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Title: Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation

Abstract

Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. Here, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. In particular, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision-making process of a central manager, an agent-based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The findings indicate the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we discover that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade-off between performance and cost in designing effective reward allocation policies. This paper providesmore » a physical and behavior simulation modeling tool to study the feasibility and complexity of reward-based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.« less

Authors:
ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]
  1. Univ. of Illinois at Urbana-Champaign, IL (United States). Dept. of Civil and Environmental Engineering and DOE Center for Advanced Bioenergy and Bioproducts Innovation; Hong Kong Univ. of Science and Technology (Hong Kong). Dept. of Civil and Environmental Engineering
  2. Hong Kong Univ. of Science and Technology (Hong Kong). Dept. of Civil and Environmental Engineering; Commonwealth Scientific and Industrial Research Organization (CSIRO) Land and Water, Clayton, VIC (Australia)
  3. Univ. of Illinois at Urbana-Champaign, IL (United States). Dept. of Civil and Environmental Engineering and DOE Center for Advanced Bioenergy and Bioproducts Innovation
Publication Date:
Research Org.:
Center for Advanced Bioenergy and Bioproducts Innovation (CABBI), Urbana, IL (United States); Univ. of Illinois at Urbana-Champaign, IL (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER) (SC-23)
OSTI Identifier:
1574501
Alternate Identifier(s):
OSTI ID: 1570546
Grant/Contract Number:  
SC0018420; DE‐SC0018420
Resource Type:
Accepted Manuscript
Journal Name:
Water Resources Research
Additional Journal Information:
Journal Volume: 55; Journal ID: ISSN 0043-1397
Publisher:
American Geophysical Union (AGU)
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; crowdsourcing; rainfall monitoring; agent-based model; reward allocation; participant management

Citation Formats

Yang, Pan, Ng, Tze Ling, and Cai, Ximing. Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation. United States: N. p., 2019. Web. doi:10.1029/2018WR024447.
Yang, Pan, Ng, Tze Ling, & Cai, Ximing. Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation. United States. doi:10.1029/2018WR024447.
Yang, Pan, Ng, Tze Ling, and Cai, Ximing. Sat . "Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation". United States. doi:10.1029/2018WR024447.
@article{osti_1574501,
title = {Reward-Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent-Based Model Simulation},
author = {Yang, Pan and Ng, Tze Ling and Cai, Ximing},
abstractNote = {Crowdsourcing incorporates common citizens as rich sources of data and is promising for environmental monitoring. Here, we propose and test the idea of incorporating incentives to crowdsourcing management for rainfall monitoring. In particular, we model the allocation of incentives (quantitatively measurable and limited rewards) among crowdsourcing participants for a theoretical rainfall monitoring case. For this purpose, we develop an integrated model comprising a reward allocation component to represent the decision-making process of a central manager, an agent-based model to simulate the interactions between the manager and participants, and a rainfall simulation model to evaluate the effectiveness of various reward allocation policies. We simulate six reward allocation policies of varying levels of administrative cost, and consideration of participant and rainfall spatial heterogeneities. The findings indicate the performance of each policy to improve with the reward budget and their spatial uniformity. Among the six policies tested, we discover that the participant density weighted maximum participation policy yields the most accurate estimation of rainfall intensity due to its more explicit consideration of the spatial distribution of participants; however, this policy associates with a high administrative cost. This highlights the trade-off between performance and cost in designing effective reward allocation policies. This paper provides a physical and behavior simulation modeling tool to study the feasibility and complexity of reward-based participant management for crowdsourcing rainfall monitoring. The proposed crowdsourcing method is beneficial for a wide range of applications that require rainfall data with fine resolution, such as storm water management and water availability and biomass assessment for food and energy crops.},
doi = {10.1029/2018WR024447},
journal = {Water Resources Research},
number = ,
volume = 55,
place = {United States},
year = {2019},
month = {9}
}

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