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Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
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Distributed Task Offloading in Heterogeneous Vehicular Crowd Sensing.

Yazhi Liu1, Wendong Wang2, Yuekun Ma3

  • 1College of Information Engineering, North China University of Science and Technology, Tangshan 063009, China. liuyazhi@vip.163.com.

Sensors (Basel, Switzerland)
|July 19, 2016
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Summary
This summary is machine-generated.

Vehicular sensing systems face challenges with data collection due to varying vehicle abilities. This study proposes a utility-based algorithm to decompose and offload sensing tasks, improving data collection efficiency and coverage.

Keywords:
mobile crowd sensingtask offloadingvehicular crowd sensing

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Area of Science:

  • Computer Science
  • Intelligent Transportation Systems
  • Wireless Sensor Networks

Background:

  • Vehicular sensing systems face challenges in data collection due to heterogeneous vehicle sensing capabilities and trajectories.
  • Efficiently gathering tempo-spatial sensing data is critical but difficult for vehicles with limited sensing abilities.
  • Existing methods struggle to optimize data collection across diverse vehicular networks.

Purpose of the Study:

  • To propose a novel utility-based algorithm for sensing task decomposition and offloading in vehicular sensing systems.
  • To address the limitations of individual vehicles in collecting comprehensive and uniformly distributed sensing data.
  • To enhance the efficiency and effectiveness of data collection in dynamic vehicular environments.

Main Methods:

  • Developed a utility function considering vehicle mobility, sensing interfaces, data requirements, and coverage needs.
  • Implemented a sensing task decomposition strategy based on calculated vehicle utilities.
  • Offloaded decomposed sensing tasks to neighboring vehicles with optimal utility for the tasks.
  • Utilized real trace-driven simulations to evaluate the proposed algorithm.

Main Results:

  • The proposed utility-based algorithm significantly improves the comprehensiveness of collected sensing data.
  • The algorithm ensures a more uniform distribution of sensing data compared to other approaches.
  • Demonstrated enhanced performance in collecting tempo-spatial sensing data in vehicular networks.
  • Effectively managed sensing tasks for vehicles with varying capabilities.

Conclusions:

  • The proposed utility-based sensing task decomposition and offloading algorithm effectively overcomes data collection challenges in vehicular sensing systems.
  • This approach enhances the quality and distribution of collected sensing data by leveraging the collective capabilities of neighboring vehicles.
  • The algorithm offers a promising solution for improving the performance of intelligent transportation systems and mobile sensing applications.