Abstract
Mobile crowdsensing (MCS) has become a new paradigm of massive sensory data collection, analysis and exploration. Most studies in MCS tend to focus only on the goal of maximizing the social utility without considering the social fairness. The main challenge for considering social fairness in the multi-term multi-task allocation (MMA) problem lies in how to achieve a balance between social utility and social fairness in the multi-term. In order to maintain social fairness and stimulate mobile users to compete for sensing tasks, this is the first paper to introduce the weighted max-min fairness updated depending on the max-min fairness into the multi-term multi-task allocation problem. Using a deterministic local search (DLS) auction mechanism, we design a novel MMA algorithm which can work out the multi-task allocation problem and maintain the social fairness in the long term. Finally, extensive evaluation results show that our approach has a good performance which can balance utility and fairness with a relatively steady value of PoF.
This work was supported in part by the National Natural Science Foundation of China (No. 62072411, 61872323, 61751303), in part by the Social Development Project of Zhejiang Provincial Public Technology Research (No. 2017C33054), in part by the Natural Science Foundation of Guangdong Province (No. 2018A030313061), and in part by the Guangdong Science and Technology Plan (no. 2017B010124001, 201902020016, 2019B010139001).
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
Similar content being viewed by others
References
Xiao, L., Li, Y., Han, G., Dai, H., Poor, H.V.: A secure mobile crowdsensing game with deep reinforcement learning. IEEE Trans. Inf. Forensics Secur. 13(1), 35–47 (2018)
Sun, W., Liu, J.: Congestion-aware communication paradigm for sustainable dense mobile crowdsensing. IEEE Commun. Mag. 55(3), 62–67 (2017)
Meng, Y., Jiang, C., Quek, T.Q.S., Han, Z., Ren, Y.: Social learning based inference for crowdsensing in mobile social networks. IEEE Trans. Mob. Comput. 17(8), 1966–1979 (2018)
Jayaraman, P.P., Bártolo Gomes, J., Nguyen, H., Abdallah, Z.S., Krishnaswamy, S., Zaslavsky, A.: Scalable energy-efficient distributed data analytics for crowdsensing applications in mobile environments. IEEE Trans. Comput. Soc. Syst. 2(3), 109–123 (2015)
Pryss, R., Schobel, J., Reichert, M.: Requirements for a flexible and generic API enabling mobile crowdsensing mHealth applications. In: 2018 4th International Workshop on Requirements Engineering for Self-Adaptive, Collaborative, and Cyber Physical Systems (RESACS), Banff, AB, pp. 24–31 (2018)
Zhang, J., Wang, D.: Duplicate report detection in urban crowdsensing applications for smart city. In: 2015 IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity), Chengdu, pp. 101–107 (2015)
Zhang, Y., Zhang, D., Li, Q., Wang, D.: Towards optimized online task allocation in cost-sensitive crowdsensing applications. In: 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC), Orlando, FL, USA, pp. 1–8 (2018)
Wang, J., Tang, J., Yang, D., Wang, E., Xue, G.: Quality-aware and fine-grained incentive mechanisms for mobile crowdsensing. In: 2016 IEEE 36th International Conference on Distributed Computing Systems (ICDCS), Nara, pp. 354–363 (2016)
Wang, L., Yu, Z., Zhang, D., Guo, B., Liu, C.H.: Heterogeneous multi-task assignment in mobile crowdsensing using spatiotemporal correlation. IEEE Trans. Mob. Comput. 18(1), 84–97 (2019)
Zhou, P., Chen, W., Ji, S., Jiang, H., Yu, L., Wu, D.: Privacy-preserving online task allocation in edge-computing-enabled massive crowdsensing. IEEE Internet Things J. 6(5), 7773–7787 (2019)
Wang, Z., et al.: Personalized privacy-preserving task allocation for mobile crowdsensing. IEEE Trans. Mob. Comput. 18(6), 1330–1341 (2019)
Hu, A., Gu, Y.: Mobile crowdsensing tasks allocation for multi-parameter bids. In: 2017 IEEE 3rd Information Technology and Mechatronics Engineering Conference (ITOEC), Chongqing, pp. 489–493 (2017)
Tao, X., Song, W.: Location-dependent task allocation for mobile crowdsensing with clustering effect. IEEE Internet Things J. 6(1), 1029–1045 (2019)
Zhang, Y., Zhang, D., Vance, N., Wang, D.: Optimizing online task allocation for multi-attribute social sensing. In: 2018 27th International Conference on Computer Communication and Networks (ICCCN), Hangzhou, pp. 1–9 (2018)
Meng, J., Tan, H., Li, X., Han, Z., Li, B.: Online deadline-aware task dispatching and scheduling in edge computing. IEEE Trans. Parallel Distrib. Syst. 31(6), 1270–1286 (2020)
Xiao, M., Wu, J., Huang, L., Cheng, R., Wang, Y.: Online task assignment for crowdsensing in predictable mobile social networks. IEEE Trans. Mob. Comput. 16(8), 2306–2320 (2017)
Wang, X., Jia, R., Tian, X., Gan, X.: Dynamic task assignment in crowdsensing with location awareness and location diversity. In: IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, Honolulu, HI, pp. 2420–2428 (2018)
Nie, J., Luo, J., Xiong, Z., Niyato, D., Wang, P.: A Stackelberg game approach toward socially-aware incentive mechanisms for mobile crowdsensing. IEEE Trans. Wirel. Commun. 18(1), 724–738 (2019)
Chen, X., Deng, B.: Task allocation schemes for crowdsourcing in opportunistic mobile social networks. In: 2018 International Conference on Computing, Networking and Communications (ICNC), Maui, HI, pp. 615–619 (2018)
Huang, H., Xin, Y., Sun, Y., Yang, W.: A truthful double auction mechanism for crowdsensing systems with max-min fairness. In: 2017 IEEE Wireless Communications and Networking Conference (WCNC), San Francisco, CA, pp. 1–6 (2017)
Li, X., Shankaran, R., Orgun, M.A., Fang, G., Xu, Y.: Resource allocation for underlay D2D communication with proportional fairness. IEEE Trans. Veh. Technol. 67(7), 6244–6258 (2018)
Huang, J., Bi, J.: A proportional fairness scheduling for wireless sensor networks. In: 2015 International Conference on Identification, Information, and Knowledge in the Internet of Things (IIKI), Beijing, pp. 266–271 (2015)
Feige, U., Mirrokni, V.S., Vondrak, J.: Maximizing non-monotone submodular functions. In: 48th Annual IEEE Symposium on Foundations of Computer Science (FOCS 2007), Providence, RI, pp. 461–471 (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Liu, H., Jiang, W., Yang, S., Lu, J., Ning, D. (2020). Multi-term Multi-task Allocation for Mobile Crowdsensing with Weighted Max-Min Fairness. In: Chen, X., Yan, H., Yan, Q., Zhang, X. (eds) Machine Learning for Cyber Security. ML4CS 2020. Lecture Notes in Computer Science(), vol 12487. Springer, Cham. https://doi.org/10.1007/978-3-030-62460-6_35
Download citation
DOI: https://doi.org/10.1007/978-3-030-62460-6_35
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-62459-0
Online ISBN: 978-3-030-62460-6
eBook Packages: Computer ScienceComputer Science (R0)