Title

Near-Optimal Incentive Allocation for Piggyback Crowdsensing

Abstract

Piggyback crowdsensing (PCS) is a novel energy- efficient mobile crowdsensing paradigm that reduces the energy consumption of crowdsensing tasks by leveraging smartphone app opportunities (SAOs). This article, based on several fundamental assumptions of incentive payment for PCS task participation and spatial-temporal coverage assessment for collected sensor data, first proposes two alternating data collection goals. Goal 1 is maximizing overall spatial-temporal coverage under a predefined incentive budget constraint; goal 2 is minimizing total incentive payment while ensuring predefined spatial-temporal coverage for collected sensor data, all on top of the PCS task model. With all of the above assumptions, settings, and models, we introduce CrowdMind - a generic incentive allocation framework for the two optimal data collection goals, on top of the PCS model. We evaluated CrowdMind extensively using a large-scale real-world SAO dataset for the two incentive allocation problems. The results demonstrate that compared to baseline algorithms, CrowdMind achieves better spatial-temporal coverage under the same incentive budget constraint, while costing less in total incentive payments and ensuring the same spatial-temporal coverage, under various coverage/incentive settings. Further, a short theoretical analysis is presented to analyze the performance of Crowd- Mind in terms of the optimization with total incentive cost and overall spatial-temporal coverage objectives/constraints.

Department(s)

Computer Science

Sponsor(s)

National Natural Science Foundation of China

Comments

This article was partially supported by the Startup Grant of Missouri S&T CPS Cluster Hire, the UVA Hobby Postdoctoral Fellowship for Computational Sciences, and the NSFC under Grant No. 61572048.

Keywords and Phrases

Allocation Problems; Budget Constraint; Data Collection; Energy Efficient; Incentive Payments; Optimal Data; Spatial Temporals; Task Modeling

International Standard Serial Number (ISSN)

0163-6804

Document Type

Article - Journal

Document Version

Citation

File Type

text

Language(s)

English

Rights

© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.

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