Ensuring High-Quality Data Collection for Mobile Crowd Sensing

2017 ◽  
Author(s):  
Hui Gao ◽  
Chi Harold Liu ◽  
Ye Tian ◽  
Teng Xi ◽  
Wendong Wang
Keyword(s):  
Data Collection ◽  
Quality Data ◽  
Mobile Crowd Sensing ◽  
High Quality ◽  
High Quality Data ◽  
Crowd Sensing ◽  
Mobile Crowd

Collecting High-Quality Data

2018 ◽  
Author(s):  
Mary Kay Gugerty ◽  
Dean Karlan
Keyword(s):  
Data Collection ◽  
Social Desirability ◽  
Measurement Errors ◽  
Monitoring And Evaluation ◽  
Quality Data ◽  
Social Desirability Bias ◽  
High Quality ◽  
High Quality Data ◽  
Evaluation Systems ◽  
Demand Effect

Without high-quality data, even the best-designed monitoring and evaluation systems will collapse. Chapter 7 introduces some the basics of collecting high-quality data and discusses how to address challenges that frequently arise. High-quality data must be clearly defined and have an indicator that validly and reliably measures the intended concept. The chapter then explains how to avoid common biases and measurement errors like anchoring, social desirability bias, the experimenter demand effect, unclear wording, long recall periods, and translation context. It then guides organizations on how to find indicators, test data collection instruments, manage surveys, and train staff appropriately for data collection and entry.

scholarly journals A Cost-Effective Distributed Framework for Data Collection in Cloud-Based Mobile Crowd Sensing Architectures

2017 ◽  
Vol 2 (1) ◽  
pp. 3-16 ◽  
Author(s):  
Andrea Capponi ◽  
Claudio Fiandrino ◽  
Dzmitry Kliazovich ◽  
Pascal Bouvry ◽  
Stefano Giordano
Keyword(s):  
Data Collection ◽  
Cost Effective ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Distributed Framework ◽  
Mobile Crowd

scholarly journals Information Explanatory Applications in Street Activity Security – A Survey

2020 ◽  
pp. 81-83
Author(s):  
Samsudeen S ◽  
Salomi M
Keyword(s):  
Data Collection ◽  
Path Selection ◽  
Data Sources ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
Start Up ◽  
Improvement Strategies ◽  
Paper Survey

The paper survey helps to diminish the start-up complex of knowledge assortment and clear analytics for factual modeling & course improvement for probability connected by engine vehicles. We tend to seem that the writing is isolated into 2 totally different inquire concerning areas: (a) discerning/illustrative methods which endeavor in order to urge it and assess clatter hazard supported distinctive powerful conditions, and (b) improvement strategies which center by minimizing clatter probability by route, path-selection and break design. Interpretation based on inquire concerning results of the 2 streams are restricted to beat the problem that tends to show freely accessible high-quality data sources (diverse take into account plans, result factors, and indicator factors) and communicative instructive strategies (information summarization, visualization, and measuring decrease) which are used for understanding safer-routing and provides code to encourage data collection/exploration by practitioners/res

scholarly journals LCBPA: two-stage task allocation algorithm for high-dimension data collecting in mobile crowd sensing network

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Ning Zhou ◽  
Jianhui Zhang ◽  
Binqiang Wang ◽  
Jia Xiao
Keyword(s):  
Data Collection ◽  
High Dimension ◽  
Task Allocation ◽  
High Dimensional Data ◽  
High Dimensional ◽  
Mobile Crowd Sensing ◽  
Allocation Algorithm ◽  
Crowd Sensing ◽  
High Dimension Data ◽  
Mobile Crowd

AbstractMobile crowd sensing (MCS) is a novel emerging paradigm that leverages sensor-equipped smart mobile terminals (e.g., smartphones, tablets, and intelligent wearable devices) to collect information. Compared with traditional data collection methods, such as construct wireless sensor network infrastructures, MCS has advantages of lower data collection costs, easier system maintenance, and better scalability. However, the limited capabilities make a mobile crowd terminal only support limited data types, which may result in a failure of supporting high-dimension data collection tasks. This paper proposed a task allocation algorithm to solve the problem of high-dimensional data collection in mobile crowd sensing network. The low-cost and balance-participating algorithm (LCBPA) aims to reduce the data collection cost and improve the equality of node participation by trading-off between them. The LCBPA performs in two stages: in the first stage, it divides the high-dimensional data into fine-grained and smaller dimensional data, that is, dividing an m-dimension data collection task into k sub-task by K-means, where (k < m). In the second stage, it assigns different nodes with different sensing capability to perform sub-tasks. Simulation results show that the proposed method can improve the task completion ratio, minimizing the cost of data collection.

A fibre-based crystal mounting technique for protein cryocrystallography

2012 ◽  
Vol 45 (2) ◽  
pp. 362-366 ◽  
Author(s):  
Michihiro Sugahara
Keyword(s):  
Data Collection ◽  
Success Rate ◽  
Single Crystals ◽  
Conventional Technique ◽  
Synthetic Zeolite ◽  
Quality Data ◽  
High Quality ◽  
High Quality Data ◽  
X Ray ◽  
Loop Technique

The CryoFibre, a crystal mounting tool, has been developed for protein cryocrystallography. The technique attaches single crystals to the tips of polyester fibres, allowing removal of excess liquid around each crystal. Single-wavelength anomalous dispersion phasing using a Cu Kα X-ray source (Cu SAD) was applied to crystals from five proteins without any derivatization, demonstrating a clear improvement in the success rate of Cu SAD compared with the conventional loop technique. In addition, a xylanase crystal on the surface of a synthetic zeolite as a hetero-epitaxic nucleant was directly mounted on the CryoFibre without separation treatment of the crystal from the zeolite. The crystal had a lower mosaicity than that observed using the conventional technique, indicating that the fibre technique is suitable for high-quality data collection from zeolite-mediated crystals.

scholarly journals Participant Service Ability Aware Data Collecting Mechanism for Mobile Crowd Sensing

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4219
Author(s):  
Jing Yang ◽  
Jialiang Xu
Keyword(s):  
Data Collection ◽  
Stackelberg Game ◽  
Task Complexity ◽  
Game Model ◽  
Mobile Crowd Sensing ◽  
Sensing Data ◽  
Crowd Sensing ◽  
Data Collecting ◽  
Mutual Choice ◽  
Mobile Crowd

To collect data efficiently and reliably in Mobile Crowd Sensing (MCS), a Participant Service Ability Aware (PSAA) data collecting mechanism is proposed. First, participants select the best sensing task according to the task complexity and desired reward in the multitasking scenario. Second, the Stackelberg Game model is established based on the mutual choice of participants and platform to maximize their utilities to evaluate the service ability of participants. Finally, participants transmit data to platform directly or indirectly through the best relay and the sensing data from the participants with better service ability is selected to complete sensing tasks accurately and efficiently with the minimum overall reward expense. The numerical results show that the proposed data collection mechanism can maximize the utility of participants and platform, efficiently accomplish sensing tasks and significantly reduce the overall reward expense.

scholarly journals Ensuring Honest Data Collection Against Collusive CSDF Attack With Binary-Minmaxs Clustering Analysis in Mobile Crowd Sensing

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 124491-124501 ◽  
Author(s):  
Jingyu Feng ◽  
Tao Li ◽  
Yujia Zhai ◽  
Shaoqing Lv ◽  
Feng Zhao
Keyword(s):  
Data Collection ◽  
Clustering Analysis ◽  
Mobile Crowd Sensing ◽  
Crowd Sensing ◽  
Mobile Crowd
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