Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Types of Global Positioning System Surveys01:30

Types of Global Positioning System Surveys

GPS surveying methods vary in application, accuracy, and data collection techniques, catering to diverse surveying and mapping needs. Static GPS, kinematic GPS, and real-time kinematic (RTK) surveying are widely used. Each technique offers distinct advantages.Static GPS involves placing one receiver at a known reference point and another at the target point. It collects exact positional data by observing multiple satellite ranges over an extended period, achieving centimeter-level accuracy for...
Bystander Effect02:09

Bystander Effect

The discussion of bullying highlights the problem of witnesses not intervening to help a victim. This is a common occurrence, as the following well-publicized event demonstrates. In 1964, in Queens, New York, a 19-year-old woman named Kitty Genovese was attacked by a person with a knife near the back entrance to her apartment building and again in the hallway inside her apartment building. When the attack occurred, she screamed for help numerous times and eventually died from her stab wounds.

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Experimental demonstration of a dual-dynamic encryption strategy for IM-DD systems with key update and symbol scrambling.

Optics letters·2026
Same author

A Truth-Oriented Trust Evaluation Model of Shared Traffic Messages in the Internet of Vehicles.

Entropy (Basel, Switzerland)·2025
Same author

Joint interference of laser linewidth, chromatic dispersion, and multipath interference mitigation in self-coherent detection.

Optics express·2025
Same author

Real-time fault detection for IIoT facilities using GA-Att-LSTM based on edge-cloud collaboration.

Frontiers in neurorobotics·2024
Same author

Dual-polarization carrier-assisted differential detection with asymmetric twin-SSB modulation and simplified receiver structure.

Optics express·2024
Same author

Robust Optimization Research of Cyber-Physical Power System Considering Wind Power Uncertainty and Coupled Relationship.

Entropy (Basel, Switzerland)·2024
Same journal

Research on a Regional Availability Evaluation Model for Road-Area High-Entropy Energy Based on Synergy Factors.

Entropy (Basel, Switzerland)·2026
Same journal

Atmospheric Turbulence Channel Modeling and Performance Analysis of a CO-ZP-OFDM Coherent Optical Communication System for UAV Air-to-Ground Scenarios.

Entropy (Basel, Switzerland)·2026
Same journal

Information Geometry and Asymptotic Theory for SMML Estimators.

Entropy (Basel, Switzerland)·2026
Same journal

Correlation Entropy and Power-Law Kinetics.

Entropy (Basel, Switzerland)·2026
Same journal

Research on the Contagion of Systemic Financial Risk Under the Impact of Climate Risks-From the Perspective of Complex Networks and Machine Learning.

Entropy (Basel, Switzerland)·2026
Same journal

The Statistical-Mechanical Meaning of the Wave Function of Quantum Mechanics.

Entropy (Basel, Switzerland)·2026
See all related articles

Related Experiment Video

Updated: May 28, 2026

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
09:09

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

BEP-IM: A Vehicular Crowdsensing Incentive Mechanism to Drive Sustained Spatial Coverage and Proactive Sensing

Jiamin Zhang1, Lisha Shuai1, Jiuling Dong1

  • 1School of Computer and Communication Engineering, University of Science and Technology Beijing, Beijing 100083, China.

Entropy (Basel, Switzerland)
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a behavioral economics and psychology incentive mechanism for vehicular crowdsensing, improving data quality and coverage in low-participation areas. The approach addresses skewed vehicle distribution and enhances data reliability for safer autonomous driving.

Keywords:
incentive mechanismloss aversionoperant conditioningreference-dependentvehicular crowdsensing

More Related Videos

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Related Experiment Videos

Last Updated: May 28, 2026

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees
09:09

Radio Frequency Identification and Motion-sensitive Video Efficiently Automate Recording of Unrewarded Choice Behavior by Bumblebees

Published on: November 15, 2014

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior
10:52

Simulation of Human-induced Vibrations Based on the Characterized In-field Pedestrian Behavior

Published on: April 13, 2016

Area of Science:

  • Vehicular crowdsensing
  • Internet of Vehicles
  • Behavioral economics and psychology

Background:

  • Vehicular crowdsensing faces challenges with uneven vehicle distribution, leading to data redundancy in some areas and coverage gaps in others.
  • Current methods struggle with inconsistent data quality, impacting the reliability of information for traffic management and autonomous driving.
  • Participant behavior in crowdsensing is influenced by psychological factors and bounded rationality, which are not fully addressed by existing incentive models.

Purpose of the Study:

  • To propose a novel collaborative incentive mechanism, the Behavioral Economics and Psychology Incentive Mechanism (BEP-IM), for vehicular crowdsensing.
  • To enhance sustained spatial coverage, particularly in low-participation areas (LPAs), and improve data quality.
  • To integrate psychological principles and economic strategies to motivate participants for better sensing outcomes.

Main Methods:

  • A reference-dependent two-sided selection and bidding (RD-TSB) strategy was designed to attract participants to LPAs.
  • A loss-aversion-based sustained incentive strategy (LA-RPI) was introduced to encourage continued participation in LPAs.
  • An operant conditioning-based proactive sensing shaping (OC-SFQ) strategy was developed to improve data quality through reinforcement and feedback loops.

Main Results:

  • The proposed BEP-IM effectively increased participation frequency in low-participation areas.
  • The mechanism demonstrated significant improvements in the overall quality of sensing data collected.
  • Simulation results validated the efficacy of the integrated strategies in addressing coverage deficits and data quality issues.

Conclusions:

  • The BEP-IM offers a robust solution for optimizing vehicular crowdsensing by leveraging behavioral insights.
  • The integration of psychological principles enhances participant engagement and data reliability.
  • This approach contributes to more effective traffic congestion alleviation and safer autonomous driving systems.