TUBE: time-dependent pricing for mobile data

By Sangtae Ha, Soumya Sen, Carlee Joe-Wong, Youngbin Im, Mung Chiang. Conference on Applications, Technologies, Architectures, and Protocols for Computer Communication, 2012

297 citations recorded by the source corpus.

The two largest U.S. wireless ISPs have recently moved towards usage-based pricing to better manage the growing demand on their networks. Yet usage-based pricing still requires ISPs to over-provision capacity for demand at peak times of the day. Time-dependent pricing (TDP) addresses this problem by considering when a user consumes data, in addition to how much is used. We present the architecture, implementation, and a user trial of an end-to-end TDP system called TUBE. TUBE creates a price-based feedback control loop between an ISP and its end users. On the ISP side, it computes TDP prices so as to balance the cost of congestion during peak periods with that of offering lower prices in less congested periods. On mobile devices, it provides a graphical user interface that allows users to respond to the offered prices either by themselves or using an "autopilot" mode. We conducted a pilot TUBE trial with 50 iPhone or iPad 3G data users, who were charged according to our TDP algorithms. Our results show that TDP benefits both operators and customers, flattening the temporal fluctuation of demand while allowing users to save money by choosing the time and volume of their usage.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Media Technology

View Paper: https://www.semanticscholar.org/paper/f617455fa339959a9f8ae9a01a057c02d7b1b90e

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Citations in the corpus

Machine Learning for Wireless Networks with Artificial Intelligence: A Tutorial on Neural Networks (2017, background): “For instance, ANNs can be trained over large datasets which take into account the variations of the traffic load over several days for scenarios in which the traffic load of WiFi access points (WAPs) can be characterized based on a particular traffic model [208].”
Artificial Neural Networks-Based Machine Learning for Wireless Networks: A Tutorial (2017, background): “which take into account the variations of the trafﬁc load over several days for scenarios in which the trafﬁc load of WiFi access points (WAPs) can be characterized based on a particular trafﬁc model [208]. Here, note that cellular data trafﬁc networks exhibit statistically ﬂuctuating and periodic d”
Proactive Resource Management for LTE in Unlicensed Spectrum: A Deep Learning Perspective (2017, background): “tions such as file transfer, video streaming and browsing [12], therefore providing an opportunity for the network to exploit”