[1] LI X, ZHANG X L. Multi-task allocation under time constraints in mobile crowdsensing[J]. IEEE Transactions on Mobile Computing, 2021, 20(4):1494-1510. [2] GUO W Z, ZHU W P, YU Z Y, et al. A survey of task allocation:contrastive perspectives from wireless sensor networks and mobile crowdsensing[J]. IEEE Access, 2019, 7:78406-78420. [3] BEN MESSAOUD R, REJIBA Z, GHAMRI-DOUDANE Y. An energy-aware end-to-end crowdsensing platform:sensarena[C]//2016 13th IEEE Annual Consumer Communications & Networking Conference. Piscataway, NJ:IEEE Press, 2016:284-285. [4] ANTONIĆ A, MARJANOVIĆ M, PRIPUŽIĆ K, et al. A mobile crowd sensing ecosystem enabled by CUPUS:cloud-based publish/subscribe middleware for the Internet of things[J]. Future Generation Computer Systems, 2016, 56:607-622. [5] WU D P, YANG Z G, YANG B R, et al. From centra-lized management to edge collaboration:a privacy-preserving task assignment framework for mobile crowdsensing[J]. IEEE Internet of Things Journal, 2021, 8(6):4579-4589. [6] BELLAVISTA P, BELLI D, CHESSA S, et al. A social-driven edge computing architecture for mobile crowd sen-sing management[J]. IEEE Communications Magazine, 2019, 57(4):68-73. [7] ZHOU P, CHEN W B, JI S L, et al. Privacy-preserving online task allocation in edge-computing-enabled massive crowdsensing[J]. IEEE Internet of Things Journal, 2019, 6(5):7773-7787. [8] WANG J T, WANG F, WANG Y S, et al. Social-network-assisted worker recruitment in mobile crowd sensing[J]. IEEE Transactions on Mobile Computing, 2019, 18(7):1661-1673. [9] LU A Q, ZHU J H. Hybrid network assisted dynamic worker recruitment algorithm[C]//2019 IEEE International Conference on Smart Internet of Things (Smart-IoT). Piscataway, NJ:IEEE Press, 2019:254-261. [10] LU A Q, ZHU J H. Worker recruitment with cost and time constraints in mobile crowd sensing[J]. Future Generation Computer Systems, 2020, 112:819-831. |