Presentation 2019-03-05
Reward-based Allocation for Mobile Crowdsensing in Real-time Prediction of Spatial Information
Rieko Takagi, Yuichi Inagaki, Ryoichi Shinkuma, Fatos Xhafa, Takehiro Sato, Eji Oki,
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Abstract(in Japanese) (See Japanese page)
Abstract(in English) Real-time prediction of spatial information has attracted a lot of attention as a potential solution to social problems such as road traffic congestion. Prediction of spatial information is performed using sensor data collected independently by geographically distributed user devices, which is called mobile crowdsensing. However, conventionally, researchers assumed that user devices provide their data altruistically and did not consider that they may refuse to do that. Likewise, in mobile crowdsensing is commonly assumed that data is truthful and of equal quality. Unfortunataley, this is not always the case of participatory systems, such as mobile crowdsensing, where users participate at will and data veracity and quality are not always ensured. Therefore, in this technical report, we propose a rewarding system that incentives user devices to provide their truthful and quality data. Our system estimates temporal and spatial importance of data provided by each user device and allocates a larger amount of reward to user devices making a larger contribution to the prediction accuracy. Performance evaluation validates the effectiveness of our system by using a real dataset.
Keyword(in Japanese) (See Japanese page)
Keyword(in English) crowdsensing / incentive / spatial information prediction / machine learning / feature selection
Paper # MoNA2018-72
Date of Issue 2019-02-26 (MoNA)

Conference Information
Committee ASN / MoNA / IPSJ-MBL / IPSJ-UBI
Conference Date 2019/3/4(2days)
Place (in Japanese) (See Japanese page)
Place (in English) The University of Tokyo, Komaba Campus
Topics (in Japanese) (See Japanese page)
Topics (in English) General
Chair Hiraku Okada(Nagoya Univ.) / Ryoichi Shinkuma(Kyoto Univ.) / / Yoshito Tobe(Tokyo Denki Univ.)
Vice Chair Koji Yamamoto(Kyoto Univ.) / Jin Nakazawa(Keio Univ.) / Kazuya Monden(Hitachi) / Shigeaki Tagashira(Kansai Univ.) / Gen Kitagata(Tohoku Univ.)
Secretary Koji Yamamoto(NICT) / Jin Nakazawa(Sophia Univ.) / Kazuya Monden(Kanagawa Inst. of Tech.) / Shigeaki Tagashira(Kyushu Univ.) / Gen Kitagata(NEC) / (Kyoto Univ.) / (NTT DOCOMO)
Assistant Masafumi Hashimoto(Osaka Univ.) / Tomoyuki Ota(Hiroshima City Univ.) / Tatsuya Kikuzuki(Fujitu Lab.) / Ryo Nakano(HITACHI) / Yoshifumi Hotta(Mitsubishi Electric) / Ken Usui(KDDI Research) / Kenji Kanai(Waseda Univ.)

Paper Information
Registration To Technical Committee on Ambient intelligence and Sensor Networks / Technical Committee on Mobile Network and Applications / Special Interest Group on Mobile Computing and Ubiquitous Communications / Special Interest Group on Ubiquitous Computing System
Language JPN
Title (in Japanese) (See Japanese page)
Sub Title (in Japanese) (See Japanese page)
Title (in English) Reward-based Allocation for Mobile Crowdsensing in Real-time Prediction of Spatial Information
Sub Title (in English)
Keyword(1) crowdsensing
Keyword(2) incentive
Keyword(3) spatial information prediction
Keyword(4) machine learning
Keyword(5) feature selection
1st Author's Name Rieko Takagi
1st Author's Affiliation Kyoto University(Kyoto Univ.)
2nd Author's Name Yuichi Inagaki
2nd Author's Affiliation Kyoto University(Kyoto Univ.)
3rd Author's Name Ryoichi Shinkuma
3rd Author's Affiliation Kyoto University(Kyoto Univ.)
4th Author's Name Fatos Xhafa
4th Author's Affiliation Polytechnic University of Catalonia(UPC)
5th Author's Name Takehiro Sato
5th Author's Affiliation Kyoto University(Kyoto Univ.)
6th Author's Name Eji Oki
6th Author's Affiliation Kyoto University(Kyoto Univ.)
Date 2019-03-05
Paper # MoNA2018-72
Volume (vol) vol.118
Number (no) MoNA-467
Page pp.pp.53-58(MoNA),
#Pages 6
Date of Issue 2019-02-26 (MoNA)