobile broadband in India (A Jha et al ( 2019 ), A Jha et al ( 2020 ), A Jha et al ( 2021 )). Table 2 lists the papers from 1999 to 2021, along with the focus area and the research issues studied.…”

Section: Literature Summarymentioning

confidence: 99%

“… P. Das & P. Srinivasan ( 1999 ). Pricing of telecom services in India Estimate the price elasticity between local and long distance call A Jha et al ( 2019 ) Redefine the telecom circles in India Using Human Development Index, Gross Domestic Product, and Tele density indicator data and using clustering algorithm the telecom circles have been redefined. A Jha et al ( 2020 ) Forecast growth rates of 3G and 4G services in India Using non-linear regression techniques the growth rate of 3G and 4G mobile subscribers have been forecasted.…”

Section: Literature Summarymentioning

confidence: 99%

“…Only baring West Bengal in the eastern India is a B circle. While Delhi, Mumbai and Kolkata are marked as metro circles (Jha et al, 2019 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Exploring the Main Factors Affecting Mobile Phone Growth Rates in Indian States

2023

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This paper investigates the growth of mobile telecommunications across the 23 circles (or states) in India for the period 2005–2021. From this study, we note that diffusion of mobile phones in Indian circles (or states) was influenced by the telecommunication policy of the Indian government. We noted that the growth rates of mobile telephony in all the 23 states followed a logistic model. From this, we inferred that some of the Indian states, with low GDP per capita income started rolling out mobile telephony operations later than states with high GDP per capita income, but could catch with the later. It also follows that states with more rural regions had more adoption of mobile telephony. From this study, we can inform policy makers and telecom providers that electrical power consumption, Human Development Index (HDI), Herfindahl Index (HHI) and proportion of rural to urban ratio do not form an important parameter for mobile growth.

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Section: Literature Summarymentioning

confidence: 99%

Section: Literature Summarymentioning

confidence: 99%

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Exploring the Main Factors Affecting Mobile Phone Growth Rates in Indian States

2023

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“…Where,

{italicSNxCert}_{italicSNp}

is the certificate issued by

SNx

to certify the public key of

SNp

. In a large country like India with 22 Telecom circles, 37

C

will have at the most 21 certificates to facilitate domestic roaming.…”

Section: The Proposed Enhancement To the Eciesmentioning

confidence: 99%

Enhancing the Elliptic Curve Integrated Encryption Scheme in 5G mobile network for home network identity privacy

Choudhury

2022

Security and Privacy

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In mobile network, ensuring privacy to the subscriber is an obligation of the mobile operator. It is the responsibility of the operator to ensure that the private information about the subscriber like his/her identity does not get compromised.While adopting technologies with regards to issues like this, operators mostly follow specifications standardized by the third Generation Partnership Project (3GPP). In a recent technical specification published by 3GPP for 5G mobile network, an Elliptic Curve Integrated Encryption Scheme (ECIES) is adopted for identity privacy. However, this scheme does not emphasize on protecting the identity of the home network with which the subscriber is registered. This may make the subscriber vulnerable to tracking by an adversary in a visited network through his/her home network identity. In this paper, we propose an enhanced scheme called scheme for home network identity privacy. Through a formal and an informal security analysis, the proposed scheme is found to be secured. Through a performance analysis, it is found that the scheme achieves its objective of securing the home network identity of the subscriber at the expense of 47% additional computation at the UE in comparison with the execution time that is incurred by ECIES.

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“…Penerapan Metode K-Means Clustering Untuk Menentukan Status Negara 26 periode [13][14]. K-Means juga digunakan untuk mengklaster di beberapa penelitian di berbagai bidang, misalnya untuk clustering cybercrime [15], [16], kelompok pelanggan [8], bidang kesehatan [17]- [21], industri manufaktur [22], dan techno-economic [23], [24]. Selanjutnya, dalam studi kasus pada penelitian kali ini, penulis bertujuan menentukan kategori atau klaster status dari sebuah negara dengan menggunakan metode K-Means clustering.…”

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Penerapan Metode K-Means Clustering Untuk Menentukan Status Negara

Nurdin¹,

Jamilah²,

Mustaib³

2022

JDI

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Status dari sebuah negara merupakan hal yang penting untuk diketahui. Hal tersebut dikarenakan status negara dapat menunjang kesuksesan suatu investasi, bisnis, dan juga pemerataan pembangunan oleh pemerintahnya. Status negara yang sering didengar oleh masyarakat, yakni negara maju dan negara berkembang. Dalam studi kasus penentuan status negara ini, penulis mengambil dataset International Monetary Fund (IMF) yang diperoleh dari situs github. Kemudian data mentah dilakukan preproccessing terlebih dahulu berupa impute values dan normalisasi, lalu selanjutnya dilakukan perhitungan klastering dengan metode K-Means. Data akan dibagi menjadi empat klaster dan perhitungan dilakukan dengan dua cara, yakni melalui Microsoft Excel dan Rapid Miner. Berdasarkan perhitungan yang telah dilakukan, 186 negara sudah terbagi menjadi empat klaster yakni, klaster negara agraris, negara industri, negara berkembang, dan negara maju. Penentuan klasterisasi status negara tersebut cukup dikatakan valid karena performance vectornya sebesar 70%.

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Examining categorization of Telecom Circles in India using unsupervised k-means clustering on techno-economic indicators

Cited by 5 publications

References 26 publications

Exploring the Main Factors Affecting Mobile Phone Growth Rates in Indian States

Exploring the Main Factors Affecting Mobile Phone Growth Rates in Indian States

Enhancing the Elliptic Curve Integrated Encryption Scheme in 5G mobile network for home network identity privacy

Penerapan Metode K-Means Clustering Untuk Menentukan Status Negara

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