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

Bandpass Sampling01:17

Bandpass Sampling

279
In signal processing, bandpass sampling is an effective technique for sampling signals that have most of their energy concentrated within a narrow frequency band. This type of signal is known as a bandpass signal. The key principle of bandpass sampling involves sampling the signal at a rate that is greater than twice the signal's bandwidth to prevent aliasing.
A bandpass signal has a spectrum with a lower frequency limit, denoted as ω1, and an upper frequency limit, denoted as ω2....
279
Energy Budgets00:51

Energy Budgets

9.8K
Organisms must balance energy intake with the energy required for growth, maintenance and reproduction. These trade-offs result in a variety of survivorship and reproductive strategies, including semelparity and iteroparity. Semelparous species, like annual plants, have only one reproductive episode in their lifetimes and consequently have short lifespans. Iteroparous species, by contrast, have many reproductive events during their lifetimes but have relatively few offspring. These two...
9.8K
Energy and Power Signals01:17

Energy and Power Signals

703
In an electrical system with a resistor, voltage and current signals facilitate the measurement of power and energy across the resistor. For a continuous-time signal, the total energy over a time interval is defined as the integral of the square of the signal's magnitude over that interval. Mathematically, this is expressed as:
703
Upsampling01:22

Upsampling

346
Managing signal sampling rates is essential in digital signal processing to maintain signal integrity. A decimated signal, characterized by a reduced frequency range due to its lower sampling rate, can be upsampled by inserting zeros between each sample. This upsampling process expands the original spectrum and introduces repeated spectral replicas at intervals dictated by the new Nyquist frequency. To refine this zero-inserted sequence, it is passed through a lowpass filter with a cutoff...
346
Sampling Methods: Overview01:06

Sampling Methods: Overview

584
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...
584
Sampling Plans01:23

Sampling Plans

310
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
310

You might also read

Related Articles

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

Sort by
Same author

RustOnt: An Ontology to Explain Weather Favorable Conditions of the Coffee Rust.

Sensors (Basel, Switzerland)·2022
Same author

Algorithm for the Comparison of Human Periodic Movements Using Wearable Devices.

Journal of healthcare engineering·2021
Same author

Tourist Experiences Recommender System Based on Emotion Recognition with Wearable Data.

Sensors (Basel, Switzerland)·2021
Same author

Blockchain-IoT Sensor (BIoTS): A Solution to IoT-Ecosystems Security Issues.

Sensors (Basel, Switzerland)·2021
Same journal

RETRACTED: Zhang et al. A Novel Framework for Reconstruction and Imaging of Target Scattering Centers via Wide-Angle Incidence in Radar Networks. <i>Sensors</i> 2025, <i>25</i>, 6802.

Sensors (Basel, Switzerland)·2026
Same journal

Enhancing Unsupervised Multi-Source Domain Adaptation for Person Re-Identification via Mixture of Experts and Graph-Based Relation.

Sensors (Basel, Switzerland)·2026
Same journal

Development of an Instrumented Glove for Palmar Pressure Assessment in Kayakers.

Sensors (Basel, Switzerland)·2026
Same journal

Development and Experimental Validation of an Autonomous IoT-Based Monitoring System for Real-Time Water Quality Assessment in the Amazon River.

Sensors (Basel, Switzerland)·2026
Same journal

Semi-Supervised Adversarial Learning Framework for Controller Area Network Bus Intrusion Detection.

Sensors (Basel, Switzerland)·2026
Same journal

Smart Optimization Method for Safety Signs in Innovative Manufacturing Environments Integrating Industrial Field IoT Sensors and Knowledge Graphs.

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

Related Experiment Video

Updated: Oct 2, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

676

An Adaptive Sampling Period Approach for Management of IoT Energy Consumption: Case Study Approach.

Carlos Rodriguez-Pabon1, Guillermo Riva2, Carlos Zerbini2

  • 1Departamento de Telemática, Universidad del Cauca, Calle 5, No. 4-70, Popayan 190002, Colombia.

Sensors (Basel, Switzerland)
|February 26, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive sampling method for Internet of Things (IoT) devices in agricultural value chains. The new approach reduces energy consumption by up to 11% while maintaining data accuracy.

Keywords:
Colombian coffeeInternet of Thingsagricultural value chainenergy efficiency

More Related Videos

In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

6.7K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K

Related Experiment Videos

Last Updated: Oct 2, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

676
In Situ Soil Moisture Sensors in Undisturbed Soils
08:20

In Situ Soil Moisture Sensors in Undisturbed Soils

Published on: November 18, 2022

6.7K
Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data
11:21

Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

Published on: July 27, 2018

8.3K

Area of Science:

  • Agricultural Science
  • Computer Science
  • Engineering

Background:

  • The Internet of Things (IoT) offers significant potential for enhancing agricultural value chains (AVC) through monitoring, optimization, and automation.
  • A key challenge hindering widespread IoT adoption in agriculture is the substantial energy consumption of devices.
  • Environmental variables significantly influence the performance and data quality of IoT systems in agricultural settings.

Purpose of the Study:

  • To assess the impact of environmental variables on IoT performance within agricultural value chains.
  • To develop an energy-efficient adaptive sampling method for IoT devices in agriculture.
  • To maintain optimal sensing quality for critical environmental parameters like temperature and humidity.

Main Methods:

  • An adaptive sampling period algorithm was developed, dynamically adjusting based on influential environmental variables.
  • The method focused on optimizing energy usage for IoT devices, particularly for temperature and humidity sensors.
  • Real-world evaluations were conducted in a coffee crop setting to validate the algorithm's effectiveness.

Main Results:

  • The adaptive sampling algorithm demonstrated a reduction in current consumption by up to 11% compared to traditional fixed-rate sampling.
  • The proposed method successfully preserved the accuracy of collected environmental data.
  • Environmental variables, specifically temperature and humidity, were identified as key factors for adaptive sampling.

Conclusions:

  • The developed adaptive sampling method offers a viable solution for reducing IoT energy consumption in agricultural value chains.
  • This approach balances energy efficiency with the need for accurate environmental monitoring.
  • The findings suggest a pathway for more sustainable and cost-effective IoT deployments in agriculture.