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SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based

Ramasubbareddy Somula1, Yongyun Cho1, Bhabendu Kumar Mohanta2

  • 1Department of Information and Communication Engineering, Sunchon National University, Suncheon-si 57922, Republic of Korea.

Sensors (Basel, Switzerland)
|January 23, 2024
PubMed
Summary
This summary is machine-generated.

The SWARAM algorithm enhances Internet of Things (IoT) networks by optimizing cluster head selection in wireless sensor networks, improving energy efficiency and network longevity. This method boosts packet delivery and network lifetime by 10%.

Keywords:
Internet of Thingsclustering protocolenergy conservationosprey optimization algorithmwireless sensor network

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Internet of Things (IoT) networks rely on sensor nodes for data collection in diverse applications like precision agriculture.
  • Limited battery life and energy consumption are critical challenges in IoT, impacting network lifespan and maintenance.
  • Clustering is vital for IoT energy efficiency, but inappropriate Cluster Head (CH) selection causes energy holes and performance degradation.

Purpose of the Study:

  • To address the energy-hole problem in IoT wireless sensor networks by proposing an energy-efficient Cluster Head (CH) selection algorithm.
  • To enhance network longevity and performance through optimized CH selection, reducing redundant data transmission and conserving energy.
  • To introduce the Osprey Optimization Algorithm-based SWARAM (Sensor network With an Osprey-inspired Routing Algorithm for المراقبة) for intelligent CH selection.

Main Methods:

  • The SWARAM approach involves two phases: cluster formation using Euclidean distance and CH selection via the SWARAM technique.
  • The proposed algorithm was simulated and evaluated using MATLAB 2019a.
  • Performance was benchmarked against existing algorithms: EECHS-ARO, HSWO, and EECHIGWO.

Main Results:

  • The SWARAM algorithm demonstrated a 10% improvement in packet delivery ratio compared to existing methods.
  • Network lifetime was extended by 10% through the efficient energy conservation and CH selection strategy.
  • Overall network performance showed significant improvement due to the optimized clustering and energy management.

Conclusions:

  • The SWARAM algorithm effectively solves the energy-hole issue in IoT networks by selecting optimal Cluster Heads.
  • SWARAM offers a promising solution for enhancing energy efficiency, network lifetime, and overall performance in IoT applications.
  • The proposed method provides a substantial advancement in wireless sensor network clustering for IoT environments.