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Related Concept Videos

Frequency-dependent Selection01:21

Frequency-dependent Selection

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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
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Published on: September 8, 2023

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Enhanced Dual-Selection Krill Herd Strategy for Optimizing Network Lifetime and Stability in Wireless Sensor

Allam Balaram1, Rajendiran Babu2, Miroslav Mahdal3

  • 1Department of Computer Science and Engineering, MLR Institute of Technology, Hyderabad 500043, India.

Sensors (Basel, Switzerland)
|September 9, 2023
PubMed
Summary
This summary is machine-generated.

This study introduces an enhanced dual-selection krill herd optimization for wireless sensor networks (WSNs). The new method significantly boosts network lifetime and stable energy while reducing latency for efficient WSN operation.

Keywords:
dual mechanismexploitationexplorationkrill herdlatencystability

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Area of Science:

  • Computer Science
  • Network Engineering
  • Optimization Algorithms

Background:

  • Wireless sensor networks (WSNs) face critical energy management challenges.
  • Maximizing network lifetime, coverage, and data aggregation are key operational goals.
  • Efficient energy conservation is vital for sensor node deployment and scalability.

Purpose of the Study:

  • To introduce an enhanced dual-selection krill herd (KH) optimization clustering scheme for resource-efficient WSNs.
  • To address energy conservation challenges in WSNs through optimized node deployment and clustering.
  • To improve overall energy utilization and reduce inter-node communication.

Main Methods:

  • Developed an enhanced dual-selection krill herd (KH) optimization clustering scheme.
  • Implemented a dynamic layering mechanism to prevent repetitive cluster head selection.
  • Utilized a modified krill-based clustering method for enhanced exploitation and exploration.

Main Results:

  • Achieved a 23.21% enhancement in network lifetime.
  • Increased stable energy by 19.84%.
  • Reduced network latency by 22.88% compared to benchmark approaches.

Conclusions:

  • The proposed KH optimization clustering scheme offers a more efficient and reliable solution for WSN energy management.
  • The dynamic layering and dual-selection mechanisms contribute to improved network performance.
  • This approach effectively addresses critical challenges in WSN operational efficiency and longevity.