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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
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EMO-PEGASIS: A Dual-Phase Machine Learning Protocol for Energy Delay Optimisation in WSNs.

Abdulla Juwaied1

  • 1Institute of Applied Computer Science, Lodz University of Technology, ul. Stefanowskiego 18, 90-537 Lodz, Poland.

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|January 28, 2026
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Summary
This summary is machine-generated.

Enhanced Multi-Objective PEGASIS (EMO-PEGASIS) improves wireless sensor networks by using machine learning for better energy efficiency and reduced delay. This protocol significantly boosts network lifetime and stability.

Keywords:
K-Nearest Neighbours (K-NN)K-meansPEGASISenergy efficiencymachine learningmulti-objective optimisationtransmission delaywireless sensor networks (WSNs)

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) face a critical trade-off between energy conservation and data transmission delay.
  • Existing protocols like PEGASIS offer energy efficiency but suffer from high latency and unbalanced load distribution.
  • Suboptimal cluster formation in traditional WSN protocols limits overall network performance.

Purpose of the Study:

  • To introduce an Enhanced Multi-Objective PEGASIS (EMO-PEGASIS) protocol for WSNs.
  • To address the limitations of existing protocols in managing energy consumption and data transmission delay.
  • To leverage machine learning for optimizing WSN performance in terms of energy, delay, and network lifetime.

Main Methods:

  • A dual-phase machine learning strategy was employed for protocol design and implementation.
  • K-means clustering was utilized for robust spatial partitioning of the network.
  • K-Nearest Neighbours (K-NN) classification was used for adaptive and intelligent routing.

Main Results:

  • EMO-PEGASIS achieved a 45% reduction in average energy consumption compared to PEGASIS.
  • End-to-end delay was decreased by 38%, and network lifetime increased by 67%.
  • The protocol demonstrated enhanced stability, effective load balancing, and a 96.8% packet delivery ratio.

Conclusions:

  • The EMO-PEGASIS protocol effectively addresses the multi-objective optimization problem in WSNs.
  • Integrating machine learning techniques significantly enhances WSN performance.
  • EMO-PEGASIS provides reliable multi-objective optimization for energy- and delay-constrained WSN environments.