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01-07-2023 | Technical Paper

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

Authors: Sanjoy Mondal, Abhishek Das

Published in: Microsystem Technologies | Issue 4/2024

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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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Literature
go back to reference 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 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
go back to reference 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 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
go back to reference Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39CrossRef Ganti RK, Ye F, Lei H (2011) Mobile crowdsensing: current state and future challenges. IEEE Commun Mag 49(11):32–39CrossRef
go back to reference 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):2589–2603CrossRef 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):2589–2603CrossRef
go back to reference 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):2379–2391CrossRef 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):2379–2391CrossRef
go back to reference Jiang L-Y, Fan He Y, Wang L-JS, Huang H-p (2017) Quality-aware incentive mechanism for mobile crowd sensing. J Sens 2017:5757125CrossRef Jiang L-Y, Fan He Y, Wang L-JS, Huang H-p (2017) Quality-aware incentive mechanism for mobile crowd sensing. J Sens 2017:5757125CrossRef
go back to reference Jiaoyan C, Jingsen Y (2019) Maximizing coverage quality with budget constrained in mobile crowd-sensing network for environmental monitoring applications. Sensors 19(10):2399CrossRef Jiaoyan C, Jingsen Y (2019) Maximizing coverage quality with budget constrained in mobile crowd-sensing network for environmental monitoring applications. Sensors 19(10):2399CrossRef
go back to reference 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, 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
go back to reference 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 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
go back to reference 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):3202–3211CrossRef 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):3202–3211CrossRef
go back to reference Li T, Liu Y, Gao L, Liu A (2017) A cooperative-based model for smart-sensing tasks in fog computing. IEEE Access 5:21296–21311CrossRef Li T, Liu Y, Gao L, Liu A (2017) A cooperative-based model for smart-sensing tasks in fog computing. IEEE Access 5:21296–21311CrossRef
go back to reference 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 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
go back to reference 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):68–74CrossRef 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):68–74CrossRef
go back to reference 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, 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
go back to reference 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):2641–2657CrossRef 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):2641–2657CrossRef
go back to reference Ogie RI (2016) Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework. HCIS 6(1):24 Ogie RI (2016) Adopting incentive mechanisms for large-scale participation in mobile crowdsensing: from literature review to a conceptual framework. HCIS 6(1):24
go back to reference Peng D, Fan W, Chen G (2018) Data quality guided incentive mechanism design for crowdsensing. IEEE Trans Mob Comput 17(2):307–319CrossRef Peng D, Fan W, Chen G (2018) Data quality guided incentive mechanism design for crowdsensing. IEEE Trans Mob Comput 17(2):307–319CrossRef
go back to reference 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 5:1382–1397CrossRef 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 5:1382–1397CrossRef
go back to reference 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 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
go back to reference Song S, Liu Z, Li Z, Xing T, Fang D (2020) Coverage-oriented task assignment for mobile crowdsensing. IEEE Internet Things J 7(8):7407–7418CrossRef Song S, Liu Z, Li Z, Xing T, Fang D (2020) Coverage-oriented task assignment for mobile crowdsensing. IEEE Internet Things J 7(8):7407–7418CrossRef
go back to reference 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, 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
go back to reference 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 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
go back to reference 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):1550147718795351CrossRef 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):1550147718795351CrossRef
go back to reference Wang Liang Y, Zhiwen GB, Fei Y, Fei X (2018b) Mobile crowd sensing task optimal allocation: a mobility pattern matching perspective. Front Comput Sci 12:231–244CrossRef Wang Liang Y, Zhiwen GB, Fei Y, Fei X (2018b) Mobile crowd sensing task optimal allocation: a mobility pattern matching perspective. Front Comput Sci 12:231–244CrossRef
go back to reference 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):3747–3757CrossRef 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):3747–3757CrossRef
go back to reference 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):2101–2113CrossRef 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):2101–2113CrossRef
go back to reference 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):119–148CrossRef 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):119–148CrossRef
go back to reference Xia L, Tianhua C, Caixia X, Huaiyu D, Vincent Poor H (2018) Mobile crowdsensing games in vehicular networks. IEEE Trans Veh Technol 67(2):1535–1545CrossRef Xia L, Tianhua C, Caixia X, Huaiyu D, Vincent Poor H (2018) Mobile crowdsensing games in vehicular networks. IEEE Trans Veh Technol 67(2):1535–1545CrossRef
go back to reference 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):832–847CrossRef 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):832–847CrossRef
go back to reference Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330CrossRef Yick J, Mukherjee B, Ghosal D (2008) Wireless sensor network survey. Comput Netw 52(12):2292–2330CrossRef
go back to reference 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 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
go back to reference 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):7698–7707CrossRef 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):7698–7707CrossRef
go back to reference 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 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
Metadata
Title
Quality aware cost efficient reward mechanism in mobile crowdsensing system with uncertainty constraints
Authors
Sanjoy Mondal
Abhishek Das
Publication date
01-07-2023
Publisher
Springer Berlin Heidelberg
Published in
Microsystem Technologies / Issue 4/2024
Print ISSN: 0946-7076
Electronic ISSN: 1432-1858
DOI
https://doi.org/10.1007/s00542-023-05495-w