@techreport{oai:ipsj.ixsq.nii.ac.jp:00194613,
 author = {高木, 理絵子 and 稲垣, 悠一 and 新熊, 亮一 and Fatos, Xhafa and 佐藤, 丈博 and 大木, 英司 and Rieko, Takagi and Yuichi, Inagaki and Ryoichi, Shinkuma and Fatos, Xhafa and Takehiro, Sato and Eiji, Oki},
 issue = {15},
 month = {Feb},
 note = {近年，交通渋滞などの問題解決の手段として，実空間情報のリアルタイム予測の需要が高まっている．予測に必要な広範囲長時間の実空間情報を低コストで集めるために，これまでクラウドセンシングが提案されている．しかし，これまでユーザデバイスはデータを利他的に送信すると想定されており，ユーザデバイスがデータ送信を拒否する可能性があることは考慮されていなかった．そこで，本稿では，利己的にデータを送信するユーザデバイス群に対し，予算の制限下で予測精度を最大化するために，予測精度への貢献度を表す尺度であるデータ重要度に基づく報酬配分方式を提案する．そして，実データセットを用いて提案方式の有効性を示す．, 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.},
 title = {実空間情報のリアルタイム予測におけるクラウドセンシングのための報酬配分},
 year = {2019}
}