Abstract
Crowdsensing offers an effective data collection platform where data requesters can create tasks dynamically and workers are assigned to tasks. Task assignment is a vital part in crowdsensing. Most existing researches consider single capability and basic cost of workers, while ignoring the diverse capabilities and both the basic and additional cost of performing a task. In this paper, we introduce the capability diversity of tasks and workers’ additional cost of workers and formulate the task assignment as a one-to-many matching problem, in which multiple workers with different capabilities are assigned to execute one task, and a task can be successfully completed only if all the required capabilities are fully covered by the capabilities of its assigned workers within its limited budget. Based on relationship between capability and profit, we propose three heuristic algorithms that try to increase the total profits of assigned workers within budget constraint. Through extensive simulations, we show that the proposed algorithms greatly improve the total profits and the coverage ratio of task accomplishment.
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Acknowledgments
This work is partly supported by the National Key R&D Program of China (No. 2017YFB1402102), the Natural Science Basis Research Plan in Shaanxi Province of China (Nos. 2017JM6060, 2017JM6103), and the Fundamental Research Funds for the Central Universities of China (No. GK201801004).
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Li, L., Zhang, L., Wang, X., Yu, S., Wang, A. (2019). An Efficient Task Allocation Scheme with Capability Diversity in Crowdsensing. In: Shen, S., Qian, K., Yu, S., Wang, W. (eds) Wireless Sensor Networks. CWSN 2018. Communications in Computer and Information Science, vol 984. Springer, Singapore. https://doi.org/10.1007/978-981-13-6834-9_2
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DOI: https://doi.org/10.1007/978-981-13-6834-9_2
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