Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using fuzzy with adaptive frilled lizard optimization

Wireless sensor networks (WSNs) have a multitude of applications, particularly in the surveillance and tracking of non-human activities. Sensor nodes (SNs) in WSNs are known for their limited lifespan, primarily due to continuous sensing, which leads to rapid battery depletion. Consequently, energy consumption is a significant research issue in applications facilitated by WSNs. Immediate energy conservation emphasizes accurate categorization and the most efficient path determination from the SN to the receiver. Current clustering and routing methodologies in wireless sensor networks often struggle to equilibrate energy consumption and maintain network performance, leading to suboptimal longevity and efficiency. The work proposes a fuzzy with adaptive Frilled Lizard Optimization (AFLO) for cluster head (CH) selection and a Cross-layer based Improved Dwarf Mongoose Optimization (CL-IDMO) routing algorithm to select the most efficient shortest route for reducing energy consumption in WSN. Fuzzy clustering is a method that categorizes SNs according to their data and energy levels, enabling the creation of clusters that are both adaptable and efficient. In the proposed framework, cluster head selection is performed using key parameters such as residual energy level, node centrality, cluster density, distance to the base station (BS), and cluster head selection probability to ensure balanced energy utilization and effective cluster management. AFLO improves the CH selection process by dynamically adapting to network conditions, maintaining equitable energy consumption allocation, and prolonging network longevity. Furthermore, CL-IDMO improves data routing by incorporating cross-layer parameters, optimizing routing paths to reduce energy consumption and enhance data transmission efficiency. The suggested method outperforms existing clustering and routing methods across multiple measures. The metrics include an 85% Packet Delivery Ratio (PDR), 0.08 Mbps throughput, 22% channel load, 5 seconds end-to-end delay (E2ED), a packet loss rate (PLR) of 5.8%, a communication cost of 0.12%, a buffer occupancy rate of 20.9%, a network life cycle (NLT) of 4000 rounds, and an energy consumption of 40 mJ for 500 nodes.

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Publication Details

Journal
Scientific Reports
Published
2026-10-27
DOI
https://doi.org/10.1038/s41598-026-70201-5
Primary Topic
Energy Efficient Wireless Sensor Networks
Type
article
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article

Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using fuzzy with adaptive frilled lizard optimization

S. Vimalnath, N. Kumareshan, R. Maheswari, N. Prakash
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Scientific Reports
Energy Efficient Wireless Sensor Networks
article

Performance enhancement of efficient clustering and routing protocol for wireless sensor networks using fuzzy with adaptive frilled lizard optimization

S. Vimalnath, N. Kumareshan, R. Maheswari, N. Prakash
article en

Abstract

Wireless sensor networks (WSNs) have a multitude of applications, particularly in the surveillance and tracking of non-human activities. Sensor nodes (SNs) in WSNs are known for their limited lifespan, primarily due to continuous sensing, which leads to rapid battery depletion. Consequently, energy consumption is a significant research issue in applications facilitated by WSNs. Immediate energy conservation emphasizes accurate categorization and the most efficient path determination from the SN to the receiver. Current clustering and routing methodologies in wireless sensor networks often struggle to equilibrate energy consumption and maintain network performance, leading to suboptimal longevity and efficiency. The work proposes a fuzzy with adaptive Frilled Lizard Optimization (AFLO) for cluster head (CH) selection and a Cross-layer based Improved Dwarf Mongoose Optimization (CL-IDMO) routing algorithm to select the most efficient shortest route for reducing energy consumption in WSN. Fuzzy clustering is a method that categorizes SNs according to their data and energy levels, enabling the creation of clusters that are both adaptable and efficient. In the proposed framework, cluster head selection is performed using key parameters such as residual energy level, node centrality, cluster density, distance to the base station (BS), and cluster head selection probability to ensure balanced energy utilization and effective cluster management. AFLO improves the CH selection process by dynamically adapting to network conditions, maintaining equitable energy consumption allocation, and prolonging network longevity. Furthermore, CL-IDMO improves data routing by incorporating cross-layer parameters, optimizing routing paths to reduce energy consumption and enhance data transmission efficiency. The suggested method outperforms existing clustering and routing methods across multiple measures. The metrics include an 85% Packet Delivery Ratio (PDR), 0.08 Mbps throughput, 22% channel load, 5 seconds end-to-end delay (E2ED), a packet loss rate (PLR) of 5.8%, a communication cost of 0.12%, a buffer occupancy rate of 20.9%, a network life cycle (NLT) of 4000 rounds, and an energy consumption of 40 mJ for 500 nodes.

Scientific Reports
M. Kumarasamy College of Engineering (IN), Sri Eshwar College of Engineering (IN), Rajalakshmi Engineering College (IN)
Industry, innovation and infrastructure
Openalex Percentile: Top 12%
Energy Efficient Wireless Sensor Networks
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