Device-Specific Traffic Characterization for Root Cause Analysis in Cellular Networks

Romirer-Maierhofer, Peter; Schiavone, Mirko; D’Alconzo, Alessandro

doi:10.1007/978-3-319-17172-2_5

Cited by 19 publications

(20 citation statements)

References 14 publications

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“…Ref. [4] confirmed these last two observations by analyzing IoT device data collected over several weeks in 2013. Both studies concluded that the traffic generated by IoT devices significantly differs from smartphones, indicating the need for MNOs to reassess network planning traditionally optimized for smartphone users.…”

Section: Related Workmentioning

confidence: 59%

“…Empirically we find that only 1.6% of IMSIs were used with multiple devices over the entire period. 3 TOL2008 is based on the EU's classification of economic activities, NACE Rev.2, prescribed in the EC Regulation (EC) no1893/2006 4 The moving average is essentially a low-pass filter in signal processing. Regarding different industries Figure 2 shows the four-week moving average of traffic per device by industry.…”

Section: A Traffic Statisticsmentioning

confidence: 99%

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Cellular IoT Traffic Characterization and Evolution

Finley¹,

Vesselkov²

2019

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

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The adoption of Internet of Things (IoT) technologies is increasing and thus IoT is seemingly shifting from hype to reality. However, the actual use of IoT over significant timescales has not been empirically analyzed. In other words the reality remains unexplored. Furthermore, despite the variety of IoT verticals, the use of IoT across vertical industries has not been compared. This paper uses a two-year IoT dataset from a major Finnish mobile network operator to investigate different aspects of cellular IoT traffic including temporal evolution and the use of IoT devices across industries. We present a variety of novel findings. For example, our results show that IoT traffic volume per device increased three-fold over the last two years. Additionally, we illustrate diversity in IoT usage among different industries with orders of magnitude differences in traffic volume and device mobility. Though we also note that the daily traffic patterns of all devices can be clustered into only three patterns, differing mainly in the presence and timing of a peak hour. Finally, we illustrate that the share of LTE-enabled IoT devices has remained low at around 2% and 30% of IoT devices are still 2G only.

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Section: Related Workmentioning

confidence: 59%

Section: A Traffic Statisticsmentioning

confidence: 99%

Cellular IoT Traffic Characterization and Evolution

Finley¹,

Vesselkov²

2019

2019 IEEE 5th World Forum on Internet of Things (WF-IoT)

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“…Because satellite coverage varies geographically with the movement of constellations and because the traffic intensity in the coverage areas changes temporally, inter-satellite traffic demand is time varying. However, because the geographical variation of the satellite's coverage area is cyclical and deterministic and because the temporal variation in aggregate IoT traffic intensity between two certain areas exhibits an evident daily pattern, 28,29 the evolution of inter-satellite traffic demand presents a clear deterministic and periodic trend. In addition, the period of this trend can be obtained by calculating the smallest common integer multiple of the satellite orbital period, the Earth's rotation period, and the IoT traffic intensity period.…”

Section: Predictable Long-range Baselinementioning

confidence: 99%

“…Their traffic requirement, which is closely related to geographical distribution of IoT devices, the daily evolution of traffic intensity, and the coverage relationship between satellites and geographical zones show strong periodical and predictable features. [28][29][30] This suggests that the amount of traffic that needs to be routed by each satellite at a particular time can be approximately estimated. Henceforth, load balancing routing schemes should take this into account and take measures in advance to prevent traffic concentration as opposed to reacting to the onset of congestion.…”

Section: Introductionmentioning

confidence: 99%

HGL: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks

Liu

Wang

et al. 2017

International Journal of Distributed Sensor Networks

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Satellite networks provide complete connectivity and worldwide data transmission capability for constructing the Internet of Things. However, because of the varying Internet of Things traffic density, satellite networks may endure imbalanced traffic requirements and frequent link congestion. To effectively resolve these problems and optimally transmit Internet of Things data, a novel hybrid global-local load balancing routing scheme for Low Earth Orbit satellite networks is proposed in this article. Hybrid global-local load balancing routing scheme enables satellites to route Internet of Things traffic through global planning and local real-time adjustments in two steps. In hybrid global-local load balancing routing scheme, given the predictive nature of Internet of Things traffic distribution and Low Earth Orbit satellite networks, the inter-satellite traffic demand is decomposed into a predictable long-range baseline and unpredictable shortrange fluctuations. A global strategy is employed first for preliminary global traffic allocation based on long-range baselines, and a local strategy is then employed for route adjustments based on short-range fluctuations. With the combination of global planning and local real-time adjustments, network traffic can eventually obtain a near-optimal allocation. Numerical simulations indicate that in contrast to single-strategy schemes, hybrid global-local load balancing routing scheme can more thoroughly eliminate congestion, and it performs better in measures such as packet loss rate, average queuing delay, traffic distribution, route oscillation, and communication overhead.

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“…Such growth shows that cellular networks will have to accommodate an increasing number of devices used for various purposes in various industries, which may have different requirements, and generate new types of traffic patterns. Despite the pressing need to prepare for this change, only a few studies have empirically analyzed IoT traffic to better understand the use of mobile IoT communications (Marjamaa, 2012;Romirer-Maierhofer et al, 2015;Shafiq et al, 2012). These studies, however, did not investigate the differences in service usage between various customer groups or industries, which indicates the need for further research.…”

Section: Introductionmentioning

confidence: 99%

Technology and value network evolution in telehealth

Vesselkov

Hämmáinen

Töyli

2018

Technological Forecasting and Social Change

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Vesselkov, Hämmäinen, and Töyli formed the idea, constructed models and scenarios. Vesselkov conducted the interviews and wrote the paper. Hämmäinen and Töyli edited the text.Publication IV: "Design and governance of mHealth data sharing" Vesselkov formed the idea, collected the data, conducted interviews and wrote the paper. Hämmäinen and Töyli provided comments on the analysis and edited the text.Publication V: "Internet of things (IoT) platform competition: consumer switching versus provider multihoming" Basaure and Vesselkov formed the idea. Basaure designed the model, run the simulations, and wrote most of the paper. Vesselkov supported the modeling, wrote Sections 2.5 and 2.6, commented other sections, and edited the text. Töyli provided comments on the analysis and edited the text. Publication VI: "Technology and Value Network Evolution in Telehealth"Vesselkov, Hämmäinen, and Töyli formed the idea. Vesselkov collected the data and wrote the paper. Hämmäinen and Töyli provided comments on the analysis and edited the text. RQ1. How do mobile IoT usage patterns differ from consumer devices and across industries?RQ2. How could embedded SIM technology affect the mobile ecosystem? RQ3. How could collaboration and competition be governed in emerging consumer IoT data platforms and particularly mobile health data platforms? RQ4. How could mobile platform providers acting as mobile health data platform providers position themselves in the ecosystem of the healthcare industry?

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Device-Specific Traffic Characterization for Root Cause Analysis in Cellular Networks

Cited by 19 publications

References 14 publications

Cellular IoT Traffic Characterization and Evolution

Cellular IoT Traffic Characterization and Evolution

HGL: A hybrid global-local load balancing routing scheme for the Internet of Things through satellite networks

Technology and value network evolution in telehealth

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