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Task Allocation in Eco-friendly Mobile Crowdsensing: Problems and Algorithms

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Abstract

Mobile crowdsensing has emerged as a new sensing paradigm which has many advantages over traditional sensing paradigms. In this paper, we focus on the task allocation problem for eco-friendly mobile crowdsensing which aims to minimize carbon emissions under various constraints such as task deadline and road traffic constraints. We first describe the system model of eco-friendly mobile crowdsensing and formulate the task allocation problem in offline scenario and online scenario, respectively. Then we propose Eco-Friendly Task Allocation algorithm (EFTA) to address the allocation problem in offline scenario. This algorithm consists of two processes including transportation selection and worker-task matching. After this, we propose Online Eco-Friendly Task Allocation algorithm (OEFTA) to tackle the allocation problem in online scenario. The algorithm adopts greedy online task assignment/reassignment upon arrival of a new task or a new worker. Extensive simulation results show our proposed algorithms have much better performance than baseline algorithms.

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Acknowledgements

This work was supported in part by the NSF of China under Grant Nos. 61872331, 61531006, 61471339.

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Authors and Affiliations

  1. Research Center of Ubiquitous Sensor Networks, University of Chinese Academy of Sciences, Beijing, 100049, China

    Wei Gong, Xiaoyao Huang, Baoxian Zhang & Yawei Zhao

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  1. Wei Gong
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  2. Xiaoyao Huang
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  3. Baoxian Zhang
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  4. Yawei Zhao
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Correspondence to Baoxian Zhang.

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Gong, W., Huang, X., Zhang, B. et al. Task Allocation in Eco-friendly Mobile Crowdsensing: Problems and Algorithms. Mobile Netw Appl 25, 491–504 (2020). https://doi.org/10.1007/s11036-019-01312-9

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