nach oben

Erschienen in:

2024 | OriginalPaper | Buchkapitel

4. Collaborative Incentive Mechanism for Mobile Crowdsensing

verfasst von : Youqi Li, Fan Li, Song Yang, Chuan Zhang

Erschienen in: Incentive Mechanism for Mobile Crowdsensing

Verlag: Springer Nature Singapore

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

In this chapter, we propose PTASIM, an incentive mechanism that explores cooperation with POI-tagging App for Mobile Edge Crowdsensing (MEC). PTASIM requests the App to tag some edges to be POI (Points-of-Interest), which further guides App users to perform tasks at that location. We further model the interactions of users, a platform, and an App by a three-stage decision process. The App first determines the POI-tagging price to maximize its payoff. Platform and users subsequently decide how to determine tasks reward and select edges to be tagged, and how to select the best task to perform, respectively. We analyze the optimal solution in those stages. Specifically, we prove greedy algorithm could provide the optimal solution for the platform’s payoff maximization in polynomial time. The numerical results show that: (1) the cooperation with App brings long-term and sufficient participation; the optimal strategies reduce the platform’s tasks cost as well as improve App’s revenues.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Fair Incentive Mechanism for Mobile Crowdsensing

Nächstes Kapitel Coopetition-Aware Incentive Mechanism for Mobile Crowdsensing

We assume participant recruitment happens in a specific short period so that the total number of users is fixed during that time. Therefore, we apply the participant ratio as the requirement for the rest of this chapter, which is the same as the required number of users.

In this chapter, we assume POI-tagging App easily guides its users to perform tasks. And the quality and capability of performing tasks for normal users and App users are the same.

In this chapter, network effect represents App’s popularity and concentration which generates a positive externality for App users. Due to limited wireless bandwidth, we also consider the congestion effect which is a negative effect on all users simultaneously using the network to transfer the sensing data at the locations.

\(f_{app}\). is user’s intrinsic attribute and independent of tagging tasks as POI.

Wang, J., Wang, Y., Zhang, D., Goncalves, J., Ferreira, D., Visuri, A., Ma, S.: Learning-assisted optimization in mobile crowd sensing: a survey. In: IEEE TII (2018)

Chen, H., Li, F., Hei, X., Wang, Y.: Crowdx: Enhancing automatic construction of indoor floorplan with opportunistic encounters. ACM UbiComp 2(4), 159 (2018)

Wang, D., Peng, Y., Ma, X., Ding, W., Jiang, H., Chen, F.; and Jiangchuan Liu. Adaptive wireless video streaming based on edge computing: Opportunities and approaches. IEEE Trans. Serv. Comput. 12(5), 685–697 (2018)

Zhou, Z., Chen, X., Li, E., Zeng, L., Luo, K., Zhang, J.: Edge intelligence: Paving the last mile of artificial intelligence with edge computing. Proc. IEEE 107(8), 1738–1762 (2019)CrossRef

Marjanović, M., Antonić, A., Žarko, I.P.: Edge computing architecture for mobile crowdsensing. IEEE Access 6, 10662–10674 (2018)CrossRef

Li, T., Qiu, Z., Cao, L., Li, H., Guo, Z., Li, F., Shi, X., Wang, Y.: Participant grouping for privacy preservation in mobile crowdsensing over hierarchical edge clouds. In: IPCCC (2018)

Wang, D., Fan, J., Xiao, Z., Jiang, H., Chen, H., Zeng, F., Li, K.: Stop-and-wait: discover aggregation effect based on private car trajectory data. IEEE Trans. Intell. Trans. Syst. 20(10), 3623–3633 (2018)CrossRef

Zhang, X., Yang, Z., Sun, W., Liu, Y., Tang, S., Xing, K., Mao, X.: Incentives for mobile crowd sensing: A survey. IEEE Commun. Surv. Tutorials 18(1), 54–67 (2016)CrossRef

Guo, B, Liu, Y, Wang, L., Li, V.O.K., Jacqueline, C.K., and Yu, Z.: Task allocation in spatial crowdsourcing: current state and future directions. IEEE Internet Things 5, 1749–1764 (2018)CrossRef

10.

Wang, L., Yu, Z., Zhang, D., Guo, B., Liu, C.H.: Heterogeneous multi-task assignment in mobile crowdsensing using spatiotemporal correlation. In: IEEE TMC (2018)

11.

Colley, A., Thebault-Spieker, J., Lin, A.Y., Degraen, D., Fischman, B., Häkkilä, J., Kuehl, K., Nisi, V., Nunes, N.J., Wenig, N., et al.: The geography of pokémon go: beneficial and problematic effects on places and movement. In: ACM CHI (2017)

12.

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)CrossRef

13.

Zhang, D., Tan, L., Ren, J., Awad, M.K., Zhang, S., Zhang, Y., Wan, P.-J.: Near-optimal and truthful online auction for computation offloading in green edge-computing systems. In: IEEE TMC (2019)

14.

Tang, W., Ren, J., Zhang, Y.: Enabling trusted and privacy-preserving healthcare services in social media health networks. IEEE TMM 21(3), 579–590 (2018)

15.

Zhu, L., Zhang, C., Xu, C., Sharif, K.: Rtsense: Providing reliable trust-based crowdsensing services in CVCC. IEEE Netw. 32(3), 20–26 (2018)CrossRef

16.

Zhang, C., Zhu, L., Xu, C., Liu, X., Sharif, K.: Reliable and privacy-preserving truth discovery for mobile crowdsensing systems. IEEE TDSC (2019)

17.

Yang, D., Xue, G., Fang, X., Tang, J.: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: ACM MobiCom (2012)

18.

Gao, L., Hou, F., Huang, J.: Providing long-term participation incentive in participatory sensing. In: IEEE INFOCOM (2015)

19.

Zhang, X., Yang, Z., Zhou, Z., Cai, H., Chen, L., Li, X.: Free market of crowdsourcing: Incentive mechanism design for mobile sensing. IEEE TPDS 25(12), 3190–3200 (2014)

20.

Chen, Y., Li, B., Zhang, Q.: Incentivizing crowdsourcing systems with network effects. In: IEEE INFOCOM (2016)

21.

Cheung, M.H., Hou, F., Huang, J.: Make a difference: Diversity-driven social mobile crowdsensing. In: IEEE INFOCOM (2017)

22.

Zhang, X., Xue, G., Yu, R., Yang, D., Tang, J.: Truthful incentive mechanisms for crowdsourcing. In: IEEE INFOCOM (2015)

23.

Jin, H., Su, L., Nahrstedt, K.: Centurion: incentivizing multi-requester mobile crowd sensing. In: IEEE INFOCOM (2017)

24.

Liu, Y., Guo, B., Wang, Y., Wu, W., Yu, Z., Zhang, D.: Taskme: multi-task allocation in mobile crowd sensing. In: ACM UbiComp (2016)

25.

Zhang, X., Yang, Z., Liu, Y., Tang, S.: On reliable task assignment for spatial crowdsourcing. IEEE Trans. Emerg. Topics. Comput. 7(1), 174–186 (2016)CrossRef

26.

Yu, H., Iosifidisy, S., Biying, L., Huang, J.: Market your venue with mobile applications: Collaboration of online and offline businesses. In: IEEE INFOCOM (2018)

27.

Yu, H., Cheung, M.H., Gao, L., Huang, J.: Economics of public Wi-Fi monetization and advertising. In: IEEE INFOCOM (2016)

28.

Gong, X., Duan, L., Chen, X., Zhang, J.: When social network effect meets congestion effect in wireless networks: data usage equilibrium and optimal pricing. IEEE JSAC 35(2), 449–462 (2017)

29.

Zhang, M., Gao, L., Huang, J., Honig, M.: Cooperative and competitive operator pricing for mobile crowdsourced internet access. In: IEEE INFOCOM (2017)

30.

Nisan, N., Roughgarden, T., Tardos, E., Vazirani, V.V.: Algorithmic Game Theory. Cambridge University Press, Cambridge (2007)CrossRef

31.

Fudenberg, D., Tirole, J.: Game theory. Technical Report, The MIT Press, 1991

32.

Lawler, E.L.: Combinatorial Optimization: Networks and Matroids. Courier Corporation, Chelmsford (1976)

33.

Yu, H., Neely, M.J.: A new backpressure algorithm for joint rate control and routing with vanishing utility optimality gaps and finite queue lengths. IEEE/ACM ToN 26(4), 1605–1618 (2018)CrossRef

34.

Gilmore, P.C., Gomory, R.E.: A linear programming approach to the cutting-stock problem. Oper. Res. 9(6), 849–859 (1961)MathSciNetCrossRef

35.

Vasserman, S., Michal F., Avinatan H.: Implementing the wisdom of waze. In: Twenty-Fourth International Joint Conference on Artificial Intelligence (IJCAI), pp. 660–666. AAAI Press (2015)

Titel: Collaborative Incentive Mechanism for Mobile Crowdsensing
verfasst von: Youqi Li
Fan Li
Song Yang
Chuan Zhang
Verlag: Springer Nature Singapore
Buch: Incentive Mechanism for Mobile Crowdsensing
Print ISBN: 978-981-9969-20-3

Electronic ISBN: 978-981-9969-21-0

Copyright-Jahr: 2024
DOI: https://doi.org/10.1007/978-981-99-6921-0_4

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"

Premium Partner