A multi-objective grey wolf optimization algorithm for energy-efficient cluster-based routing in IoT-enabled WSNs

Sep 29, 2026

Resource limitations of Internet of Things (IoT) nodes make extending network lifetime a central challenge. Clustering is used to manage energy consumption, where selecting an appropriate Cluster Head (CH) strongly influences overall efficiency. The work introduces a Multi-Objective Grey Wolf Optimization (MOGWO) method using a fuzzy-logic fitness function based on distance, neighboring-node count, and residual energy. MATLAB simulations compare against LEACH and other heuristics, showing improved lifetime, lower communication overhead, and higher packet delivery ratio.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages23

Reading Time58 min

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Multi-Objective Grey Wolf Optimization for CH Selection

Motivation and problem statement

Clustering and energy efficiency in IoT-based WSNs

MOGWO fitness function and fuzzy logic

Simulation setup and comparative results

[www. nature.com/scientificreports](www. nature.com/scientificreports) OPEN A multi-objective grey wolf optimization algorithm for energyefficient cluster-based routing in IoT-enabled WSNs Selvaganapathi Sennan1, Sankar S2, Ramasubbareddy Somula3, Digvijay pandey4 & Yongyun Cho2􀀍 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 The concept of the Internet of Things (IoT) originated with Wireless Sensor Networks (WSNs), and Kevin Ashton coined the term “Internet of Things” in 19991. The IoT is a network of internet-connected devices that can communicate and share data with one another autonomously and without human intervention2–5. The IoT is made up of three parts: sensors, networks, and applications. The network layer is critical for wirelessly transmitting data from one location to another. The sensor devices are extremely resource constrained. Most devices in IoT networks are battery powered. Thus, conserving energy is critical for extending the network’s lifespan. WSNs are a subset ofIoT that are used as part of the network in most IoT applications6,7. WSNs are made up of sensor and sink nodes8. A WSN’s nodes can be arranged in a grid or at random9. It monitors environmental conditions via wired or wireless communication channels10, 11. The sensor node has low processing, memory, and power capacity12. IoT applications require the use of heterogeneous nodes. Nowadays, the rapid adoption of IoT applications generates a great deal of research interest in all areas13, 14. Environmental monitoring, health monitoring, smart farming, smart grid, smart city, and intelligent home automation are all examples ofIoT applications15, 16. Energy efficiency is important in IoT to extend network lifetime17. Researchers are working on new routing methods to achieve this18. Routing moves data packets from source to destination. It generally involves three stages: route discovery and maintenance, network structure, and protocol operation19. Environmental monitoring applications benefit particularly from network structure-based routing. Hierarchical routing is the most common routing mechanism in network structures. It divides the entire network into sections, each of which collects and transmits data to the sink20,21. Clustering is critical for enhancing energy efficiency and scalability in IoT-based wireless sensor networks (WSNs)22–25. By dividing sensor nodes into clusters and assigning a Cluster Head (CH) to each cluster, communication overhead is reduced since only CHs transfer aggregated data to the sink node rather than 1Technical Architect, Hexaware Technologies, Jersey City, USA. 2Department of Information and Communication Engineering, Sunchon National University, Suncheon Si 57922, Republic of Korea. 3Sym

FAQs

Why is Cluster Head (CH) selection important in IoT-based wireless sensor networks?

CH selection directly affects energy consumption because CHs use more energy to collect and transmit data. Poor CH choices can quickly drain node energy, causing imbalance and reducing network lifespan.

How does MOGWO evaluate candidate Cluster Heads?

MOGWO uses a fuzzy-logic fitness function to score potential CHs using multiple criteria: distance, number of neighboring nodes, and residual energy.

What improvements does the proposed method achieve compared with other algorithms?

Simulations show MOGWO extends network lifetime by 10–20%, reduces communication overhead by 5–10%, and increases packet delivery ratio by 2–10% compared with several baseline protocols and optimization approaches.

Sep 29, 2026

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