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

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

5.6K
In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
A small car of mass 1,200 kg traveling east at 60 km/h collides at an intersection with a truck of mass 3,000 kg traveling due north at 40 km/h. The two vehicles are locked together. What is the...
5.6K
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

7.3K
It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a...
7.3K

You might also read

Related Articles

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

Sort by
Same author

Quantum-enhanced spiking intelligence framework for real-time anomaly detection in industrial internet of things.

Scientific reports·2026
Same author

BrainAuth: A Neuro-Biometric Approach for Personal Authentication.

IEEE journal of biomedical and health informatics·2025
Same author

Context-Driven Active Contour (CDAC): A Novel Medical Image Segmentation Method Based on Active Contour and Contextual Understanding.

Sensors (Basel, Switzerland)·2025
Same author

Machine learning assisted nanozyme sensor array for accurate identification and discrimination of flavonoids in healthy tea.

Food chemistry·2025
Same author

A Non-Invasive Blood Glucose Detection System Based on Photoplethysmogram With Multiple Near-Infrared Sensors.

IEEE journal of biomedical and health informatics·2024
Same author

Alzheimer's disease diagnosis in the metaverse.

Computer methods and programs in biomedicine·2024

Related Experiment Video

Updated: Mar 27, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.9K

An Energy-Efficient Skyline Query for Massively Multidimensional Sensing Data.

Yan Wang1,2, Wei Wei3, Qingxu Deng4

  • 1School of Information Science and Engineering, Northeastern University, Shenyang 110819, China. wang_yan@lnu.edu.cn.

Sensors (Basel, Switzerland)
|January 14, 2016
PubMed
Summary
This summary is machine-generated.

This study introduces an energy-efficient skyline query method for cyber-physical systems (CPS) with massive, multi-dimensional sensing data. The approach reduces data transmission and improves query efficiency, crucial for applications like disaster early warning.

Keywords:
CPSWSNenergy-efficientnode cutskyline query

More Related Videos

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

285
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

704

Related Experiment Videos

Last Updated: Mar 27, 2026

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering
09:43

Databases to Efficiently Manage Medium Sized, Low Velocity, Multidimensional Data in Tissue Engineering

Published on: November 22, 2019

6.9K
Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers
07:12

Façade-Level Monitoring of CO2 Variability under Urban Heat Island Conditions using Low-Cost Sensor Data Loggers

Published on: December 12, 2025

285
Analysis of Multidimensional Microscopy Data Using Cell-ACDC
06:17

Analysis of Multidimensional Microscopy Data Using Cell-ACDC

Published on: November 7, 2025

704

Area of Science:

  • Computer Science
  • Data Science
  • Network Engineering

Background:

  • Cyber-physical systems (CPS) generate massive, multi-dimensional sensing data via wireless sensor networks.
  • Skyline query algorithms are vital for analyzing this data in applications like safe production monitoring and disaster early warning.
  • Increasing network sizes exacerbate challenges in query efficiency and energy consumption for skyline queries.

Purpose of the Study:

  • To propose a novel, energy-efficient skyline query method for handling massive, multi-dimensional sensing data in CPS.
  • To address the pressing issues of improving query efficiency and reducing transmission energy consumption.
  • To enhance the performance of skyline query algorithms in large-scale sensor networks.

Main Methods:

  • A node cut strategy dynamically generates filtering tuples to identify and discard irrelevant data from dominated nodes.
  • This strategy modifies query paths dynamically, reducing data comparison and computational overhead.
  • An intra-node tuple-cutting strategy further prunes non-skyline data within sub-trees, minimizing uploads.

Main Results:

  • The proposed method significantly reduces communication overhead by minimizing non-skyline data transmission.
  • Experimental results demonstrate a substantial improvement in query response time and overall efficiency.
  • The method effectively provides a quick overview of monitored areas with reduced energy expenditure.

Conclusions:

  • The developed energy-efficient skyline query method effectively addresses the challenges of massive, multi-dimensional data in CPS.
  • It offers a practical solution for optimizing data analysis in sensor networks, enhancing monitoring and early-warning systems.
  • The approach leads to faster, more efficient, and energy-saving data querying in critical applications.