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Pairing algorithm for varying data in cluster based heterogeneous wireless sensor networks.

Zahida Shaheen1, Kashif Sattar2, Mukhtar Ahmed3,4

  • 1Pir Mehr Ali Shah Arid Agriculture, University Institute of Information Technology (UIIT), Rawalpindi, Punjab, Pakistan.

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|September 24, 2024
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Summary
This summary is machine-generated.

The new Awake Sleep Heterogeneous Nodes' Pairing (ASHNP) algorithm enhances wireless sensor network (WSN) data transmission efficiency in heterogeneous environments. ASHNP outperforms existing methods like EESAA and ETASA, improving reliability and resource utilization.

Keywords:
ClusteringHeterogeneousPairing algorithmSensor network

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Area of Science:

  • Computer Science
  • Network Engineering

Background:

  • Wireless Sensor Networks (WSNs) utilize clustering and node pairing to prolong network lifespan by conserving energy.
  • Current pairing techniques, while energy-efficient, can cause data loss due to nodes entering sleep mode and communication disruptions with cluster heads.
  • Existing algorithms like EESAA and ETASA face limitations in homogeneous and diverse environments, respectively, particularly in managing data transmission from sleeping nodes and addressing listening issues.

Purpose of the Study:

  • To introduce and evaluate the Awake Sleep Heterogeneous Nodes' Pairing (ASHNP) algorithm for improved data transmission efficiency in WSNs.
  • To address the data loss and communication challenges associated with node pairing in heterogeneous WSN environments.
  • To compare the performance of ASHNP against existing algorithms (EESAA, ETASA) in terms of transmission efficiency, energy consumption, and network reliability.

Main Methods:

  • Development of the Awake Sleep Heterogeneous Nodes' Pairing (ASHNP) algorithm, designed for heterogeneous WSN environments.
  • Comparative analysis of ASHNP against the Energy Efficient Sleep Awake Aware (EESAA) and Energy and Traffic Aware Sleep Awake (ETASA) algorithms.
  • Evaluation of network performance metrics including data transmission efficiency, energy consumption, and the number of dead nodes over multiple rounds.

Main Results:

  • ASHNP demonstrates superior data transmission efficiency compared to EESAA and ETASA, with rates 5.23% and 21.73% higher, respectively.
  • The ASHNP algorithm shows improved energy conservation, maintaining node energy levels 1.5% to 10% higher than EESAA across various network rounds.
  • Comparative analysis confirms ASHNP's effectiveness in enhancing network reliability and resource utilization, overcoming limitations of previous methods.

Conclusions:

  • ASHNP offers a significant advancement in WSN data transmission, particularly for heterogeneous environments.
  • The algorithm effectively mitigates data loss from sleeping nodes and improves overall network communication.
  • ASHNP presents a promising solution for optimizing WSN performance, ensuring network integrity and efficient resource management.