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).
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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
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DOI: https://doi.org/10.1007/978-3-030-62460-6_35
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