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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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A Cluster-Based Energy Optimization Algorithm in Wireless Sensor Networks with Mobile Sink.

Qian Wei1,2, Ke Bai1,2, Lin Zhou1,2

  • 1School of Artificial Intelligence, Henan University, Kaifeng 475004, China.

Sensors (Basel, Switzerland)
|April 30, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a Cluster-Based Energy Optimization with Mobile Sink (CEOMS) algorithm for wireless sensor networks (WSNs). CEOMS enhances energy efficiency and reduces data delay by optimizing cluster head selection for mobile sinks.

Keywords:
adaptive adjustment functioncluster head selectionenergy optimizationmobile sink

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless sensor networks (WSNs) face challenges with high energy consumption and data delay, particularly when utilizing a mobile sink.
  • Existing protocols like LEACH may not optimally address energy distribution and data latency in dynamic WSN environments.

Purpose of the Study:

  • To propose a novel cluster-based energy optimization algorithm, CEOMS, specifically designed for WSNs with mobile sinks.
  • To enhance network lifetime, reduce data transmission delay, and balance energy load among nodes.

Main Methods:

  • Developed an energy density function for network nodes to prioritize nodes with higher remaining energy for cluster head selection.
  • Constructed a directivity motion performance function for the mobile sink to improve the selection probability of remote nodes as cluster heads.
  • Integrated these functions into the LEACH architecture for adaptive cluster head selection, incorporating an adjustment function based on node death and surviving node density.

Main Results:

  • The CEOMS algorithm demonstrated improved self-adaptability in cluster head selection compared to traditional methods.
  • Significant extension of the overall network lifetime was observed.
  • Noticeable reduction in data delay and a more balanced energy load across the network were achieved.

Conclusions:

  • CEOMS effectively addresses energy consumption and data delay issues in WSNs with mobile sinks.
  • The proposed algorithm offers a more adaptive and efficient approach to cluster head selection, leading to improved network performance and longevity.