Measuring the impact of redirecting and offloading mobile data traffic

2013 ◽  
Vol 18 (1) ◽  
pp. 81-103 ◽  
Author(s):  
Robert A. Novo ◽  
Christopher J. Davolos ◽  
Z. John Zhao
Keyword(s):  
Data Traffic ◽  
Mobile Data ◽  
The Impact

scholarly journals Developing a Framework for Future Mobile Data Pricing

2017 ◽  
Vol 21 (2) ◽  
pp. 84 ◽  
Author(s):  
Michael Paetsch ◽  
Peter Dorčák ◽  
František Pollák ◽  
Ľubomír Štrba ◽  
Branislav Kršák
Keyword(s):  
Mobile Networks ◽  
Economic Value ◽  
Network Capacity ◽  
Data Traffic ◽  
Fixed Amount ◽  
Mobile Data ◽  
Data Value ◽  
Voice Calls ◽  
The Impact ◽  
New Framework

<p><strong>Purpose:</strong> The revenues for mobile data transmission overtook the revenue of voice calls for the first time in 2014 in the USA. It can be observed that demand for mobile data – largely driven by video and cloud - is increasing exponentially, while overall data revenue is rising only moderately. This will lead to insufficient revenues stream to increase investments into mobile networks and ensure quality service. Consequently, hereof network performance will deteriorate sharply. At the heart of the problem is the current global pricing regime of fixed multiple MB/GB bundles, irrespective of time of the day, intensity of usage (e.g. video vs. email) and underlying economic value of the data. A new framework is proposed as to optimize and align network capacity and implicit data value/utility, which is crucial to ensure customer satisfaction and access justice.</p><p><strong>Methodology/Approach:</strong> The fundamental differences in pricing voice and data in voice and/or data centric networks are analysed in detail. Information has been synthesized as to develop insights into the impact of different devises and type of digital traffic for the overall performance of mobile networks. Based hereupon, a new framework for mobile data has been proposed to address the increasing misalignment between network capacity, usage and underlying data value/utility. Initial solutions have been proposed and discussed.</p><p><strong>Findings:</strong> While voice calls are easily quantifiable and are largely predictable in its occurrence and network load implications, mobile data traffic shows very large variations depending on type of traffic. While social media messaging by many customers consumes very little capacity, consumption of video streaming by relatively few customers can lead already to network saturation.</p><p><strong>Research Limitation/implication:</strong> Carriers set prices for a fixed amount of data – irrespective of intensity and time of data traffic - which leads to sharp spiky type of traffic patterns essentially signalling sharp overuse during busy hours coexist with large period of underused times.</p><strong>Originality/Value of paper:</strong> A new framework for proposition building and particularly pricing of mobile data services is provided.

scholarly journals Efficiency Perspective on Telecom Mobile Data Traffic

2020 ◽  
Vol 5 (1) ◽  
pp. 38-44 ◽  
Author(s):  
Riko Hendrawan ◽  
Kristian WA Nugroho ◽  
Gayuh T Permana
Keyword(s):  
Service Providers ◽  
Negative Impact ◽  
Efficiency Score ◽  
Digital Transformation ◽  
Data Traffic ◽  
Mobile Data ◽  
Digital World ◽  
Dea Method ◽  
The Impact ◽  
Over The Top

Objective – The global industry is transforming into a digital world, evidenced by digital transformation performed by almost all of the industry sectors. One of the digital drivers is the support of connectivity provided by the telecommunication industry. The increasing mobile subscribers, along with the growth of mobile data traffic, is the sign of digital transformation itself. However, the rise of OTT (Over the Top) service providers tends to acquire the revenue share of the current telecom industry, seeing the trend of voice and SMS revenue that projected to decline. Methodology/Technique – This research is intended to measure the impact of increasing mobile data traffic that mostly caused by OTT services to telecom efficiency. The efficiency measurement & analysis were performed using the Stochastic Frontier Approach (SFA) & Data Envelopment Analysis (DEA) method. Findings – By using the SFA method, Maxis (Malaysia) got the highest efficiency score (0.98), followed by AIS (Thailand) with efficiency score 0.94 and Indosat Ooredoo (Indonesia) as the least efficient telecom provider (0.5). However, by using the DEA method, TLKM (Indonesia) got the highest efficient (0.98), and Celcom Axiata (Malaysia) was the least efficient (0.73/0.8). Novelty – The compelling results of this study are variable total asset variable had a significant negative impact on the efficiency score, and the variable of mobile data traffic was not significantly impacting the efficiency value (t-Ratio 0.71). Type of Paper: Empirical. Keywords: Telecom Operators; Efficiency; Mobile Data Traffic Reference to this paper should be made as follows: Hendrawan, R; Nugroho, K.W.A; Permana,G.T. 2019. Efficiency Perspective on Telecom Mobile Data Traffic, J. Bus. Econ. Review 5(1) 38 – 44 https://doi.org/10.35609/jber.2020.5.1(5) JEL Classification: M10, M15, M19.

scholarly journals Mobile Data Traffic Offloading through Opportunistic Vehicular Communications

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Liqiang Qiao
Keyword(s):  
Cellular Network ◽  
Maximization Problem ◽  
Vehicular Communications ◽  
Data Traffic ◽  
Reachability Graph ◽  
Mobile Data ◽  
Source Selection ◽  
Traffic Offloading ◽  
Future Utility ◽  
Initial Source

To cope with an exponentially increasing demand on mobile data traffic in cellular network, proximity-based opportunistic vehicular communications can be exploited as a complementary mean to offload and reduce the load of cellular network. In this paper, we propose a two-phase approach for mobile data traffic offloading, which exploits opportunistic contact and future utility with user mobility. The proposed approach includes one phase of initial source selection and subsequent phase of data forwarding. In phase 1, we build a weighted reachability graph, which is a very useful high-level abstraction for studying vehicular communication over time. Then, we propose an initial source selection algorithm, named VRank, and apply it in the weight reachability graph to identify some influential vehicles to serve as initial sources according to the rank of VRank. In phase 2, we formulate the forwarding schedule problem as a global utility maximization problem, which takes heterogeneous user interest and future utility contribution into consideration. Then, we propose an efficient scheme MGUP to solve the problem by providing a solution that decides which object should be broadcast. The effectiveness of our algorithm is verified through extensive simulation using real vehicular trace.

scholarly journals Performance evaluation of video streaming on LTE with coexistence of Wi-Fi signal

2019 ◽  
Vol 8 (3) ◽  
pp. 890-897
Author(s):  
Y. J. I. A. Yaqoob ◽  
W. L. Pang ◽  
S. K. Wong ◽  
K. Y. Chan
Keyword(s):  
Video Streaming ◽  
Network Performance ◽  
Software Radio ◽  
Radio Spectrum ◽  
Application Framework ◽  
Data Traffic ◽  
Continuous Growth ◽  
Lte Network ◽  
The Impact ◽  
Unlicensed Band

The continuous growth in mobile data traffic and limited license wireless spectrum have led to dramatically increase the demand of the radio spectrum. It is widespread the concern about the coexistence of long term evolution (LTE) and Wi-Fi in the unlicensed band. There are several techniques have been proposed to enable the coexistence of LTE and Wi-Fi in the unlicensed band, but these works are targeted on the impact of the LTE to the Wi-Fi network performance. An experiment is carried out in this work to evaluate the impact of Wi-Fi signal on the video streaming in the LTE network. The experimental test comprised of the national instrument (NI) universal software radio peripheral (USRP) 2953R that is controlled by the LabVIEW Communication LTE application framework. Extensiveexperiments are carried out under two scenarios, i.e. (1) Coexistence of LTE and Wi-Fi signal, (2) LTE signal only. Performance evaluations are carried out with different Modulation and coding schemes (MCS) values and different mode of operations, i.e. frequency division duplex (FDD) and time division duplex (TDD) mode. The results illustrated that the interference from Wi-Fi signal caused the performance degradation of the LTE network in throughput and the power received by user equipment (UE).

scholarly journals Mobile data traffic modeling: Revealing temporal facets

2017 ◽  
Vol 112 ◽  
pp. 176-193 ◽  
Author(s):  
Eduardo Mucelli Rezende Oliveira ◽  
Aline Carneiro Viana ◽  
K.P. Naveen ◽  
Carlos Sarraute
Keyword(s):  
Traffic Modeling ◽  
Data Traffic ◽  
Mobile Data

Determinants of Customers’ Brand Choice and Continuance Intentions with Mobile Data Service Provider: The Role of Past Experience

2018 ◽  
Vol 19 (6) ◽  
pp. 1478-1493 ◽  
Author(s):  
Obinna C. Ojiaku ◽  
Aihie Osarenkhoe
Keyword(s):  
Service Quality ◽  
Service Providers ◽  
Brand Image ◽  
Brand Choice ◽  
Past Experience ◽  
Mobile Service ◽  
Data Service ◽  
Mobile Data ◽  
Mobile Data Service ◽  
The Impact

The prevalence of mobile data services has intensified competition and structured the market for mobile telecommunication services, resulting in decreased revenue particularly from voices services and high churn rate. Mobile service providers therefore seek ways to increase revenue by attracting and retaining mobile data users. This study investigates the determinants of customers’ brand choice and continuance intentions with mobile service providers in the context of mobile data service. This study also captures the impact of past experience on behavioural outcomes. Data were collected from 304 customers of mobile data service firms and analysed using regression analyses. Results indicate that mobile service quality, pricing structure and promotion, but not brand image, affect brand choice, whereas continuance intentions are affected by the mobile service quality, brand image and price. Customers’ past experience relates significantly and negatively to brand choice but not continuance intention. The contribution and implication of the study are discussed.

Sponsored Data: Smarter Data Pricing with Incomplete Information

2021 ◽  
Author(s):  
Xiaowei Mei ◽  
Hsing Kenneth Cheng ◽  
Subhajyoti Bandyopadhyay ◽  
Liangfei Qiu ◽  
Lai Wei
Keyword(s):  
Consumer Surplus ◽  
Mobile Network ◽  
Complete Information ◽  
Consumer Welfare ◽  
High Definition ◽  
Mobile Data ◽  
Data Intensive ◽  
Data Consumption ◽  
New Business ◽  
The Impact

With the development of data-intensive internet services, the world has witnessed explosive growth in mobile data consumption during the last couple of years. The upcoming generation of 5G-capable phones and networks will continue and even accelerate that process. At the same time, consumers are becoming more conscious about their data consumption because their monthly caps of mobile data plans can be easily exhausted by premium content, such as high-definition videos and virtual-reality games. In response, the mobile network operators (MNOs) have proposed a new business model, the so-called sponsored data plans, to subsidize consumers by transferring at least part of the data bills from consumers to content providers. Although industry practitioners claim that sponsored data plans increase consumer welfare, our analysis reveals that the impact of sponsored data on consumer surplus depends crucially on whether the MNO has complete information of the consumers’ valuation of mobile data. Our analysis helps provide a clearer picture of the impact of sponsored data on consumer surplus while reconciling the conflicting views from scholars, digital rights groups, and the network carriers.

scholarly journals Spectrum Gap Analysis With Practical Solutions for Future Mobile Data Traffic Growth in Malaysia

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 24910-24933 ◽  
Author(s):  
Ibraheem Shayea ◽  
Marwan Hadri Azmi ◽  
Tharek Abd. Rahman ◽  
Mustafa Ergen ◽  
Chua Tien Han ◽  
...  
Keyword(s):  
Gap Analysis ◽  
Data Traffic ◽  
Mobile Data ◽  
Traffic Growth
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