A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility
| العنوان: | A Deep Learning-Based Mobile Crowdsensing Scheme by Predicting Vehicle Mobility |
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| المؤلفون: | Md. Zakirul Alam Bhuiyan, Wenjuan Tang, Anfeng Liu, Xiaoyu Zhu, Yueyi Luo |
| المصدر: | IEEE Transactions on Intelligent Transportation Systems. 22:4648-4659 |
| بيانات النشر: | Institute of Electrical and Electronics Engineers (IEEE), 2021. |
| سنة النشر: | 2021 |
| مصطلحات موضوعية: | Scheme (programming language), 050210 logistics & transportation, Computer science, business.industry, Mechanical Engineering, Deep learning, 05 social sciences, Real-time computing, Computer Science Applications, Set (abstract data type), Crowdsensing, 0502 economics and business, Automotive Engineering, Trajectory, Task analysis, Data center, Artificial intelligence, Online algorithm, business, computer, computer.programming_language |
| الوصف: | Mobile crowdsensing is an emerging paradigm that selects users to complete sensing tasks. Recently, mobile vehicles are adopted to perform sensing data collection tasks in the urban city due to their ubiquity and mobility. In this article, we study how mobile vehicles can be optimally selected in order to collect maximum data from the urban environment in a future period of tens of minutes. We formulate the recruitment of vehicles as a maximum data limited budget problem. The application scenario is generalized to a realistic online setting where vehicles are continuously moving in real-time and the data center decides to recruit a set of vehicles immediately. A deep learning-based scheme through mobile vehicles (DLMV) is proposed to collect sensing data in the urban environment. We first propose a deep learning-based offline algorithm to predict vehicle mobility in a future time period. Furthermore, we propose a greedy online algorithm to recruit a subset of vehicles with a limited budget for the NP-Complete problem. Extensive experimental evaluations are conducted on the real mobility dataset in Rome. The results have not only verified the efficiency of our proposed solution but also validated that DLMV can improve the quantity of collected sensing data compared with other algorithms. |
| تدمد: | 1558-0016 1524-9050 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_________::43a292eefbe35642bb81d2d569e6ffa7 https://doi.org/10.1109/tits.2020.3023446 |
| حقوق: | CLOSED |
| رقم الأكسشن: | edsair.doi...........43a292eefbe35642bb81d2d569e6ffa7 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 15580016 15249050 |
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