Incentivizing for Truth Discovery in Edge-assisted Large-scale Mobile Crowdsensing
Open Access
- 2 February 2020
- Vol. 20 (3), 805
- https://doi.org/10.3390/s20030805
Abstract
The recent development of human-carried mobile devices has promoted the great development of mobile crowdsensing systems. Most existing mobile crowdsensing systems depend on the crowdsensing service of the deep cloud. With the increasing scale and complexity, there is a tendency to enhance mobile crowdsensing with the edge computing paradigm to reduce latency and computational complexity, and improve the expandability and security. In this paper, we propose an integrated solution to stimulate the strategic users to contribute more for truth discovery in the edge-assisted mobile crowdsensing. We design an incentive mechanism consisting of truth discovery stage and budget feasible reverse auction stage. In truth discovery stage, we estimate the truth for each task in both deep cloud and edge cloud. In budget feasible reverse auction stage, we design a greedy algorithm to select the winners to maximize the quality function under the budget constraint. Through extensive simulations, we demonstrate that the proposed mechanism is computationally efficient, individually rational, truthful, budget feasible and constant approximate. Moreover, the proposed mechanism shows great superiority in terms of estimation precision and expandability.Keywords
Funding Information
- National Natural Science Foundation of China (61872193)
- National Science Foundation (1717315)
This publication has 29 references indexed in Scilit:
- QUAC: Quality-Aware Contract-Based Incentive Mechanisms for CrowdsensingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2017
- On Designing Data Quality-Aware Truth Estimation and Surplus Sharing Method for Mobile CrowdsensingIEEE Journal on Selected Areas in Communications, 2017
- Ag Nanoparticles Located on Three-Dimensional Pine Tree-Like Hierarchical TiO2 Nanotube Array Films as High-Efficiency Plasmonic PhotocatalystsNanoscale Research Letters, 2017
- Near-Optimal Allocation Algorithms for Location-Dependent Tasks in CrowdsensingIEEE Transactions on Vehicular Technology, 2016
- INCEPTIONPublished by Association for Computing Machinery (ACM) ,2016
- Conflicts to Harmony: A Framework for Resolving Conflicts in Heterogeneous Data by Truth DiscoveryIEEE Transactions on Knowledge and Data Engineering, 2016
- Incentive Mechanisms for Time Window Dependent Tasks in Mobile CrowdsensingIEEE Transactions on Wireless Communications, 2015
- Quality-Driven Auction-Based Incentive Mechanism for Mobile Crowd SensingIEEE Transactions on Vehicular Technology, 2014
- Resolving conflicts in heterogeneous data by truth discovery and source reliability estimationPublished by Association for Computing Machinery (ACM) ,2014
- The budgeted maximum coverage problemInformation Processing Letters, 1999