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Short-distance Transport of Resources02:12

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Short-distance transport refers to transport that occurs over a distance of just 2-3 cells, crossing the plasma membrane in the process. Small uncharged molecules, such as oxygen, carbon dioxide, and water, can diffuse across the plasma membrane on their own. In contrast, ions and larger molecules require the assistance of transport proteins due to their charge or size. Transport across membranes also occurs within individual cells, playing a variety of essential roles for the plant as a whole.
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Related Experiment Video

Updated: Jul 12, 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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An Energy-Efficient Routing Protocol with Reinforcement Learning in Software-Defined Wireless Sensor Networks.

Daniel Godfrey1, BeomKyu Suh1, Byung Hyun Lim1

  • 1Department of Computer Science and Engineering, Chungnam National University, Daejeon 34134, Republic of Korea.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces a novel routing protocol for Internet of Things (IoT) networks that optimizes energy efficiency and network adaptability. The Dynamic Objective Selection with Reinforcement Learning (DOS-RL) protocol enhances performance in dynamic wireless environments.

Keywords:
SDWSN-IoTenergy-efficient routingmulti-objective routingreinforcement learning

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Area of Science:

  • Computer Science
  • Networking
  • Wireless Communication

Background:

  • Internet of Things (IoT) systems face challenges with heterogeneous devices, reliability, and scalability.
  • Existing Software-Defined Wireless Sensor Networks (SDWSN) integrated with IoT struggle with device energy limitations, network unpredictability, and Quality of Service (QoS).
  • Ineffective routing protocols lead to network disconnections and poor performance in wireless IoT deployments.

Purpose of the Study:

  • To develop an intelligent, energy-efficient, multi-objective routing protocol for IoT networks.
  • To enhance network adaptability to sudden changes and optimize energy consumption.
  • To improve overall network performance, including packet delivery ratio and reduced latency.

Main Methods:

  • Implementation of a novel routing protocol, Dynamic Objective Selection with Reinforcement Learning (DOS-RL).
  • Utilizing Reinforcement Learning (RL) with dynamic objective selection and informative-shaped rewards.
  • Conducting diverse simulations to evaluate protocol performance against traditional routing methods.

Main Results:

  • Demonstrated significant improvements in energy efficiency for wireless IoT devices.
  • Showcased fast adaptation capabilities to unexpected network changes.
  • Enhanced packet delivery ratio and reduced data delivery latency compared to OSPF and SDN-Q.

Conclusions:

  • The proposed DOS-RL routing scheme effectively addresses energy limitations and network dynamics in IoT.
  • DOS-RL offers a superior solution for optimizing performance in heterogeneous wireless IoT environments.
  • The protocol facilitates seamless adaptation, mitigating disruptions and improving network reliability.