Abstract
Mobile Crowdsensing (MCS) is often accompanied by various adverse objectives when performing data collection, which makes it difficult to collect accurate data of the entire target area with low cost. Therefore, how to collect a small part of the data to accurately infer the other data of the entire target area is a crucial issue. People’s daily trajectories usually follow a certain pattern, for example, students go to school, company employees go to work. This pattern allows us to find participants who often pass through fixed areas within a certain time span, leading their collected partial data are near optimal for the entire data collection task. This paper models the problem and develops multiple methods to improve the participant selection and the data recovery. Particularly, we use a random method, reinforcement learning, and greedy algorithm to handle this problem and compare the differences among these methods by experiments.
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This work is supported by the National Natural Science Foundation of China under grant No. 61772136.
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Tu, C., Han, L., Wang, L., Yu, Z. (2020). Mobile Crowd-Sensing System Based on Participant Selection. In: Yu, Z., Becker, C., Xing, G. (eds) Green, Pervasive, and Cloud Computing. GPC 2020. Lecture Notes in Computer Science(), vol 12398. Springer, Cham. https://doi.org/10.1007/978-3-030-64243-3_33
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DOI: https://doi.org/10.1007/978-3-030-64243-3_33
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