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All Technical Committee Conferences (Searched in: All Years)
| Committee | Date Time | Place | Paper Title / Authors | Abstract | Paper # | |
| IN, NS (Joint) |
2023-03-03 10:30 |
Okinawa | Okinawa Convention Centre + Online (Primary: On-site, Secondary: Online) |
Optimum Worker Sampling in Crowdsecsing with Multiple Areas Chihiro Matsuura, Noriaki Kamiyama (Ritsumeikan Univ.)  NS2022-218 |
The use of mobile crowdsensing (MCS), in which sensing data measured by mobile devices equipped with high-performance se... [more] | NS2022-218 pp.292-297 |
| NS, IN (Joint) |
2022-03-11 09:10 |
Online | Online | Optimal Poisoning Attacks on Crowdsensing at Multiple Locations Rin Fujimoto (Fukuoka Univ), Noriaki Kamiyama (Ritsumeikan Univ) NS2021-138 |
Recently, crowdsensing using mobile devices has gathered wide attention as a way to estimate various environmental data ... [more] | NS2021-138 pp.91-96 |
| RISING (3rd) |
2021-11-16 11:30 |
Tokyo | (Primary: On-site, Secondary: Online) |
Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning Satoshi Nakaniida, Takeo Fujii (UEC) |
We propose an approach using federated learning for predicting Wi-Fi and LTE transmission control protocol (TCP) through... [more] | |
| SITE, ISEC, LOIS | 2021-11-12 13:00 |
Online | Online | A Revocable Anonymous Reputation System for Crowd Sensing Haruki Kobayashi, Toru Nakanishi, Teruaki Kitasuka (Hiroshima Univ.) ISEC2021-41 SITE2021-35 LOIS2021-24 |
In crowdsensing, since the data gathered by the server includes user's GPS locations and moving paths, the anonymity of ... [more] | ISEC2021-41 SITE2021-35 LOIS2021-24 pp.1-6 |
| SR | 2021-11-05 11:15 |
Online | Online | Throughput Prediction by Radio Environment Correlation Recognition Using Crowd Sensing and Federated Learning Satoshi Nakaniida, Takeo Fujii (UEC) SR2021-53 |
We propose an approach using federated learning for predicting Wi-Fi and LTE transmission control protocol (TCP) through... [more] | SR2021-53 pp.72-78 |
| LOIS, ISEC, SITE | 2020-11-06 11:20 |
Online | Online | Speeding Up a Revocable Group Signature Scheme for Crowdsensing Yuto Nakazawa, Toru Nakanishi (Hiroshima Univ.) ISEC2020-34 SITE2020-31 LOIS2020-14 |
In crowdsensing, many users carrying mobile terminals send environmental data and congestion status to the server togeth... [more] | ISEC2020-34 SITE2020-31 LOIS2020-14 pp.13-18 |
| LOIS | 2020-03-12 13:45 |
Okinawa | Nobumoto Ohama Memorial Hall (Cancelled but technical report was issued) |
Mobile Sensing of Pedestrian Mobility and its Assessment Chenwei Song, Masaki Ito, Yuuki Nishiyama, Kaoru Sezaki (Univ.Tokyo) LOIS2019-77 |
We propose a client-server system that provides crowd detection and mobility information. Our proposed system has the ad... [more] | LOIS2019-77 pp.121-126 |
| NS, IN (Joint) |
2020-03-06 09:50 |
Okinawa | Royal Hotel Okinawa Zanpa-Misaki (Cancelled but technical report was issued) |
A Study on Reaction-diffusion based Adaptive Incentive Control for Vehicular Crowd Sensing Yushi Sugawara, Hiroki Akitaya, Yukio Ogawa, Kaoru Ota, Mianxiong Dong (Muroran-IT) IN2019-106 |
Vehicles can be regarded as a mobile crowd sensing platform for gathering environmental information. Because environment... [more] | IN2019-106 pp.165-170 |
| SeMI | 2020-01-30 14:50 |
Kagawa | Considering Reward-Based Allocation Strategies in Multiple Crowdsensing Yoshiki Amano, Osamu Mizuno (Kogkuin Univ) SeMI2019-101 |
Crowd sensing is useful to monitor wide-area data, such as environmental conditions, traffic conditions. It uses sensors... [more] | SeMI2019-101 pp.11-16 |
|
| SR (2nd) |
2019-11-04 - 2019-11-05 |
Overseas | Rutgers University Inn & Conference Center, NJ, USA | [Poster Presentation]
A Study of Hidden Node Discrimination Method using Wireless LAN Packets Fumiya Aizawa, Takeo Fujii (UEC) |
Hidden node problem is well known problem to degrade wireless LAN performance. In IoT era, due to increasing the number ... [more] | |
| ISEC, SITE, ICSS, EMM, HWS, BioX, IPSJ-CSEC, IPSJ-SPT [detail] | 2019-07-23 13:35 |
Kochi | Kochi University of Technology | A Revocable Group Signature Scheme with Strong Anonymity for Crowdsensing Yuto Nakazawa, Toru Nakanishi (Hiroshima Univ.) ISEC2019-22 SITE2019-16 BioX2019-14 HWS2019-17 ICSS2019-20 EMM2019-25 |
In crowdsensing, many users carrying mobile terminals send environmental data and congestion status to the server togeth... [more] | ISEC2019-22 SITE2019-16 BioX2019-14 HWS2019-17 ICSS2019-20 EMM2019-25 pp.107-112 |
| RCS, SR, SRW (Joint) |
2019-03-06 15:40 |
Kanagawa | YRP | [Invited Lecture]
Spectrum Sharing based on Radio Environment Map: Theory and Open Issues Koya Sato (Tokyo Univ. of Science) SR2018-127 |
This paper summarizes the radio environment map (REM) for spatial spectrum sharing. REM contains spatial distribution of... [more] | SR2018-127 pp.43-49 |
| ASN, MoNA, IPSJ-MBL, IPSJ-UBI [detail] | 2019-03-05 09:40 |
Tokyo | The University of Tokyo, Komaba Campus | Reward-based Allocation for Mobile Crowdsensing in Real-time Prediction of Spatial Information Rieko Takagi, Yuichi Inagaki, Ryoichi Shinkuma (Kyoto Univ.), Fatos Xhafa (UPC), Takehiro Sato, Eji Oki (Kyoto Univ.) MoNA2018-72 |
Real-time prediction of spatial information has attracted a lot of attention as a potential solution to social problems ... [more] | MoNA2018-72 pp.53-58 |
| IN, NS (Joint) |
2019-03-04 11:10 |
Okinawa | Okinawa Convention Center | A proposal of mobile crowdsensing application using blockchain technology Akihiro Fujihara (CIT) IN2018-96 |
Mobile crowdsensing is a method to collectively gather and share local data with the help of a large number of anonymo... [more] |
IN2018-96 pp.73-78 |
| SR | 2019-01-24 15:45 |
Fukushima | Corasse, Fukushima city (Fukushima prefecture) | On the Radio Environment Map Construction using Neural Network Residual Kriging Koya Sato (TUS), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2018-106 |
In this paper, we discuss the performance of feedforward neural network (FFNN) in radio environment map (REM) constructi... [more] | SR2018-106 pp.63-70 |
| SR | 2019-01-25 15:15 |
Fukushima | Corasse, Fukushima city (Fukushima prefecture) | A Study on Frequency-domain Interpolation of a Radio Environment Map for Bands of Cellular System Keita Onose, Keita Katagiri (UEC), Koya Sato (TUS), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2018-116 |
A Measurement-based Spectrum Database (MSD) attracts attention as highly accurate radio environment recognition. It is i... [more] | SR2018-116 pp.123-128 |
| ASN, NS, RCS, SR, RCC (Joint) |
2018-07-12 10:20 |
Hokkaido | Hakodate Arena | Experimental Verification of Frequency-Correlation of Radio Propagation Characteristics for Radio Environment Recognition Keita Onose (UEC), Koya Sato (TUS), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2018-32 |
Spectrum sharing with cognitive radio has attracted attention to solve the shortage of spectrum resources. In order to r... [more] | SR2018-32 pp.73-78 |
| SR | 2018-01-26 10:55 |
Fukuoka | Fukuoka univ. | A Calibration Method for Crowdsensing Based Radio Environment Database Miho Ito, Keita Onose, Koya Sato (UEC), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2017-103 |
In spectrum sharing with cognitive radio, radio environment estimation is essential to secure quality of unlicensed user... [more] | SR2017-103 pp.57-62 |
| SR, SRW (Joint) |
2017-10-23 14:00 |
Overseas | Grand Hotel Palatino, Rome, Italy | [Poster Presentation]
Experimental Verification of Frequency-Correlation for Radio Environment Recognition Using Crowd Sensing Keita Onose, Koya Sato (UEC), Kei Inage (TMCIT), Takeo Fujii (UEC) SR2017-74 SRW2017-26 |
Spectrum sharing with cognitive radio has attracted attention to solve the shortage of spectrum resources. In order to r... [more] | SR2017-74 SRW2017-26 pp.27-28(SR), pp.31-32(SRW) |
| CQ | 2017-07-27 10:20 |
Hyogo | Kobe University | Mobile Delay Tomography Considering Differences in the Amount of Measurement Data among Paths Hideaki Kinsho (Osaka Univ.), Rie Tagyo, Daisuke Ikegami (NTT), Takahiro Matsuda (Osaka Univ.), Jun Okamoto (NTT), Tetsuya Takine (Osaka Univ.) CQ2017-31 |
In mobile networks, in order to estimate delays at network components such as base stations and servers, a delay tomogra... [more] | CQ2017-31 pp.13-18 |
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