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

49
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...
49
Data Collection by Observations01:08

Data Collection by Observations

11.7K
Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
11.7K
Cluster Sampling Method01:20

Cluster Sampling Method

11.6K
Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
11.6K
Sampling Methods: Overview01:06

Sampling Methods: Overview

279
A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
279
Heuristics01:21

Heuristics

75
Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
75
Random Sampling Method01:09

Random Sampling Method

11.0K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest. Among the various sampling methods used by...
11.0K

You might also read

Related Articles

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

Sort by
Same author

High-Definition Map-Based Autonomous Vehicle Localization Using LiDAR Point Cloud Similarity Metrics: A Comparative Experimental Study.

Sensors (Basel, Switzerland)·2026
Same author

A Review of Data Engineering in United States Healthcare Infrastructure.

Healthcare (Basel, Switzerland)·2026
Same author

Second thoughts about first principles in biology.

Trends in ecology & evolution·2026
Same author

Diagnostic Accuracy of Examination, Brain-Type Natriuretic Peptide, and Imaging for Volume Overload.

Academic emergency medicine : official journal of the Society for Academic Emergency Medicine·2026
Same author

Using dynamic foodscape models to assess bottom-up constraints on population performance of herbivores.

Ecological applications : a publication of the Ecological Society of America·2025
Same author

Quantifying the ecological role of crocodiles: a 50-year review of metabolic requirements and nutrient contributions in northern Australia.

Proceedings. Biological sciences·2025

Related Experiment Video

Updated: Jun 6, 2025

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

5.9K

Bridging the Gap: An Algorithmic Framework for Vehicular Crowdsensing.

Luis G Jaimes1, Craig White1, Paniz Abedin1

  • 1Department of Computer Science, Florida Polytechnic University, Lakeland, FL 33805, USA.

Sensors (Basel, Switzerland)
|November 27, 2024
PubMed
Summary

Greedy algorithms adapted for vehicular crowdsensing (VCS) can improve urban data collection. This study explores dynamic incentives and mobility patterns to enhance VCS effectiveness, addressing engagement and privacy issues.

Keywords:
greedy algorithmsrecurrent reverse auctionsvehicularcrowdsensing

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

8.7K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.2K

Related Experiment Videos

Last Updated: Jun 6, 2025

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants
06:28

A Networked Desktop Virtual Reality Setup for Decision Science and Navigation Experiments with Multiple Participants

Published on: August 26, 2018

5.9K
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

8.7K
Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles
11:54

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

9.2K

Area of Science:

  • Computer Science
  • Urban Computing
  • Mobile Sensing

Background:

  • Vehicular crowdsensing (VCS) offers potential for urban data collection but faces challenges in user engagement and privacy.
  • Traditional greedy algorithms, successful in pedestrian crowdsensing, may not directly translate to the dynamic nature of VCS.
  • Existing participatory sensing approaches need adaptation for the unique characteristics of vehicular data.

Purpose of the Study:

  • To investigate the effectiveness of greedy algorithms in vehicular crowdsensing (VCS).
  • To develop and evaluate a dynamic incentive mechanism for enhancing VCS user engagement.
  • To adapt and assess participatory sensing strategies for realistic urban vehicular scenarios.

Main Methods:

  • Utilized a recurrent reverse auction model for dynamic incentive allocation.
  • Incorporated vehicular mobility patterns and realistic urban scenarios using SUMO and OpenStreetMap.
  • Selected a representative subset of vehicles within a fixed budget to optimize coverage and reduce redundancy.

Main Results:

  • Demonstrated limitations of applying pedestrian-centric greedy algorithms to VCS.
  • Showcased the potential of a dynamic incentive mechanism to improve VCS coverage and data quality.
  • Highlighted the importance of considering vehicular mobility in crowdsensing algorithm design.

Conclusions:

  • Greedy algorithms require adaptation for the specific dynamics of vehicular crowdsensing.
  • Dynamic incentive mechanisms are crucial for addressing user engagement in VCS.
  • This research provides valuable insights for optimizing vehicular crowdsensing systems.