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Related Experiment Video

Updated: Nov 3, 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

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Energy Conservation for Internet of Things Tracking Applications Using Deep Reinforcement Learning.

Salman Md Sultan1, Muhammad Waleed1, Jae-Young Pyun1

  • 1Department of Information and Communication Engineering, Chosun University, Gwangju 61452, Korea.

Sensors (Basel, Switzerland)
|June 2, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel deep reinforcement learning model using long short-term memory deep Q-network for energy-efficient sensor selection in Internet of Things (IoT) tracking systems. The method optimizes sensor choice to reduce battery consumption in smart applications.

Keywords:
best sensor selectiondeep reinforcement learningenergy consumptioninternet of thingstarget tracking

Related Experiment Videos

Last Updated: Nov 3, 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

884

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Internet of Things

Background:

  • Internet of Things (IoT) systems require efficient sensor networks for applications like smart farms, factories, and cities.
  • Battery-powered sensor devices in IoT tracking systems face significant energy consumption challenges.
  • Traditional sensor selection methods struggle with the dynamic demands and limited battery life of IoT applications.

Purpose of the Study:

  • To develop an energy-efficient sensor selection model for IoT-based target tracking applications.
  • To address the limitations of existing methods in managing sensor node battery lifetime.
  • To leverage deep reinforcement learning for optimizing sensor selection in real-time tracking.

Main Methods:

  • Proposed a novel model combining Long Short-Term Memory (LSTM) with Deep Q-Network (DQN), a type of deep reinforcement learning (Deep RL).
  • Implemented a sensor selection strategy based on minimizing a distance function to identify the most energy-efficient sensor.
  • Utilized simulation to evaluate the performance of the proposed Deep RL target tracking model.

Main Results:

  • The proposed LSTM-DQN model effectively selects the best sensor for target tracking, prioritizing energy efficiency.
  • Demonstrated significant improvements in reducing sensor device energy consumption compared to traditional approaches.
  • Simulation results indicate favorable outcomes in both sensor selection accuracy and overall energy savings.

Conclusions:

  • The LSTM-DQN based Deep RL model offers a promising solution for energy-efficient sensor selection in IoT target tracking.
  • This approach effectively mitigates the battery lifetime constraints of sensor nodes in real-time IoT applications.
  • The method contributes to the development of more sustainable and long-lasting smart IoT systems.