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Quality aware cost efficient reward mechanism in mobile crowdsensing system with uncertainty constraints

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A Correction to this article was published on 15 July 2023

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Abstract

Mobile Crowdsensing (MCS) has shown the greatest potential that allows smart devices to collect and share different sensing data. Mobile users (or participants) send the desired sensing data to the service providers and collect rewards. However, the reward needs to be given such as, it does not increase platform costs. On the other hand, the unsatisfactory reward may reduce the interest of the participant which may degrade the quality data. Therefore, increasing sensing data quality with a constrained budget is a crucial challenge. There has been extensive research on the reward mechanism for MCS, but, most of the work is on the basic assumption that participant will complete their assigned task positively. In this paper, we propose an efficient user selection mechanism for Mobile Crowdsensing System (MCS) by considering the Probability of Success (PoS) of users (i.e. participant may fail to complete the assigned task with some probability). For the selection of an efficient user, the proposed mechanism also accounts the parameters like data quality and platform cost. We also propose a reward calculation model for the selected users. Minimizing the platform cost with a constrained budget is an NP-hard problem. To provide a sub-optimal solution to this problem Chaotic Krill-Herd optimization algorithm is used. The extensive simulation results reveal that the proposed method outperforms the existing work by considerable margins.

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Data availability

The website for downloading the dataset https://www.kaggle.com/datasets/arashnic/tdriver.

Change history

Abbreviations

n :

Number of mobile users (or participants)

m :

Number of tasks

b j :

Allocated budget for task j

S i :

Set of tasks coverd by user i

\(p_i^j\) :

Probability of success of user i for the task j

\(r_{i}^{s}\) :

Sensing range of user i

\(\mathcal P\) :

Set of locations

\(c^j\) :

Platform cost to complete task j

\(d^{j^{'}}\) :

Required minimum data quality for task j

\(\mathcal D_Q\) :

Total data quality

\(\omega _{n}\) :

Inertia weight of the induced motion

\(\omega _{f}\) :

Inertia weight of the foraging motion

\(\alpha _{i}\) :

Direction of the induced motion

\(V^f\) :

Foraging speed

\(\beta _{i}^{best}\) :

Best fitness effect of the ith Krill

PoI:

Point of Interest

References

  • Ding S, He X, Wang J (2017) Multiobjective optimization model for service node selection based on a tradeoff between quality of service and resource consumption in mobile crowd sensing. IEEE Internet Things J 4(1):258–268

    Google Scholar 

  • El Khatib RF, Zorba N, Hassanein HS (2018) Cost-efficient multi-tasking in coverage-aware mobile crowd sensing. In 2018 14th International Wireless Communications Mobile Computing Conference (IWCMC), p. 594–599

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

    Article  Google Scholar 

  • Gao H, Harold LC, Jian T, Dejun Y, Pan H, Wendong W (2019) Online quality-aware incentive mechanism for mobile crowd sensing with extra bonus. IEEE Trans Mobile Comput 18(11):2589–2603

    Article  Google Scholar 

  • Guo B, Chen H, Han Q, Zhiwen Y, Zhang D, Wang Y (2017) Worker-contributed data utility measurement for visual crowdsensing systems. IEEE Trans Mob Comput 16(8):2379–2391

    Article  Google Scholar 

  • Jiang L-Y, Fan He Y, Wang L-JS, Huang H-p (2017) Quality-aware incentive mechanism for mobile crowd sensing. J Sens 2017:5757125

    Article  Google Scholar 

  • Jiaoyan C, Jingsen Y (2019) Maximizing coverage quality with budget constrained in mobile crowd-sensing network for environmental monitoring applications. Sensors 19(10):2399

    Article  Google Scholar 

  • Jin H, Su L, Chen D, Nahrstedt K, Xu J (2015) Quality of information aware incentive mechanisms for mobile crowd sensing systems. In Proceedings of the 16th ACM International Symposium on Mobile Ad Hoc Networking and Computing, MobiHoc 15. New York, NY, USA., p. 167-176, Association for Computing Machinery

  • Jin H, Su L, Nahrstedt K (2017) Theseus: incentivizing truth discovery in mobile crowd sensing systems. In Proceedings of the 18th ACM International Symposium on Mobile Ad Hoc Networking and Computing, Mobihoc 17. New York, NY, USA. Association for Computing Machinery

  • Ko H, Sangheon P, Leung Victor CM (2019) Coverage-guaranteed and energy-efficient participant selection strategy in mobile crowdsensing. IEEE Internet Things J 6(2):3202–3211

    Article  Google Scholar 

  • Li T, Liu Y, Gao L, Liu A (2017) A cooperative-based model for smart-sensing tasks in fog computing. IEEE Access 5:21296–21311

    Article  Google Scholar 

  • Li M, Gao Y, Wang M, Guo C, Tan X (2019) Multi-objective optimization for multi-task allocation in mobile crowd sensing. Procedia Computer Science, 155:360–368. The 16th International Conference on Mobile Systems and Pervasive Computing (MobiSPC 2019),The 14th International Conference on Future Networks and Communications (FNC-2019),The 9th International Conference on Sustainable Energy Information Technology

  • Luo T, Kanhere SS, Huang J, Das SK, Fan W (2017) Sustainable incentives for mobile crowdsensing: auctions, lotteries, and trust and reputation systems. IEEE Commun Mag 55(3):68–74

    Article  Google Scholar 

  • Mondal S, Ghosh S, Khatua S, Das R, Biswas U (2018) Cost effective algorithms for participant selection problem in mobile crowd sensing environment. In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC), p. 453–458

  • Mondal S, Mitra S, Mukherjee A, Ghosh S, Khatua S, Das A, Das RK (2022) Participant selection algorithms for large-scale mobile crowd sensing environment. Microsyst Technol 28(12):2641–2657

    Article  Google Scholar 

  • Ogie RI (2016) Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework. HCIS 6(1):24

    Google Scholar 

  • Peng D, Fan W, Chen G (2018) Data quality guided incentive mechanism design for crowdsensing. IEEE Trans Mob Comput 17(2):307–319

    Article  Google Scholar 

  • Pouryazdan M, Kantarci B, Soyata T, Foschini L, Song H (2017) Quantifying user reputation scores, data trustworthiness, and user incentives in mobile crowd-sensing. IEEE Access 5:1382–1397

    Article  Google Scholar 

  • Saremi S, Mirjalili SM, Mirjalili S (2014) Chaotic krill herd optimization algorithm. Procedia Technology, 12:180–185. The 7th International Conference Interdisciplinarity in Engineering, INTER-ENG 2013, 10–11 October 2013. Petru Maior University of Tirgu Mures, Romania

  • Song S, Liu Z, Li Z, Xing T, Fang D (2020) Coverage-oriented task assignment for mobile crowdsensing. IEEE Internet Things J 7(8):7407–7418

    Article  Google Scholar 

  • Wang J, Tang J, Yang D, Wang , Xue G (2016) Quality-aware and fine-grained incentive mechanisms for mobile crowdsensing. In 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), p. 354–363

  • Wang J, Wang Y, Zhang D, Wang F, He Y, Ma L (2017) Psallocator: multi-task allocation for participatory sensing with sensing capability constraints. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing, CSCW 17, New York, NY, USA, p. 1139-1151 Association for Computing Machinery

  • Wang C, Li C, Qin C, Wang W, Li X (2018a) Maximizing spatial-temporal coverage in mobile crowd-sensing based on public transports with predictable trajectory. Int J Distrib Sens Netw 14(8):1550147718795351

    Article  Google Scholar 

  • Wang Liang Y, Zhiwen GB, Fei Y, Fei X (2018b) Mobile crowd sensing task optimal allocation: a mobility pattern matching perspective. Front Comput Sci 12:231–244

    Article  Google Scholar 

  • Wang J, Wang L, Wang Y, Zhang D, Kong L (2018c) Task allocation in mobile crowd sensing: state-of-the-art and future opportunities. IEEE Internet Things J 5(5):3747–3757

    Article  Google Scholar 

  • Wang J, Wang Y, Zhang D, Wang F, Xiong H, Chen C, Lv Q, Qiu Z (2018d) Multi-task allocation in mobile crowd sensing with individual task quality assurance. IEEE Trans Mob Comput 17(9):2101–2113

    Article  Google Scholar 

  • Wang G-G, Gandomi AH, Alavi AH, Gong D (2019) A comprehensive review of krill herd algorithm: variants, hybrids and applications. Artif Intell Rev 51(1):119–148

    Article  Google Scholar 

  • Xia L, Tianhua C, Caixia X, Huaiyu D, Vincent Poor H (2018) Mobile crowdsensing games in vehicular networks. IEEE Trans Veh Technol 67(2):1535–1545

    Article  Google Scholar 

  • Yang S, Tang S, Gao X, Yang B, Chen G (2017) On designing data quality-aware truth estimation and surplus sharing method for mobile crowdsensing. IEEE J Sel Areas Commun 35(4):832–847

    Article  Google Scholar 

  • Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330

    Article  Google Scholar 

  • Yu J, Xiao M, Gao G, Hu C (2016) Minimum cost spatial-temporal task allocation in mobile crowdsensing. In: International Conference on Wireless Algorithms, Systems, and Applications, volume 9798, p. 262–271, 08

  • Zhang M, Yang P, Tian C, Tang S, Gao X, Wang B, Xiao F (2016) Quality-aware sensing coverage in budget-constrained mobile crowdsensing networks. IEEE Trans Veh Technol 65(9):7698–7707

    Article  Google Scholar 

  • Yuan J, Zheng Y, Zhang C, Xie W, Xie X, Sun G, Huang Y (2010) T-drive: driving directions based on taxi trajectories. In: Proceedings of the 18th SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS '10. New York, NY, USA, ACM, pp 99–108

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Authors and Affiliations

  1. Department of Computer Science and Information Technology, ITER, Siksha ’O’ Anusandhan (Deemed to be University), Bhubaneswar, India

    Sanjoy Mondal

  2. Department of Computer Science and Engineering, Aliah University, Kolkata, India

    Abhishek Das

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  1. Sanjoy Mondal
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  2. Abhishek Das
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Correspondence to Sanjoy Mondal.

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Mondal, S., Das, A. Quality aware cost efficient reward mechanism in mobile crowdsensing system with uncertainty constraints. Microsyst Technol 30, 381–390 (2024). https://doi.org/10.1007/s00542-023-05495-w

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