Matching with Externalities for Device Assignment in IoT Sharding Blockchain

Wu, J; Zhang, J

Matching with Externalities for Device Assignment in IoT Sharding Blockchain

Wu, J

Zhang, J

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Publisher:: Springer Nature
Publication Type:: Conference Proceeding
Citation:: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2022, 437 LNICST, pp. 46-60
Issue Date:: 2022-01-01

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Field	Value	Language
dc.contributor.author	Wu, J https://orcid.org/0000-0002-5592-1515
dc.contributor.author	Zhang, J
dc.date.accessioned	2023-04-18T03:15:40Z
dc.date.available	2023-04-18T03:15:40Z
dc.date.issued	2022-01-01
dc.identifier.citation	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, 2022, 437 LNICST, pp. 46-60
dc.identifier.isbn	9783031042300
dc.identifier.issn	1867-8211
dc.identifier.issn	1867-822X
dc.identifier.uri	http://hdl.handle.net/10453/169907
dc.description.abstract	Blockchain technology provides a powerful platform to record and manage Internet-of-Things (IoT) data. To support massive amounts of IoT data, sharding protocols are applied to enlarge the blockchain system scalability and increase efficiency. It divides the IoT devices into several committees (also called shards) so that data of different IoT devices can be processed at the same time in various shards. Random device assignment is the most popular way in IoT sharding blockchain system. Such assignment does not consider the features of different IoT devices and relationship among each IoT device. Hence, the random device assignment might lower the data processing speed. In this paper, we address this issue by modeling the IoT device assignment problem as a many-to-one matching model. Devices are assigned to only one shard while a shard can house many devices. Due to dynamic preference lists of devices, we consider the matching model with externalities. We propose an algorithm to find a stable matching and prove its convergence and stability. The simulation results indicate that the algorithm converges efficiently. Besides, the proposed algorithm has better performance than random assignment.
dc.language	en
dc.publisher	Springer Nature
dc.relation.ispartof	Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
dc.relation.ispartofseries	Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
dc.relation.isbasedon	10.1007/978-3-031-04231-7_4
dc.rights	info:eu-repo/semantics/closedAccess
dc.title	Matching with Externalities for Device Assignment in IoT Sharding Blockchain
dc.type	Conference Proceeding
utslib.citation.volume	437 LNICST
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/Faculty of Engineering and Information Technology/School of Computer Science
utslib.copyright.status	closed_access	*
dc.date.updated	2023-04-18T03:15:38Z
pubs.publication-status	Published
pubs.volume	437 LNICST

Abstract:

Blockchain technology provides a powerful platform to record and manage Internet-of-Things (IoT) data. To support massive amounts of IoT data, sharding protocols are applied to enlarge the blockchain system scalability and increase efficiency. It divides the IoT devices into several committees (also called shards) so that data of different IoT devices can be processed at the same time in various shards. Random device assignment is the most popular way in IoT sharding blockchain system. Such assignment does not consider the features of different IoT devices and relationship among each IoT device. Hence, the random device assignment might lower the data processing speed. In this paper, we address this issue by modeling the IoT device assignment problem as a many-to-one matching model. Devices are assigned to only one shard while a shard can house many devices. Due to dynamic preference lists of devices, we consider the matching model with externalities. We propose an algorithm to find a stable matching and prove its convergence and stability. The simulation results indicate that the algorithm converges efficiently. Besides, the proposed algorithm has better performance than random assignment.

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http://hdl.handle.net/10453/169907