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A resilient and secure two-stage ITA and blockchain mechanism in mobile crowd sourcing

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Abstract

Mobile crowd sourcing offers a way for an organization to gain afar their “base of minds” of workforce offers in the form of idea competitions or innovation contests where task is divided among individuals to achieve a pooled result using mobile devices. However, mobile crowd sourcing consequence to an urban bias in addition to other key challenging issues such as safety, privacy and connectivity or data fault tolerance. Fault tolerance in mobile crowd sourcing is a property to tolerate any fault related to data collected or the working of the system. Further, it is needed to ensure a secure resilient mechanism due to de-centralized behavior of the network. In this paper, we have proposed a resilient-improved two-stage auction (ITA) and blockchain mechanism in mobile crowd sourcing beneficial for the organizations. The resilient technique and secure scheme is beneficial for the organizations that select the workers according to their budget and time. Further, an akka API tool is implemented upon already existing mobile crowd sourcing technique i.e. ITA. The proposed resilient mechanism is initially implemented over. The proposed resilient ITA mechanism is analyzed over further crowd sourcing mechanisms needed for workers selection in order to benefit the organizations. Further, the resilient-ITA is measured against database connection loss that is considered as a promising type of fault occurring during the workers selection.

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References

  • Brady E, Morris MR, Bigham JP (2015) Gauging receptiveness to social microvolunteering. In: Proceedings of the 33rd annual ACM conference on human factors in computing systems, pp 1055–1064

  • Christin D, Reinhardt A, Kanhere SS, Hollick M (2011) A survey on privacy in mobile participatory sensing applications. J Syst Softw 84(11):1928–1946

    Article  Google Scholar 

  • Feng W, Yan Z, Zhang H, Zeng K, Xiao Y, Hou YT (2017) A survey on security, privacy, and trust in mobile crowdsourcing. IEEE Internet Things J 5(4):2971–2992

    Article  Google Scholar 

  • Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39

    Article  Google Scholar 

  • Gruenheid A, Nushi B, Kraska T, Gatterbauer W, Kossmann D (2015) Fault-tolerant entity resolution with the crowd. arXiv:1512.00537

  • Han K, Liu H, Tang S, Xiao M, Luo J (2018) Differentially private mechanisms for budget limited mobile crowdsourcing. IEEE Trans Mob Comput 18(4):934–946

    Article  Google Scholar 

  • Li C, Gong S, Wang X, Wang L, Jiang Q, Okamura K (2017) Secure and efficient content distribution in crowdsourced vehicular content-centric networking. IEEE Access 6:5727–5739

    Article  Google Scholar 

  • Liu CH, Lin Q, Wen S (2018) Blockchain-enabled data collection and sharing for industrial IoT with deep reinforcement learning. IEEE Trans Ind Inf 15(6):3516–3526

    Article  Google Scholar 

  • Qi L, Dou W, Wang W, Li G, Yu H, Wan S (2018) Dynamic mobile crowdsourcing selection for electricity load forecasting. IEEE Access 6:46926–46937

    Article  Google Scholar 

  • Ren J, Zhang Y, Zhang K, Shen X (2015) Exploiting mobile crowdsourcing for pervasive cloud services: challenges and solutions. IEEE Commun Mag 53(3):98–105

    Article  Google Scholar 

  • Rula JP, Navda V, Bustamante FE, Bhagwan R, Guha S (2014) No “one-size fits all” towards a principled approach for incentives in mobile crowdsourcing. In: Proceedings of the 15th workshop on mobile computing systems and applications, pp 1–5

  • Smith KL, Ramos I, Desouza KC (2015) Economic resilience and crowdsourcing platforms. J Inf Syst Technol Manag 12(3):595–626

    Google Scholar 

  • Wang Y, Huang Y, Louis C (2013) Respecting user privacy in mobile crowdsourcing. Science 2(2):50

    Google Scholar 

  • Wang Y, Cai Z, Yin G, Gao Y, Tong X, Wu G (2016) An incentive mechanism with privacy protection in mobile crowdsourcing systems. Comput Netw 102:157–171

    Article  Google Scholar 

  • Wang L, Yu Z, Han Q, Guo B, Xiong H (2017a) Multi-objective optimization based allocation of heterogeneous spatial crowdsourcing tasks. IEEE Trans Mob Comput 17(7):1637–1650

    Article  Google Scholar 

  • Wang X, Lin Y, Zhang S, Cai Z (2017b) A social activity and physical contact-based routing algorithm in mobile opportunistic networks for emergency response to sudden disasters. Enterp Inf Syst 11(5):597–626

    Article  Google Scholar 

  • Wang Y, Cai Z, Zhan ZH, Gong YJ, Tong X (2019) An optimization and auction-based incentive mechanism to maximize social welfare for mobile crowdsourcing. IEEE Trans Comput Soc Syst 6(3):414–429

    Article  Google Scholar 

  • Xu X, Liu Q, Zhang X, Zhang J, Qi L, Dou W (2019) A blockchain-powered crowdsourcing method with privacy preservation in mobile environment. IEEE Trans Comput Soc Syst 6(6):1407–1419

    Article  Google Scholar 

  • Yang K, Zhang K, Ren J, Shen X (2015) Security and privacy in mobile crowdsourcing networks: challenges and opportunities. IEEE Commun Mag 53(8):75–81

    Article  Google Scholar 

  • Zhang L, Cai Z, Lu J, Wang X (2015) Mobility-aware routing in delay tolerant networks. Pers Ubiquit Comput 19(7):1111–1123

    Article  Google Scholar 

  • Zhao D, Li XY, Ma H (2014) How to crowdsource tasks truthfully without sacrificing utility: online incentive mechanisms with budget constraint. In: IEEE INFOCOM 2014-IEEE conference on computer communications, pp 1213–1221

  • Zhao C, Yang S, Yang X, McCann JA (2016) Rapid, user-transparent, and trustworthy device pairing for D2D-enabled mobile crowdsourcing. IEEE Trans Mob Comput 16(7):2008–2022

    Article  Google Scholar 

  • Zhou P, Zheng Y, Li M (2012) How long to wait? Predicting bus arrival time with mobile phone based participatory sensing. In: Proceedings of the 10th international conference on mobile systems, applications, and services, pp 379–392

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Correspondence to Ravi Rastogi.

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Sivaram, M., Rathee, G., Rastogi, R. et al. A resilient and secure two-stage ITA and blockchain mechanism in mobile crowd sourcing. J Ambient Intell Human Comput 11, 5003–5016 (2020). https://doi.org/10.1007/s12652-020-01800-x

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  • DOI: https://doi.org/10.1007/s12652-020-01800-x

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