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Variable-Length Multiobjective Social Class Optimization for Trust-Aware Data Gathering in Wireless Sensor Networks.

Mohammed Ayad Saad1,2, Rosmina Jaafar1, Kalaivani Chellappan1

  • 1Department of Electrical, Electronics & System Engineering, Faculty of Engineering & Built Environment, Universiti Kebangsaan Malaysia (UKM), Bangi 43600, Selangor, Malaysia.

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This summary is machine-generated.

This study introduces a modified Social Class Multiobjective Particle Swarm Optimization (SC-MOPSO) for efficient and secure data gathering in wireless sensor networks (WSNs). The method enhances trust, energy efficiency, and reduces travel time, outperforming existing algorithms.

Keywords:
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Area of Science:

  • Computer Science
  • Network Engineering
  • Optimization Algorithms

Background:

  • Data gathering in Wireless Sensor Networks (WSNs) is crucial for Internet of Things (IoT) integration.
  • Large-scale WSN deployments face efficiency challenges and security threats impacting data reliability.
  • Trust in data sources and routing nodes is essential for reliable data collection in WSNs.

Purpose of the Study:

  • To develop a multiobjective optimization method for data gathering in WSNs.
  • To jointly optimize energy consumption, travel time, cost, and trust in WSN data collection.
  • To propose a modified Social Class Multiobjective Particle Swarm Optimization (SC-MOPSO) algorithm.

Main Methods:

  • Introduction of a modified SC-MOPSO featuring application-dependent interclass operators.
  • Incorporation of solution generation, rendezvous point management, and class-based movement strategies.
  • Utilization of the Simple Additive Weighting (SAW) method from multicriteria decision-making (MCDM) to select solutions from the Pareto front.

Main Results:

  • SC-MOPSO and SAW demonstrate superior performance in terms of solution domination.
  • SC-MOPSO achieved a set coverage 0.06 dominant over NSGA-II, with NSGA-II showing only 0.04 dominance over SC-MOPSO.
  • The proposed method exhibited competitive performance compared to NSGA-III.

Conclusions:

  • The modified SC-MOPSO effectively addresses the multiobjective nature of WSN data gathering.
  • The integration of trust as an optimization objective enhances data reliability in WSNs.
  • The SC-MOPSO-SAW approach provides a robust solution for optimizing complex WSN data collection scenarios.