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

Cluster Sampling Method01:20

Cluster Sampling Method

12.7K
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...
12.7K
Classification of Systems-I01:26

Classification of Systems-I

296
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
296
Randomized Experiments01:13

Randomized Experiments

7.2K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
7.2K
Random Sampling Method01:09

Random Sampling Method

12.3K
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...
12.3K
Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

5.8K
The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
Payment for healthcare services has historically promoted adoption of costly and often unnecessary or inefficient...
5.8K
Classification of Systems-II01:31

Classification of Systems-II

240
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
240

You might also read

Related Articles

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

Sort by
Same author

An Intelligence-Based Hybrid CNN-GAT Framework Optimized by the Whale Optimization Algorithm for Clinical Lung Cancer Classification from Chest CT Images.

Journal of imaging informatics in medicine·2026
Same author

Efficient abnormal behavior detection in information-centric internet of things using SVM.

Scientific reports·2026
Same author

A reinforcement learning-enhanced discrete zebra optimization algorithm for solving the traveling salesman problem.

Scientific reports·2026
Same author

Energy-aware priority-based task scheduling in cloud data centers using bacterial foraging optimization.

Scientific reports·2026
Same author

Machine learning approaches for resource management and forecasting in energy consumption systems.

Scientific reports·2026
Same author

LDSC: enhancing lung disease diagnosis using a simple 1D-CNN.

Scientific reports·2026

Related Experiment Video

Updated: Sep 10, 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

658

Intelligent resource allocation in internet of things using random forest and clustering techniques.

Nahideh Derakhshanfard1, Lida Hosseinzadeh2, Fahimeh Rashid Jafari2

  • 1Department of Computer Engineering, Ta.C, Islamic Azad University, Tabriz, Iran. n.derakhshan@iaut.ac.ir.

Scientific Reports
|August 20, 2025
PubMed
Summary

This study introduces an intelligent resource allocation method for the Internet of Things (IoT) using clustering and machine learning. The approach enhances efficiency, reduces energy use, and improves response times in dynamic IoT networks.

Keywords:
Dynamic resource schedulingInternet of thingsK-means clusteringRandom forestResource allocationResource management

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

405
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Related Experiment Videos

Last Updated: Sep 10, 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

658
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

405
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

7.6K

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • The proliferation of Internet of Things (IoT) devices presents significant resource management challenges.
  • Constraints like limited energy, bandwidth, and computation power necessitate efficient resource allocation strategies.
  • Existing methods (e.g., evolutionary algorithms, multi-agent reinforcement learning) struggle with the dynamic nature of IoT networks due to complexity and cost.

Purpose of the Study:

  • To propose an intelligent resource allocation approach for Internet of Things (IoT) networks.
  • To address the inefficiencies of current methods in dynamic and scalable IoT environments.
  • To improve prediction accuracy, reduce energy consumption, and decrease response times.

Main Methods:

  • Integration of clustering (K-Means) and machine learning (Random Forest) techniques.
  • Clustering IoT devices based on energy consumption and bandwidth requirements.
  • Training a Random Forest model to predict resource needs for optimal allocation.

Main Results:

  • Achieved a prediction accuracy of 94% for resource needs.
  • Reduced energy consumption by 20% compared to existing methods.
  • Decreased response time by 10% in dynamic IoT environments.

Conclusions:

  • The proposed approach effectively manages resources in dynamic and scalable IoT networks.
  • Combining K-Means clustering and Random Forest machine learning offers a superior solution for IoT resource allocation.
  • Demonstrated significant improvements in accuracy, energy efficiency, and response time.