| Presentation | 2023-03-03 Optimum Worker Sampling in Crowdsecsing with Multiple Areas Chihiro Matsuura, Noriaki Kamiyama, |
|---|---|
| PDF Download Page |  PDF download Page Link |
| Abstract(in Japanese) | (See Japanese page) |
| Abstract(in English) | The use of mobile crowdsensing (MCS), in which sensing data measured by mobile devices equipped with high-performance sensing capabilities are collected from various workers to estimate true values, is expanding. Since the widespread use of smartphones in the 2010s, MCS has attracted particular attention as a sensor device because of its excellent sensing capabilities, its ability to acquire large amounts of data from a wide range of locations, and its low cost because it does not require the construction of infrastructure. However, in reality, it is expected that the values measured by sensors have errors, and it is essential for service providers using MCSs to consider how to control errors. Existing studies have proposed methods such as extending and applying the DPA method from a single area to multiple areas and deploying attackers so that the estimation error is maximized, but methods for controlling estimation error in multiple areas have not been studied. In addition, although studies have been conducted based on the assumption that data is collected from all workers existing in each area, it is necessary to provide incentives to workers, and due to budget constraints of the MCS, it is expected that data will actually be collected only from workers sampled with a certain probability. Therefore, this study proposes a method for setting the optimal number of workers to sample when collecting data from workers in multiple areas under the condition that the total number of sampled workers is fixed. The service provider sets the total number of workers to collect data, obtains values from a database that stores the average estimation error for the number of sampled workers, and determines the number of sampled workers in each area based on the amount of change in the estimation error after changing the number of sampled workers. Simulation experiments of this type of operation confirmed the effectiveness of error suppression by collecting more data from areas with large errors. |
| Keyword(in Japanese) | (See Japanese page) |
| Keyword(in English) | mobile crowdsensing / sampling |
| Paper # | NS2022-218 |
| Date of Issue | 2023-02-23 (NS) |
| Conference Information | |
| Committee | IN / NS |
|---|---|
| Conference Date | 2023/3/2(2days) |
| Place (in Japanese) | (See Japanese page) |
| Place (in English) | Okinawa Convention Centre + Online |
| Topics (in Japanese) | (See Japanese page) |
| Topics (in English) | General |
| Chair | Kunio Hato(Internet Multifeed) / Tetsuya Oishi(NTT) |
| Vice Chair | Tsutomu Murase(Nagoya Univ.) / Takumi Miyoshi(Shibaura Insti of Tech.) |
| Secretary | Tsutomu Murase(KDDI Research) / Takumi Miyoshi(Nagaoka Univ. of Tech.) |
| Assistant | / Kotaro Mihara(NTT) |
| Paper Information | |
| Registration To | Technical Committee on Information Networks / Technical Committee on Network Systems |
|---|---|
| Language | JPN |
| Title (in Japanese) | (See Japanese page) |
| Sub Title (in Japanese) | (See Japanese page) |
| Title (in English) | Optimum Worker Sampling in Crowdsecsing with Multiple Areas |
| Sub Title (in English) | |
| Keyword(1) | mobile crowdsensing |
| Keyword(2) | sampling |
| 1st Author's Name | Chihiro Matsuura |
| 1st Author's Affiliation | Ritsumeikan University(Ritsumeikan Univ.) |
| 2nd Author's Name | Noriaki Kamiyama |
| 2nd Author's Affiliation | Ritsumeikan University(Ritsumeikan Univ.) |
| Date | 2023-03-03 |
| Paper # | NS2022-218 |
| Volume (vol) | vol.122 |
| Number (no) | NS-406 |
| Page | pp.pp.292-297(NS), |
| #Pages | 6 |
| Date of Issue | 2023-02-23 (NS) |