Abstract
Crowdsourcing has been a popular paradigm leveraging the power of the crowd to accomplish a common goal. Traditional crowdsourcing systems rely on a centralized platform to allocate tasks and rewards to users, facing severe problems of single node failure and malicious behaviors of the platform. Recently, some works take advantage of blockchain to solve the drawback of centralization. However, the key issue, fair and efficient task allocation, has not been explored in existing blockchain-based crowdsourcing systems. In this paper, we design a novel blockchain-based framework for crowdsourcing, in which a distributed reverse and blind auction-based task allocation mechanism (RbatAlloc) is proposed utilizing user profile and bidding price to realize fair and efficient task allocation in transparent blockchain environment. Finally, we implement a prototype of our system and deploy it to a locally developed network. The experimental results demonstrate the effectiveness of the proposed framework and mechanism.
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References
An, B., Xiao, M., Liu, A., Gao, G., Zhao, H.: Truthful crowdsensed data trading based on reverse auction and blockchain. In: International conference on database systems for advanced applications, Springer, pp. 292–309 (2019)
Buccafurri, F., Lax, G., Nicolazzo, S., Nocera, A.: Tweetchain: an alternative to blockchain for crowd-based applications. In: International Conference on Web Engineering, Springer, pp 386–393 (2017)
Chatzopoulos, D., Gujar, S., Faltings, B., Hui, P.: Privacy preserving and cost optimal mobile crowdsensing using smart contracts on blockchain. In: 2018 IEEE 15th International Conference on Mobile Ad Hoc and Sensor Systems (MASS), IEEE, pp. 442–450 (2018)
Feng, W., Yan, Z.: Mcs-chain: decentralized and trustworthy mobile crowdsourcing based on blockchain. Fut. Gen. Comput. Syst. 95, 649–666 (2019)
Gu, Y., Chen, J., Wu, X.: An implement of smart contract based decentralized online crowdsourcing mechanism. In: Proceedings of the 2018 2nd international conference on computer science and artificial intelligence, ACM, pp 195–199 (2018)
Hu, D., Ma, C., Gao, Y., Liu, W.: Research on crowdsourcing camera mode system based on block chain technology. In: MATEC web of conferences, vol 176, p. 03015 (2018)
Hu, J., Wang, Z., Wei, J., Lv, R., Zhao, J., Wang, Q., Chen, H., Yang, D.: Towards demand-driven dynamic incentive for mobile crowdsensing systems. IEEE Trans. Wireless Commun. (2020). https://doi.org/10.1109/TWC.2020.2988271
Hu, Y., Wang, Y., Li, Y., Tong, X.: An incentive mechanism in mobile crowdsourcing based on multi-attribute reverse auctions. Sensors 18(10), 3453 (2018b)
Li, J., Zhu, Y., Hua, Y., Yu, J.: Crowdsourcing sensing to smartphones: a randomized auction approach. IEEE Trans. Mob. Comput. 16(10), 2764–2777 (2017a)
Li, L., Liu, J., Cheng, L., Qiu, S., Wang, W., Zhang, X., Zhang, Z.: Creditcoin: a privacy-preserving blockchain-based incentive announcement network for communications of smart vehicles. IEEE Trans. Intell. Transp. Syst. 19(7), 2204–2220 (2018)
Li, M., Weng, J., Yang, A., Lu, W., Zhang, Y., Hou, L., Liu, J.N., Xiang, Y., Deng, R.H.: Crowdbc: A blockchain-based decentralized framework for crowdsourcing. IACR Cryptol ePrint Arch, Univ California, Santa Barbara, Santa Barbara, CA, USA, Tech Rep 444, 2017 (2017b)
Lu, Y., Tang, Q., Wang, G.: Zebralancer: Private and anonymous crowdsourcing system atop open blockchain. In: 2018 IEEE 38th International conference on distributed computing systems (ICDCS), IEEE, pp. 853–865 (2018)
Lynch, S.: Openlittermap. com–open data on plastic pollution with blockchain rewards (littercoin). Open Geospatial Data Softw. Stand. 3(1):6 (2018)
Ma, Y., Sun, Y., Lei, Y., Qin, N., Lu, J.: A survey of blockchain technology on security, privacy, and trust in crowdsourcing services. World Wide Web 23(1), 393–419 (2020)
Nakamoto, S.: Bitcoin: A peer-to-peer electronic cash system (2008)
Ren, J., Guo, H., Xu, C., Zhang, Y.: Serving at the edge: a scalable iot architecture based on transparent computing. IEEE Netw 31(5), 96–105 (2017)
Ren, J., Zhang, D., He, S., Zhang, Y., Li, T.: A survey on end-edge-cloud orchestrated network computing paradigms: transparent computing, mobile edge computing, fog computing, and cloudlet. ACM Comput. Surv. (CSUR) 52(6), 1–36 (2019)
Tang, W., Ren, J., Zhang, Y.: Enabling trusted and privacy-preserving healthcare services in social media health networks. IEEE Trans. Multimed. 21(3), 579–590 (2018)
Tang, W., Ren, J., Zhang, K., Zhang, D., Zhang, Y., Shen, X.: Efficient and privacy-preserving fog-assisted health data sharing scheme. ACM Trans. Intell. Syst. Technol. (TIST) 10(6), 1–23 (2019)
Wang, J., Li, M., He, Y., Li, H., Xiao, K., Wang, C.: A blockchain based privacy-preserving incentive mechanism in crowdsensing applications. IEEE Access 6, 17545–17556 (2018a)
Wang, S., Taha, A.F., Wang, J.: Blockchain-assisted crowdsourced energy systems. In: 2018 IEEE Power & Energy Society General Meeting (PESGM), IEEE, pp. 1–5 (2018b)
Wang, Z., Hu, J., Lv, R., Wei, J., Wang, Q., Yang, D., Qi, H.: Personalized privacy-preserving task allocation for mobile crowdsensing. IEEE Trans. Mob. Comput. 18(6), 1330–1341 (2018c)
Wang, Z., Pang, X., Chen, Y., Shao, H., Wang, Q., Wu, L., Chen, H., Qi, H.: Privacy-preserving crowd-sourced statistical data publishing with an untrusted server. IEEE Trans. Mob. Comput. 18(6), 1356–1367 (2018d)
Wang, Z., Pang, X., Hu, J., Liu, W., Wang, Q., Li, Y., Chen, H.: When mobile crowdsensing meets privacy. IEEE Commun. Mag. 57(9), 72–78 (2019)
Wang, Z., Huang, Y., Wang, X., Ren, J., Wang, Q., Wu, L.: Socialrecruiter: Dynamic incentive mechanism for mobile crowdsourcing worker recruitment with social networks. IEEE Trans. Mob. Comput. (2020). https://doi.org/10.1109/TMC.2020.2973958
Yang, M., Zhu, T., Liang, K., Zhou, W., Deng, R.H.: A blockchain-based location privacy-preserving crowdsensing system. Fut. Gen. Comput. Syst. 94, 408–418 (2019)
Zou, J., Ye, B., Qu, L., Wang, Y., Orgun, M.A., Li, L.: A proof-of-trust consensus protocol for enhancing accountability in crowdsourcing services. IEEE Trans. Serv. Comput. 12(3), 429–445 (2018)
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This work was supported by National Natural Science of China under Grant No. 61872274 and Fundamental Research Funds for the Central Universities under Grant No. 2042019gf0098.
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Pang, X., Guo, D., Wang, Z. et al. Towards fair and efficient task allocation in blockchain-based crowdsourcing. CCF Trans. Netw. 3, 193–204 (2020). https://doi.org/10.1007/s42045-020-00043-w
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DOI: https://doi.org/10.1007/s42045-020-00043-w