What question did this study set out to answer?

This work aims to develop an energy-efficient framework for clustering and routing in wireless sensor networks (WSNs).

July 6, 2026Open Access

An energy-efficient clustering and routing in WSN using density based adaptive soft clustering with adaptive lotus effect optimization and IGGO

Key Points

This work aims to develop an energy-efficient framework for clustering and routing in wireless sensor networks (WSNs).
Developed a framework combining density-based adaptive soft clustering, adaptive lotus effect optimization, and improved greylag goose optimization.
Proposed clustering organizes sensor nodes into balanced clusters based on density and proximity to reduce communication overhead.
Utilized simulations to evaluate energy consumption, throughput, network lifetime, and delays under dynamic conditions.
Achieved low energy consumption of 10 mJ and high throughput of 0.95 Mbps.
Extended network lifetime to 8000 rounds with an end-to-end delay of only 1.3 ms and packet delivery ratio of 99%.
Outperformed state-of-the-art approaches in energy efficiency, routing stability, and quality-of-service metrics.

Abstract

Wireless Sensor Networks (WSNs) are extensively employed in military surveillance, industrial automation, environmental monitoring, and healthcare applications; however, their performance is severely constrained by limited battery energy, restricted computational capability, communication overhead, and dynamic network topology variations. To address these challenges, this work proposes an energy-efficient clustering and routing framework based on Density-Based Adaptive Soft Clustering (DBASC), Adaptive Lotus Effect Optimization Algorithm (ALEOA), and Improved Greylag Goose Optimization (IGGO). Initially, DBASC organizes sensor nodes into adaptive and balanced clusters by considering node density and spatial proximity, thereby reducing redundant communication and improving cluster stability. Subsequently, ALEOA performs optimal cluster head (CH) selection by exploiting the adaptive and self-organizing characteristics inspired by the lotus effect, enabling efficient energy balancing, adaptive CH rotation, and minimized communication overhead under dynamic WSN conditions. Furthermore, IGGO identifies reliable and energy-aware routing paths by utilizing the cooperative navigation and coordinated movement behavior of greylag geese, which supports stable multi-hop communication, rapid convergence, and efficient route exploration between cluster heads and the base station. The proposed hybrid ALEOA–IGGO framework jointly optimizes clustering and routing operations to minimize energy dissipation, improve packet forwarding reliability, and extend network lifetime. Extensive simulation results demonstrate that the proposed approach achieves low energy consumption of 10 mJ, high throughput of 0.95 Mbps, prolonged network lifetime of 8000 rounds, reduced end-to-end delay of 1.3 ms, and a packet delivery ratio of 99%. Comparative analysis further confirms that the proposed framework outperforms several state-of-the-art approaches, including F-PSO, F-GWO, BOA-ACO, OAFS-IMFO, and QPSOFL, in terms of energy efficiency, routing stability, and quality-of-service performance. The obtained results validate that the proposed ALEOA–IGGO framework effectively addresses the critical challenges of energy-aware clustering and reliable routing in large-scale and dynamic WSN environments.

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