Abstract
In the current Ocean Internet of Things (OIoT), the data collected by underwater nodes need to be transmitted to the data center through the multi-hop path, which consumes a lot of resources. Based on our observations, there are a large number of ships with sufficient energy in OIoT; using these ships to transmit information will effectively save the energy of underwater nodes and improve transmission efficiency. However, how to transmit underwater node information to ships with different routes is a challenging issue. To address this problem, we propose a distributed spatial crowdsourcing task allocation scheme based on OIoT. In this scheme, the underwater nodes use the distributed spatial crowdsourcing method to assign tasks to ships in OIoT and use ships’ communication ability to transfer information to the data center. First, we propose a spatial crowdsourcing task allocation algorithm based on ship confidence (ShipCon-SCTA), in which underwater nodes are task publishers and ships are workers. It distinguishes the quality of the ship and preferentially selects high-quality ships to improve the stability of data transmission. Second, when no ship accepts the task, we use the ship and its adjacent nodes as the secondary task publisher. Third, due to the need for data aggregation, homomorphic encryption is used to ensure the task’s security. Finally, we use the ship’s actual position data to conduct simulation experiments. The experimental results show the scheme’s feasibility and effectiveness.
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References
Abdullah NA, Rahman MM, Rahman MM et al (2020) A framework for optimal worker selection in spatial crowdsourcing using Bayesian network. IEEE Access 8:120218–120233. https://doi.org/10.1109/ACCESS.2020.3005543
Akyildiz IF, Pompili D, Melodia T (2005) Underwater acoustic sensor networks: research challenges. 3. https://doi.org/10.1016/j.adhoc.2005.01.004
Alt F, Shirazi AS, Schmidt A et al (2010) Location-based crowdsourcing: Extending crowdsourcing to the real world. In: the 6th Nordic Conference. https://doi.org/10.1145/1868914.1868921
Burkard RE, Martello S, Dell’Amico M (2012) Assignment problems
Chen L, Shahabi C (2016) Spatial crowdsourcing: Challenges and opportunities. IEEE Data Eng Bull 39:14–25
Cheng P, Chen L, Lian X et al (2016) Task assignment on multi-skill oriented spatial crowdsourcing. IEEE Trans Knowl Data Eng 28:2201–2215. https://doi.org/10.1109/TKDE.2016.2550041
Gao C, Hu W, Chen K (2022) Research on multi-AUVs data acquisition system of underwater acoustic communication network. Sensors 22:5090. https://doi.org/10.3390/s22145090
Han G, Gong A, Wang H et al (2021) Multi-AUV collaborative data collection algorithm based on Q-learning in underwater acoustic sensor networks. IEEE Trans Veh Technol 70:9294–9305. https://doi.org/10.1109/TVT.2021.3097084
Han S, Zhao S, Lin J et al (2020) Location privacy-preserving distance computation for spatial crowdsourcing. IEEE Internet of Things J 7:7550–7563. https://doi.org/10.1109/JIOT.2020.2985454
Hu S, Liu H, Su L et al (2015) Smartroad: Smartphone-based crowd sensing for traffic regulator detection andidentification. ACM Trans Sensor Netw 11:1–27. https://doi.org/10.1145/2770876
Huo Y, Dong X, Beatty S (2020) Cellular communications in ocean waves for maritime Internet of Things. https://doi.org/10.1109/JIOT.2020.2988634
Jiao Y, Long Y, Lin Z et al (2022) A fine-grain batching-based task allocation algorithm for spatial crowdsourcing. ISPRS Int J Geo-Inf 11:203. https://doi.org/10.3390/ijgi11030203
Josko C, Etchemendy S (1993) Development of underwater acoustic modems and networks. Oceanography 6:112–119
Kazemi L, Shahabi C (2012) Geocrowd: enabling query answering with spatial crowdsourcing. In: Proceedings of the 20th ACM SIGSPATIAL international conference on advances in geographic information systems. https://doi.org/10.1145/2424321.2424346
Khan W, Hua W, Anwar MS et al (2022) An effective data-collection scheme with AUV path planning in underwater wireless sensor networks. Wirel Commun Mob Comput 2022. https://doi.org/10.1155/2022/8154573
Li M, Wang W, Wu J et al (2021) Toward privacy-preserving task assignment for fully distributed spatial crowdsourcing. IEEE Internet Things J 8:13991–14002. https://doi.org/10.1109/JIOT.2021.3069462
Li Y, Yang WY, Jia MD et al (2018) Optimal task assignment algorithm based on tree-decouple in spatial crowdsourcing. J Softw 29:824–838. https://doi.org/10.13328/j.cnki.jos.005453
Liang MZ, Su X, Liu X et al (2020) Intelligent ocean convergence platform based on IoT empowered with edge computing. J Internet Technol 21:235–244. https://doi.org/10.3966/160792642020012101020
Lilhore UK, Khalaf OI, Simaiya S et al (2022) A depth-controlled and energy-efficient routing protocol for underwater wireless sensor networks. Int J Distrib Sensor Netw 18:155013292211171. https://doi.org/10.1177/15501329221117118
Liu A, Liu GF, Li ZX et al (2017) Privacy-preserving task assignment in spatial crowdsourcing. J Comput Sci Technol 32:905–918. https://doi.org/10.1007/s11390-017-1772-5
Ltd XBHITXC (2022) ship data. figshare http://www.ships66.com/
Pankratius V, Coster F, Lind F et al (2014) Mobile crowd sensing in space weather monitoring: the Mahali project. IEEE Commun Mag 52:22–28. https://doi.org/10.1109/MCOM.2014.6871665
Periola AA, Alonge AA, Ogudo KA (2022) Edge computing for big data processing in underwater applications. Wirel Netw 28:813–829. https://doi.org/10.1007/s11276-022-02971-5
Sozer EM, Proakis JG, Stojanovic M (2000) Underwateracoustic networks. IEEE J Ocean Eng 25:72–83. https://doi.org/10.1109/48.820738
Sun L, Guo J, Yu X et al (2021) Deep reinforcement learning for task assignment in spatial crowdsourcing and sensing. IEEE Sensors J 21:25323–25330. https://doi.org/10.1109/JSEN.2021.3057376
Tong Y, Zeng Y, Zhou Z et al (2020) Spatial crowdsourcing: A survey. The VLDB J 29:217–250. https://doi.org/10.1007/s00778-019-00568-7
Wang C, Wright K-L, Liu H et al (2015) A privacy mechanism for mobile-based urban traffic monitoring. Pervasive Mob Comput 20:1–12. https://doi.org/10.1016/j.pmcj.2014.12.007
Zhang X, Liu Y, Yang Z et al (2019) On reliable task assignment for spatial crowdsourcing. IEEE Trans Emerg Top Comput 7:174–186. https://doi.org/10.1109/TETC.2016.2614383
Acknowledgements
This work was supported by Natural Science Foundation of Shandong Province (No. ZR2020MF061).
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This work was supported by Natural Science Foundation of Shandong Province (No. ZR2020MF061).
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All authors contributed to the study conception and design. Material preparation, data collection and analysis were performed by Ying Guo, Fei Li and Keyi Zhang. The first draft of the manuscript was written by Hongtang Cao and Ying Guo. All authors read and approved the final manuscript.
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Cao, H., Guo, Y., Li, F. et al. Distributed spatial crowdsourcing based task allocation in Ocean Internet of Things. Earth Sci Inform 16, 1195–1205 (2023). https://doi.org/10.1007/s12145-023-00942-8
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DOI: https://doi.org/10.1007/s12145-023-00942-8