Abstract
Crowd computing is a new paradigm, in which a group of users are coordinated to deal with a huge job or huge amounts of data that one user cannot easily do. In this paper, we design an Opportunistic Crowd Computing system (OCC) for mobile social networks (MSNs). Unlike traditional crowd computing systems, the mobile users in OCC move around and communicate each other by using short-distance wireless communication mechanisms (e.g., WiFi or Bluetooth) when they encounter each other, so as to save communication costs. The key design of OCC is the task assignment scheme. Unlike the traditional crowd computing task assignment problem, the task assignment in OCC must take into consideration the users’ mobile behaviors. To solve this problem, we present an optimal user group algorithm (OUGA). It can minimize the total cost, while ensuring the task completion rates. Moreover, we conduct a performance analysis, and prove the optimality of this algorithm. In addition, the simulations show that our algorithm achieves a good performance.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alonso, O., Rose, D.E., Stewart, B.: Crowdsourcing for relevance evaluation. ACM SIGIR Forum Arch. 42, 9–15 (2008)
Guo, X., Chan, E.C.L., Liu, C., Wu, K., Liu, S., Ni, L.M.: ShopProfiler: profiling shops with crowdsourcing data. In: INFOCOM, pp. 1240–1248 (2014)
Murray, D.G., Yoneki, E., Crowcroft, J., Hand, S.: The case for crowd computing. In: MobiHeld 10 Proceedings of the Second ACM SIGCOMM, pp. 39–44 (2010)
Hu, X., Li, X., Ngai, E.C., Leung, V.C., Kruchten, P.: Multidimensional context-aware social network architecture for mobile crowdsensing. IEEE Commun. Mag. 52, 78C–87 (2014)
Li, M., Li, P.: Crowdsourcing in cyber-physical systems: stochastic optimization with strong stability. IEEE Trans. Emerg. Top. Comput. 1(2), 218–231 (2013)
Ma, H., Zhao, D., Yuan, P.: Opportunities in mobile crowd sensing. IEEE Commun. Mag. 52(8), 29–35 (2014)
He, S., Shin, D., Zhang, J., Chen, J.: Toward optimal allocation of location dependent tasks in crowdsensing. In: IEEE INFOCOM, pp. 745–753 (2014)
Weppner, J., Lukowicz, P.: Bluetooth based collaborative crowd density estimation with mobile phones. In: IEEE PerCom (2013)
Krontiris, I., Dimitriou, T.: Privacy-respecting discovery of data providers in crowd-sensing applications. In: IEEE DCOSS (2013)
Ganti, R.K., Ye, F., Lei, H.: Mobile crowdsensing: current state and future challenges. IEEE Commun. Mag. 49, 32C–39 (2011)
Li, Y., Jiang, Y., Jin, D., Su, L., Zeng, L., Wu, D.: Energy-efficient optimal opportunistic forwarding for delay-tolerant networks. IEEE Trans. Veh. Technol. 59, 4500–4512 (2010)
Wang, X., Chen, M., Han, Z., Wu, D.O., Kwon, T.T.: TOSS: traffic offloading by social network service-based opportunistic sharing in mobile social networks. In: INFOCOM, pp. 2346–2354 (2014)
Demirbas, M., Bayir, M., Akcora, C., Yilmaz, Y.: Crowd-sourced sensing and collaboration using twitter. In: IEEE WoWMoM, pp. 1–9 (2010)
Simoens, P., Xiao, Y., Pillai, P.: Scalable crowd-sourcing of video from mobile devices. In: ACM MobiSys, pp. 139–152 (2013)
Yang, D., Xue, G., Fang, X.: Crowdsourcing to smartphones: incentive mechanism design for mobile phone sensing. In: ACM MobiSys, pp. 173–184 (2012)
Hu, X., Chu, T.H.S., Chan, H.C.B., Leung, V.C.M.: Vita: a crowdsensing-oriented mobile cyber-physical system. IEEE Commun. Mag. 1, 78–87 (2014)
Ra, M., Liu, B., Porta, T., Govindan, R.: Medusa: a programming framework for crowd-sensing applications. In: ACM MobiSys, pp. 337–350 (2012)
Mathur, S., Jin, T., Kasturirangan, N.: ParkNet: driveby sensing of road-side parking statistics. In: ACM MobiSys, pp. 123–136 (2010)
Xiao, M., Wu, J., Huang, L., Wang, Y., Liu, C.: Multi-task assignment for crowdsensing in mobile social networks. In: IEEE INFOCOM (2015)
Ganti, R., Pham, N., Ahmadi, H.: GreenGps: a participatory sensing fuel-efficient maps application. In: ACM MobiSys, pp. 151–164 (2010)
Mohan, P., Padmanabhan, V.N., Ramjee, R.: Nericell: rich monitoring of road and traffic conditions using mobile smartphones. In: ACM SenSys, pp. 323–336 (2008)
Acknowledgment
This research was supported in part by the National Natural Science Foundation of China (NSFC) (Grant No. 61572457, 61572336, 61502261, 61379132), and the Natural Science Foundation of Jiangsu Province in China (Grant No. BK20131174, BK2009150).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this paper
Cite this paper
Mao, H., Xiao, M., Liu, A., Li, J., Hu, Y. (2016). OCC: Opportunistic Crowd Computing in Mobile Social Networks. In: Gao, H., Kim, J., Sakurai, Y. (eds) Database Systems for Advanced Applications. DASFAA 2016. Lecture Notes in Computer Science(), vol 9645. Springer, Cham. https://doi.org/10.1007/978-3-319-32055-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-32055-7_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-32054-0
Online ISBN: 978-3-319-32055-7
eBook Packages: Computer ScienceComputer Science (R0)