Volume 3,Issue 9
Task Allocation in Mobile Crowdsensing for Smart Agriculture: A Survey
Mobile Crowdsensing (MCS) serves as a scalable data acquisition paradigm for smart agriculture, supporting a range of applications from soil monitoring to crop assessment. A core challenge lies in efficiently allocating sensing tasks to distributed participants (e.g., farmers), which directly impacts task execution efficiency, cost control, and overall system effectiveness. This paper provides a systematic review of MCS task allocation methods for agricultural scenarios, covering classical greedy strategies, evolutionary algorithms, and others, aimed at addressing practical constraints such as dynamic field environments and limited resources. By synthesizing existing research, this survey summarizes current trends, identifies unresolved issues, and proposes future directions for developing more robust task allocation mechanisms, with the goal of fully leveraging the potential of MCS in precision agriculture.
[1] Ma H, Zhao D, Yuan P, 2014, Opportunities in Mobile Crowd Sensing. IEEE Communications Magazine, 52(8): 29–35.
[2] Sun Y, Ding W, Shu L, et al., 2022, On Enabling Mobile Crowd Sensing for Data Collection in Smart Agriculture: A Vision. IEEE Systems Journal, 16(1): 132–143.
[3] Suhag D, Jha V, 2023, A Comprehensive Survey on Mobile Crowdsensing Systems. Journal of Systems Architecture, 2023(142): 1–28.
[4] Yu J, Xiao M, Gao G, et al., 2016, Minimum Cost Spatial-temporal Task Allocation in Mobile Crowdsensing. International Conference on Wireless Algorithms, Systems, Applications, 262–271.
[5] Guo B, Yan L, Wu W, et al., 2017, ActiveCrowd: A Framework for Optimized Multitask Allocation in Mobile Crowdsensing Systems. IEEE Transactions on Human-Machine Systems, 47(3): 392–403.
[6] Song Z, Liu CH, Wu J, et al., 2014, QoI-aware Multitask-oriented Dynamic Participant Selection with Budget Constraints. IEEE Transactions on Vehicular Technology, 63(9): 4618–4632.
[7] Wang Z, Zhao J, Hu J, et al., 2020, Towards Personalized Task-oriented Worker Recruitment in Mobile Crowdsensing. IEEE Transactions on Mobile Computing, 20(5): 2080–2093.
[8] Zhao D, Li XY, Ma H, 2016, Budget-feasible Online Incentive Mechanisms for Crowdsourcing Tasks Truthfully. IEEE/ACM Transactions on Networking, 24(2): 647–661.
[9] Gao H, Liu CH, Tang J, et al., 2019, Online Quality-aware Incentive Mechanism for Mobile Crowd Sensing with Extra Bonus. IEEE Transactions on Mobile Computing, 18(11): 2589–2603.
[10] Li H, Li T, Wang W, et al., 2018, Dynamic Participant Selection for Large-scale Mobile Crowd Sensing. IEEE Transactions on Mobile Computing, 18(12): 2842–2855.
[11] Wang X, Jia R, Tian X, et al., 2018, Dynamic Task Assignment in Crowdsensing with Location Awareness and Location Diversity. IEEE Conference on Computer Communications, 2420–2428.
[12] Xiong H, Zhang D, Wang L, et al., 2015, EMC3: Energy-efficient Data Transfer in Mobile Crowdsensing under Full Coverage Constraint. IEEE Transactions on Mobile Computing, 14(7): 1355–1368.
[13] Wang J, 2016, Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget. IEEE Internet of Things Journal, 3(6): 1395–1405.
[14] Zhang D, Xiong H, Wang L, et al., 2014, CrowdRecruiter: Selecting Participants for Piggyback Crowdsensing under Probabilistic Coverage Constraint. UbiComp 2014 — Proceedings of the 2014 ACM International Joint Conference on Pervasive and Ubiquitous Computing, 703–714.
[15] Majeed DM, Zhang L, Shi K, 2020, Optimal Data Collection for Mobile Crowdsensing Over Integrated Cellular and Opportunistic Networks. IEEE Access, 2020(8): 157270–157283.
[16] Karaliopoulos M, Telelis O, Koutsopoulos I, 2015, User Recruitment for Mobile Crowdsensing over Opportunistic Networks. Proceedings — IEEE INFOCOM, 2015(26): 2254–2262.
[17] Li MC, Gao Y, Wang ML, et al., 2019, Multi-objective Optimization for Multi-task Allocation in Mobile Crowd Sensing. Procedia Computer Science, 2019(155): 360–368.
[18] Ji J, Guo Y, Yang X, et al., 2023, Generative Adversarial Networks-based Dynamic Multi-objective Task Allocation Algorithm for Crowdsensing. Information Sciences, 2023(647): 119472.
[19] Shen XN, Chen QZ, Pan HL, et al., 2022, Variable Speed Multi-task Allocation for Mobile Crowdsensing Based on a Multi-objective Shuffled Frog Leaping Algorithm. Applied Soft Computing, 2022(127): 109330.
[20] Peng T, You W, Guan KJ, et al., 2024, Privacy-preserving Multiobjective Task Assignment Scheme with Differential Obfuscation in Mobile Crowdsensing. Journal of Network and Computer Applications, 2024(224): 103836.
[21] Lin RN, Huang YK, Zhang YY, et al., 2024, Achieving Lightweight, Efficient, Privacy-preserving User Recruitment in Mobile Crowdsensing. Journal of Information Security and Applications, 2024(85): 103854.
[22] Wang JT, Wang YS, Zhang DQ, et al., 2017, PSAllocator: Multi-Task Allocation for Participatory Sensing with Sensing Capability Constraints. In Proceedings of the 2017 ACM Conference on Computer Supported Cooperative Work and Social Computing (CSCW ‘17). Association for Computing Machinery, New York, NY, USA, 1139–1151.
[23] Reddy SK, 2010, Towards Design Guidelines for Participatory Sensing Campaigns (Order No. 3437543), thesis, University of California.
[24] Singla A, Krause A, 2013, Incentives for Privacy Tradeoff in Community Sensing. In Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (Vol. 1, pp. 165–173).
[25] Wang E, Yang Y, Wu J, et al., 2019, User Recruitment System for Efficient Photo Collection in Mobile Crowdsensing. IEEE Transactions on Human-Machine Systems, 50(1): 1–12.
[26] Yang F, Lu JL, Zhu Y, et al., 2015, Heterogeneous Task Allocation in Participatory Sensing. In 2015 IEEE Global Communications Conference (GLOBECOM) (pp. 1–6).
[27] Tao X, Song W, 2019, Location-dependent Task Allocation for Mobile Crowdsensing with Clustering Effect. IEEE Internet of Things Journal, 6(1): 1029–1045.
[28] Ipaye AA, Chen Z, Asim M, et al., 2022, Location and Time Aware Multitask Allocation in Mobile Crowd-sensing Based on Genetic Algorithm. Sensors, 22(8): 3013.
[29] Li X, Zhang X, 2019, Multi-task Allocation under Time Constraints in Mobile Crowdsensing. IEEE Transactions on Mobile Computing, 20(4): 1494–1510.
[30] Tao X, Song W, 2020, Profit-oriented Task Allocation for Mobile Crowdsensing with Worker Dynamics: Cooperative Offline Solution and Predictive Online Solution. IEEE Transactions on Mobile Computing, 20(8): 2637–2653.
[31] Li K, Zhang T, Wang R, 2020, Deep Reinforcement Learning for Multiobjective Optimization. IEEE Transactions on Cybernetics, 51(6): 3103–3114.
[32] Xu C, Song W, 2023, Intelligent Task Allocation for Mobile Crowdsensing with Graph Attention Network and Deep Reinforcement Learning. IEEE Transactions on Network Science and Engineering, 10(2): 1032–1048.