Name
Title | ||
|---|---|---|
A Blockchain-Powered Crowdsourcing Method With Privacy Preservation in Mobile Environment |
Abstract | ||
|---|---|---|
Crowdsourcing is a booming technique that enables participants to exchange data directly, thus making it possible to answer latency-sensitive service requests and relieve the burden of core networks. With some incentives, providers compete to furnish service requests, thus pledging the quality of experience (QoE) for requestors. However, the decentralized communication in crowdsourcing increases the probability of information tapering. Furthermore, providers’ arbitrary selection of the requests poses great threat to the efficient and profitable service provision for the requestors. To deal with these challenges, we propose a blockchain-powered crowdsourcing method, named BPCM, while considering the privacy preservation in mobile environment. Specifically, a mobile crowdsourcing framework based on blockchain is designed first to preserve the privacy of the participants and keep the integrity of the service request and provision. Then, density-based spatial clustering of applications with noise (DBSCAN) and improved dynamic programming (IDP) are adopted to cluster the requestors and generate service strategies, respectively. Furthermore, simple additive weighting (SAW) and multiple criteria decision making (MCDM) are utilized to select the optimal strategy that achieves the tradeoffs among maximizing the service time, increasing the profits, and reducing the energy consumption for the providers. Finally, comprehensive experiments are conducted to verify the accuracy and effectiveness of BPCM. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/TCSS.2019.2909137 | IEEE Transactions on Computational Social Systems |
Keywords | Field | DocType |
Crowdsourcing,Privacy,Blockchain,Task analysis,Device-to-device communication,Data privacy,Quality of experience | Dynamic programming,Weighting,Multiple-criteria decision analysis,Computer security,Crowdsourcing,Computer science,Artificial intelligence,Quality of experience,Cluster analysis,Energy consumption,DBSCAN,Machine learning | Journal |
Volume | Issue | ISSN |
6 | 6 | 2329-924X |
Citations | PageRank | References |
12 | 0.49 | 0 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xu Xiaolong | 1 | 424 | 64.23 |
| Qingxiang Liu | 2 | 12 | 0.49 |
| Zhang Xuyun | 3 | 952 | 69.49 |
| Jie Zhang | 4 | 41 | 3.03 |
| Lianyong Qi | 5 | 560 | 57.12 |
| Wanchun Dou | 6 | 878 | 96.01 |