A First Look at SIM-Enabled Wearables in the Wild

Kolamunna, Harini; Leontiadis, Ilias; Perino, Diego; Seneviratne, Suranga; Thilakarathna, Kanchana; Seneviratne, Aruna

doi:10.1145/3278532.3278540

Cited by 9 publications

(3 citation statements)

References 19 publications

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“…Existing works on IoT mainly focus on understanding the traffic pattern of IoT devices [8], [12]- [14], [38], communication model in LET networks [15]- [18] and the IoT security problems [19]- [21]. Mobility of IoT devices are rarely considered.…”

Section: Data-driven Iot Analysismentioning

confidence: 99%

IoT vs. Human: A Comparison of Mobility

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

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Internet of Thing (IoT) devices are rapidly becoming an indispensable part of our life with their increasing deployment in many promising areas, including tele-health, smart city, intelligent agriculture. Understanding the mobility of IoT devices is essential to improve quality of service in IoT applications, such as route planning in logistic management, infrastructure deployment, cellular network update and congestion detection in intelligent traffic. Despite its importance, there are not many results pertaining to the mobility of IoT devices. In this article, we aim to answer three research questions: (i) what are the mobility patterns of IoT device? (ii) what are the differences between IoT device and smartphone mobility patterns? (iii) how the IoT device mobility patterns differ among device types and usage scenarios? We present a comprehensive characterization of IoT device mobility patterns from the perspective of cellular data networks, using a 36-days long signal trace, including 1.5 million IoT devices and 0.425 million smartphones, collected from a nation-wide cellular network in China. We first investigate the basic patterns of IoT devices from two perspectives: temporal and spatial characteristics. Our study finds that IoT device mobility exhibits significantly different patterns compared with smartphones in multiple aspects. For instance, IoT devices move more frequently and have larger radius of gyration. Then we explore the essential mobility of IoT devices by utilizing two models that reveal the nature of human mobility, i.e., exploration and preferential return (EPR) model and entropy based predictability model. We find that IoT devices, with few exceptions, behave totally different from human, and we further derive a new formulation to describe their movement. We also find the gap mobility predictability and predictability limit between IoT and human is not as big as people expected.

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Section: Data-driven Iot Analysismentioning

confidence: 99%

IoT vs. Human: A Comparison of Mobility

Wang

et al. 2022

IEEE Trans. on Mobile Comput.

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“…There is a large body of work on IoT/M2M traffic. In particular, numerous research shows that IoT devices generate traffic with significantly different patterns from human-driven mobile devices in the cellular networks [8,15,16,27]. Based on such observations, Markus et al [17] tried to fabricate M2M traffic models, and the other researchers design future systems that can efficiently handle these traffic [2,24].…”

Section: Related Workmentioning

confidence: 99%

Insights from operating an IP exchange provider

Lutu

Perino

Bagnulo

et al. 2021

Proceedings of the 2021 ACM SIGCOMM 2021 Conference

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IP Exchange Providers (IPX-Ps) offer to their customers (e.g., mobile or IoT service providers) global data roaming and support for a variety of emerging services. They peer to other IPX-Ps and form the IPX network, which interconnects 800 MNOs worldwide offering their customers access to mobile services in any other country. Despite the importance of IPX-Ps, little is known about their operations and performance. In this paper, we shed light on these opaque providers by analyzing a large IPX-P with more than 100 PoPs in 40+ countries, with a particularly strong presence in America and Europe. Specifically, we characterize the traffic and performance of the main infrastructures of the IPX-P (i.e., 2-3-4G signaling and GTP tunneling), and provide implications for its operation, as well as for the IPX-P's customers. Our analysis is based on statistics we collected during two time periods (i.e., prior and during COVID-19 pandemic) and includes insights on the main service the platform supports (i.e., IoT and data roaming), traffic breakdown and geographical/temporal distribution, communication performance (e.g., tunnel setup time, RTTs). Our results constitute a step towards advancing the understanding of IPX-Ps at their core, and provide guidelines for their operations and customer satisfaction.

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“…They further concluded that the large-scale over-the-air updates of car firmware could seriously harm network performance. Moreover, Kolamunna et al (2018) took the first look on SIM-enabled wearable devices, namely smartwatches, and analyzed their traffic and usage patterns based on sevenmonth-long data from a large European MNO. The authors found that only 34% users of SIMenabled wearables generate any network traffic, although such users are more active in terms of mobility, data consumption, and app usage than users that do not own a SIM-enabled wearable device.…”

Section: New Customers and Traffic Patternsmentioning

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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A First Look at SIM-Enabled Wearables in the Wild

Cited by 9 publications

References 19 publications

IoT vs. Human: A Comparison of Mobility

IoT vs. Human: A Comparison of Mobility

Insights from operating an IP exchange provider

Technology and value network evolution in telehealth

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