Near-Optimal Incentive Allocation for Piggyback Crowdsensing
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.
H. Xiong et al., "Near-Optimal Incentive Allocation for Piggyback Crowdsensing," IEEE Communications Magazine, vol. 55, no. 6, pp. 120 - 125, Institute of Electrical and Electronics Engineers (IEEE), Jan 2017.
The definitive version is available at https://doi.org/10.1109/MCOM.2017.1600748
Intelligent Systems Center
National Natural Science Foundation of China
Keywords and Phrases
Allocation Problems; Budget Constraint; Data Collection; Energy Efficient; Incentive Payments; Optimal Data; Spatial Temporals; Task Modeling
International Standard Serial Number (ISSN)
Article - Journal
© 2017 Institute of Electrical and Electronics Engineers (IEEE), All rights reserved.
01 Jan 2017