Optimal Pricing of Internet of Things: A Machine Learning Approach

Alsheikh, MA; Hoang, DT; Niyato, D; Leong, D; Wang, P; Han, Z

Optimal Pricing of Internet of Things: A Machine Learning Approach

Alsheikh, MA Hoang, DT Niyato, D Leong, D Wang, P Han, Z

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Publisher:: Institute of Electrical and Electronics Engineers (IEEE)
Publication Type:: Journal Article
Citation:: IEEE Journal on Selected Areas in Communications, 2020, 38, (4), pp. 669-684
Issue Date:: 2020-04-01

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Field	Value	Language
dc.contributor.author	Alsheikh, MA
dc.contributor.author	Hoang, DT
dc.contributor.author	Niyato, D
dc.contributor.author	Leong, D
dc.contributor.author	Wang, P
dc.contributor.author	Han, Z
dc.date.accessioned	2021-03-23T00:37:09Z
dc.date.available	2021-03-23T00:37:09Z
dc.date.issued	2020-04-01
dc.identifier.citation	IEEE Journal on Selected Areas in Communications, 2020, 38, (4), pp. 669-684
dc.identifier.issn	0733-8716
dc.identifier.issn	1558-0008
dc.identifier.uri	http://hdl.handle.net/10453/147455
dc.description.abstract	© 1983-2012 IEEE. Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.
dc.language	en
dc.publisher	Institute of Electrical and Electronics Engineers (IEEE)
dc.relation.ispartof	IEEE Journal on Selected Areas in Communications
dc.relation.isbasedon	10.1109/JSAC.2020.2971898
dc.rights	info:eu-repo/semantics/embargoedAccess
dc.rights	© 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.subject	0805 Distributed Computing, 0906 Electrical and Electronic Engineering, 1005 Communications Technologies
dc.subject.classification	Networking & Telecommunications
dc.title	Optimal Pricing of Internet of Things: A Machine Learning Approach
dc.type	Journal Article
utslib.citation.volume	38
utslib.for	0805 Distributed Computing
utslib.for	0805 Distributed Computing
utslib.for	0906 Electrical and Electronic Engineering
utslib.for	1005 Communications Technologies
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - GBDTC - Global Big Data Technologies
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
utslib.copyright.status	open_access	*
utslib.copyright.embargo	2022-04-01T00:00:00+1000Z
dc.date.updated	2021-03-23T00:37:05Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	38
utslib.citation.issue	4

Abstract:

© 1983-2012 IEEE. Internet of things (IoT) produces massive data from devices embedded with sensors. The IoT data allows creating profitable services using machine learning. However, previous research does not address the problem of optimal pricing and bundling of machine learning-based IoT services. In this paper, we define the data value and service quality from a machine learning perspective. We present an IoT market model which consists of data vendors selling data to service providers, and service providers offering IoT services to customers. Then, we introduce optimal pricing schemes for the standalone and bundled selling of IoT services. In standalone service sales, the service provider optimizes the size of bought data and service subscription fee to maximize its profit. For service bundles, the subscription fee and data sizes of the grouped IoT services are optimized to maximize the total profit of cooperative service providers. We show that bundling IoT services maximizes the profit of service providers compared to the standalone selling. For profit sharing of bundled services, we apply the concepts of core and Shapley solutions from cooperative game theory as efficient and fair allocations of payoffs among the cooperative service providers in the bundling coalition.

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http://hdl.handle.net/10453/147455