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An improved energy saving clustering method for IWSN based on Gaussian mutation adaptive artificial fish swarm

Yeshen Lan1, Chuchu Rao2, Qike Cao3

  • 1School of Mechanical and Electrical Engineering, Quzhou College of Technology, 324000, Quzhou, China.

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This study introduces a new clustering model and routing protocol for industrial wireless sensor networks (IWSNs) to enhance energy efficiency. The Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA) significantly improves network lifespan and data transmission reliability.

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Industrial wireless sensor networks (IWSNs) face challenges with energy inefficiency in current cluster routing methods.
  • Efficient cluster-based routing protocols are essential for optimizing IWSN performance and extending network lifespan.

Purpose of the Study:

  • To design a novel clustering model for efficient cluster head (CH) selection and data transmission in IWSNs.
  • To propose a new cluster routing protocol, Gaussian mutation adaptive artificial fish swarm algorithm (GAAFSA), to address energy inefficiency.

Main Methods:

  • Developed a clustering model considering CH energy, base station (BS) distance, packet loss rate, and data delay.
  • Introduced a Gaussian mutation strategy and an adaptive strategy within the artificial fish swarm algorithm (AFSA) to prevent local optima and premature convergence.
  • Experimentally compared the GAAFSA protocol against five existing schemes (CMSTR, D2CRP, EEHCHR, ESCVAD, BAFSA).

Main Results:

  • The GAAFSA protocol demonstrated superior performance in network energy consumption, system lifetime, data transmission reliability, and latency compared to other schemes.
  • Achieved at least a 15.68% improvement in network lifespan.
  • Increased packets received by the base station (BS) by at least 7.46% and reduced packet loss rate by at least 15.28%.

Conclusions:

  • The proposed GAAFSA protocol effectively optimizes IWSN performance and extends network lifespan.
  • Significantly reduces energy loss within the network.
  • Substantially improves the overall network quality of service (QoS).