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- Publisher Website: 10.1109/TWC.2020.3000241
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Article: Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labeling
Title | Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labeling |
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Authors | |
Keywords | Labeling Multicast communication Resource management Encoding Wireless networks |
Issue Date | 2020 |
Publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693 |
Citation | IEEE Transactions on Wireless Communications, 2020, v. 9 n. 19, p. 6116-6129 How to Cite? |
Abstract | The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources. |
Persistent Identifier | http://hdl.handle.net/10722/295789 |
ISSN | 2023 Impact Factor: 8.9 2023 SCImago Journal Rankings: 5.371 |
ISI Accession Number ID |
DC Field | Value | Language |
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dc.contributor.author | LI, X | - |
dc.contributor.author | ZHU, G | - |
dc.contributor.author | Shen, K | - |
dc.contributor.author | Yu, W | - |
dc.contributor.author | Gong, Y | - |
dc.contributor.author | Huang, K | - |
dc.date.accessioned | 2021-02-08T08:14:02Z | - |
dc.date.available | 2021-02-08T08:14:02Z | - |
dc.date.issued | 2020 | - |
dc.identifier.citation | IEEE Transactions on Wireless Communications, 2020, v. 9 n. 19, p. 6116-6129 | - |
dc.identifier.issn | 1536-1276 | - |
dc.identifier.uri | http://hdl.handle.net/10722/295789 | - |
dc.description.abstract | The massive sensing data generated by Internet-of-Things will provide fuel for ubiquitous artificial intelligence (AI), automating the operations of our society ranging from transportation to healthcare. The implementation of ubiquitous AI, however, entails labelling of an enormous amount of data prior to the training of AI models via supervised learning. To tackle this challenge, we explore a new direction called wireless crowd labelling, which involves downloading data to many imperfect mobile annotators for repetition labelling with an aim of exploiting multicasting in wireless networks. In this cross-disciplinary area, the rate-distortion theory and the principle of repetition labelling for accuracy improvement together give rise to a new tradeoff between radio-and-annotator resources under a constraint on labelling accuracy. Building on the tradeoff and aiming at maximizing the labelling throughput, this work focuses on the joint optimization of encoding rate, annotator clustering, and sub-channel allocation, which results in an NP-hard integer programming problem. To devise an efficient solution approach, we establish an optimal sequential annotator-clustering scheme based on the order of decreasing signal-to-noise ratios, thereby allowing the optimal solution to be found by an efficient tree search. This solution can be further simplified when the channels are symmetric. Alternatively, the optimization problem can be recognized as a knapsack problem, which can be efficiently solved in pseudo-polynomial time by means of dynamic programming. In addition, the optimal polices are derived for the annotator constrained and spectrum constrained cases. Last, simulation results are presented to demonstrate the significant throughput gains based on the optimal solution compared with decoupled allocation of the two types of resources. | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers. The Journal's web site is located at http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=7693 | - |
dc.relation.ispartof | IEEE Transactions on Wireless Communications | - |
dc.rights | IEEE Transactions on Wireless Communications. Copyright © Institute of Electrical and Electronics Engineers. | - |
dc.rights | ©20xx 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. | - |
dc.subject | Labeling | - |
dc.subject | Multicast communication | - |
dc.subject | Resource management | - |
dc.subject | Encoding | - |
dc.subject | Wireless networks | - |
dc.title | Joint Annotator-and-Spectrum Allocation in Wireless Networks for Crowd Labeling | - |
dc.type | Article | - |
dc.identifier.email | Huang, K: huangkb@eee.hku.hk | - |
dc.identifier.authority | Huang, K=rp01875 | - |
dc.description.nature | link_to_subscribed_fulltext | - |
dc.identifier.doi | 10.1109/TWC.2020.3000241 | - |
dc.identifier.scopus | eid_2-s2.0-85091135461 | - |
dc.identifier.hkuros | 321255 | - |
dc.identifier.volume | 9 | - |
dc.identifier.issue | 19 | - |
dc.identifier.spage | 6116 | - |
dc.identifier.epage | 6129 | - |
dc.identifier.isi | WOS:000568683900032 | - |
dc.publisher.place | United States | - |