Journal of Modeling and Simulation in Electrical and Electronics Engineering

Journal of Modeling and Simulation in Electrical and Electronics Engineering

A Centralized Energy-Efficient Clustering and Routing Approach for Wireless Sensor Networks via Graph Neural Networks

Articles in Press

Document Type : Research Article

Authors
Sayyed Majid Mazinani 1
Morteza Saadati 2
Ali Akbar Khazaei 2
1 Department of Electrical and Computer Engineering, Imam Reza International University, Mashhad, Iran.
2 Department of Electrical Engineering, Mashhad Branch, Islamic Azad University, Mashhad, Iran.
10.22075/mseee.2026.40942.1266
Abstract
In Wireless Sensor Networks (WSNs) the energy efficiency is one of the main challenges. Clustering in an efficient method for prolonging lifetime in WSNs. The canonical problem in this context is the choice of Cluster Heads (CHs). CHs consume more energy than member nodes, therefore the rotation of CH role among all nodes is essential to prevent early death of CHs. As this problem is $\mathcal{NP}$-hard, traditional approaches relied on random selection and rotation of CH role, while others considered heuristics such as position, member count and residual energy to select CHs in either centralized or distributed fashion. In this paper we present a CH selection method based a on Graph Neural Network (GNN) to replace ad hoc heuristics with a neural network that effectively considers both state of the nodes and the intricate relationships between them to select CH with the goal of prolonging the lifetime of the network. To this end, we trained a GNN on WSNs with few nodes, in which solving with exact methods is feasible. Thanks the scalability feature of GNNs we can apply this trained GNN to larger networks for CH selection. Also to avoid long-range communications that consume a considerable amount of energy, a multi-hop routing scheme is presented with the goal of balancing energy consumption . Simulation results confirm the effectiveness of our GNN in comparison to state-of-the-art protocols. Furthermore our results highlight the promising usefulness of GNNs in addressing other challenges in WSNs.
Keywords
Energy Efficiency
Clustering
Graph Neural Network
Routing
WSN
Subjects
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Journal of Modeling and Simulation in Electrical and Electronics Engineering

Articles in Press, Accepted Manuscript
Available Online from 19 October 2026

  • Receive Date 25 April 2026
  • Revise Date 21 May 2026
  • Accept Date 23 August 2026