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A multi-objective grey wolf optimization algorithm for energy-efficient cluster-based routing in IoT-enabled WSNs
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DOI：10.1038/s41598-025-28950-2.png)
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Abstract
En 中文
Due to the limited resources of Internet of Things (IoT) nodes, extending network lifetime is a critical challenge. Clustering helps manage this data, especially in applications like temperature monitoring and smart farming. Choosing Cluster Heads (CHs) is important in clustering since it strongly affects energy use. Many studies use optimization for CH selection, but poor choices quickly drain node energy. To address this, we propose a Multi-Objective Grey Wolf Optimization (MOGWO) algorithm to improve network life. MOGWO employs a fitness function that uses fuzzy logic to evaluate potential CHs based on distance, number of neighbouring nodes, and residual energy. Simulations are performed in MATLAB 2019a, and the proposed MOGWO algorithm is compared with the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, Improved Fruit Fly Optimization Algorithm (IFFOA), Spotted Hyena Optimisation for Cluster Head (SHO-CH) and the Sea-Horse Optimiser with Opposition based Learning (SHO-OBL). Results show that MOGWO extends network lifetime by 10–20%, reduces communication overhead by 5–10% and increases Packet Delivery Ratio by 2–10% compared to other algorithms.
Keywords:
Internet of things
Optimisation algorithms
Grey wolf optimisation
And whale optimisation
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