Multi-Task-Oriented Vehicular Crowdsensing: A Deep Learning Approach

Chi Harold Liu, Zipeng Dai, Haoming Yang, Jian Tang

Research output: Chapter in Book/Entry/PoemConference contribution

29 Scopus citations


With the popularity of drones and driverless cars, vehicular crowdsensing (VCS) becomes increasingly widely-used by taking advantage of their high-precision sensors and durability in harsh environments. Since abrupt sensing tasks usually cannot be prepared beforehand, we need a generic control logic fit-for-use all tasks which are similar in nature, but different in their own settings like Point-of-Interest (PoI) distributions. The objectives include to simultaneously maximize the data collection amount, geographic fairness, and minimize the energy consumption of all vehicles for all tasks, which usually cannot be explicitly expressed in a closed-form equation, thus not tractable as an optimization problem. In this paper, we propose a deep reinforcement learning (DRL)-based centralized control, distributed execution framework for multi-task-oriented VCS, called DRL-MTVCS. It includes an asynchronous architecture with spatiotemporal state information modeling, multi-task-oriented value estimates by adaptive normalization, and auxiliary vehicle action exploration by pixel control. We compare with three baselines, and results show that DRL-MTVCS outperforms all others in terms of energy efficiency when varying different numbers of tasks, vehicles, charging stations and sensing range.

Original languageEnglish (US)
Title of host publicationINFOCOM 2020 - IEEE Conference on Computer Communications
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages10
ISBN (Electronic)9781728164120
StatePublished - Jul 2020
Event38th IEEE Conference on Computer Communications, INFOCOM 2020 - Toronto, Canada
Duration: Jul 6 2020Jul 9 2020

Publication series

NameProceedings - IEEE INFOCOM
ISSN (Print)0743-166X


Conference38th IEEE Conference on Computer Communications, INFOCOM 2020


  • Vehicular crowdsensing
  • deep reinforcement learning
  • energy efficiency
  • multiple tasks

ASJC Scopus subject areas

  • General Computer Science
  • Electrical and Electronic Engineering


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