Scipediacontent: Scipediacontent moved page Draft Content 991263481 to Sliwa et al 2018a

2021-01-28T16:52:58Z

Scipediacontent moved page Draft Content 991263481 to Sliwa et al 2018a

Scipediacontent: Created page with " == Abstract == Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of sta..."

2021-01-28T16:52:55Z

Created page with " == Abstract == Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of sta..."

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== Abstract ==

Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of static and mobile Internet of Things (IoT) systems. Cars acting as mobile sensors will become important data sources for cloud-based applications like predictive maintenance and dynamic traffic forecast. Due to the limitation of available communication resources, it is expected that the grows in Machine-Type Communication (MTC) will cause severe interference with Human-to-human (H2H) communication. Consequently, more efficient transmission methods are highly required. In this paper, we present a probabilistic scheme for efficient transmission of vehicular sensor data which leverages favorable channel conditions and avoids transmissions when they are expected to be highly resource-consuming. Multiple variants of the proposed scheme are evaluated in comprehensive realworld experiments. Through machine learning based combination of multiple context metrics, the proposed scheme is able to achieve up to 164% higher average data rate values for sensor applications with soft deadline requirements compared to regular periodic transmission.

Comment: Best Student Paper Award

== Original document ==

The different versions of the original document can be found in:

* [http://arxiv.org/abs/1801.03290 http://arxiv.org/abs/1801.03290]

* [http://arxiv.org/pdf/1801.03290 http://arxiv.org/pdf/1801.03290]

* [http://dx.doi.org/10.1109/vtcspring.2018.8417753 http://dx.doi.org/10.1109/vtcspring.2018.8417753]

* [http://xplorestaging.ieee.org/ielx7/8417184/8417457/08417753.pdf?arnumber=8417753 http://xplorestaging.ieee.org/ielx7/8417184/8417457/08417753.pdf?arnumber=8417753],
: [http://dx.doi.org/10.1109/vtcspring.2018.8417753 http://dx.doi.org/10.1109/vtcspring.2018.8417753]

* [https://dblp.uni-trier.de/db/journals/corr/corr1801.html#abs-1801-03290 https://dblp.uni-trier.de/db/journals/corr/corr1801.html#abs-1801-03290],
: [https://arxiv.org/pdf/1801.03290 https://arxiv.org/pdf/1801.03290],
: [https://arxiv.org/abs/1801.03290 https://arxiv.org/abs/1801.03290],
: [https://ui.adsabs.harvard.edu/abs/2018arXiv180103290S/abstract https://ui.adsabs.harvard.edu/abs/2018arXiv180103290S/abstract],
: [https://au.arxiv.org/pdf/1801.03290 https://au.arxiv.org/pdf/1801.03290],
: [https://aps.arxiv.org/pdf/1801.03290 https://aps.arxiv.org/pdf/1801.03290],
: [https://export.arxiv.org/pdf/1801.03290 https://export.arxiv.org/pdf/1801.03290],
: [https://jp.arxiv.org/abs/1801.03290 https://jp.arxiv.org/abs/1801.03290],
: [https://au.arxiv.org/abs/1801.03290 https://au.arxiv.org/abs/1801.03290],
: [https://fr.arxiv.org/pdf/1801.03290 https://fr.arxiv.org/pdf/1801.03290],
: [https://ru.arxiv.org/pdf/1801.03290 https://ru.arxiv.org/pdf/1801.03290],
: [http://export.arxiv.org/abs/1801.03290 http://export.arxiv.org/abs/1801.03290],
: [https://academic.microsoft.com/#/detail/2783347352 https://academic.microsoft.com/#/detail/2783347352]

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Sliwa et al 2018a - Revision history

Scipediacontent: Scipediacontent moved page Draft Content 991263481 to Sliwa et al 2018a

Scipediacontent: Created page with " == Abstract == Upcoming 5G-based communication networks will be confronted with huge increases in the amount of transmitted sensor data related to massive deployments of sta..."