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Information And Computing Sciences
Distributed Computing And Systems Software
Cyberphysical Systems And Internet Of Things
Software Defined Wireless Sensor Load Balancing Routing For Internet Of Things Applications: Review Of Approaches.

Home
Research Domains
Information And Computing Sciences
Distributed Computing And Systems Software
Cyberphysical Systems And Internet Of Things
Software Defined Wireless Sensor Load Balancing Routing For Internet Of Things Applications: Review Of Approaches.

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

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Software defined wireless sensor load balancing routing for internet of things applications: Review of approaches.

Babangida Isyaku^1,2, Kamalrulnizam Bin Abu Bakar¹, Nura Muhammed Yusuf³

¹Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Johor, Johor Bahru, Malaysia.

|May 3, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

This paper analyzes load-balancing routing solutions for Software-Defined Internet of Things (SDIoT) networks. It classifies existing methods, highlighting their weaknesses and future research needs for effective network management.

Keywords:

IoT application Load balancing Routing SDIoT

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Area of Science:

Computer Science
Network Engineering

Background:

The Internet of Things (IoT) generates diverse traffic with varying Quality of Service (QoS) demands.
Traditional IoT networks struggle with centralized management, hindering effective traffic handling.
Software-Defined Networking (SDN) offers centralized control, enabling Software-Defined IoT (SDIoT) and Software-Defined Wireless Sensor Networks (SDWSN).

Purpose of the Study:

To address load-imbalance routing issues in SDIoT networks.
To provide a comprehensive classification and analysis of existing load-balancing routing solutions.
To identify shortcomings and postmortem challenges in current SDIoT integration studies.

Main Methods:

In-depth classification of existing load-balancing routing solutions for SDIoT.

Categorization of problems addressed by different load-balancing schemes.

Analysis of findings, weaknesses, and postmortem challenges of current studies.

Main Results:

Existing load-balancing solutions for SDIoT networks are categorized based on their objectives and effectiveness.
Shortcomings in current load-balancing strategies and integration approaches are identified.
Key challenges and limitations in existing research are summarized.

Conclusions:

A critical gap exists in the comprehensive analysis of load-balancing solutions for SDIoT.
Understanding the weaknesses of current approaches is crucial for future development.
Further research is needed to overcome integration challenges and improve network performance.