Volume 17, Number 6

Multimodal QOS Aware Load Balanced Clustering in 5G-Enabled IOT Sensor Network

  Authors

Biswanath Dey ¹, Sukumar Nandi ², Sivaji Bandyopadhyay ³ and Samir Borgohain ¹, ¹ National Institute of Technology, India
² Indian Institute of Technology, India, ³ Jadavpur University, India

  Abstract

Formation of balanced clusters in IoT-based wireless sensor networks constitutes an NP-hard problem for which no straightforward solution exists. Existing approaches often employ centralized schemes with significant communication overhead or rely on computationally intensive evolutionary algorithms, which limit their practical applicability. To address this challenge, we introduce two simple yet efficient distributed methods: the Load Balanced Greedy Cluster Assignment (LBGCA) for large-scale IoT networks, and its extension, the Multi-modal Load Balanced Greedy Cluster Assignment (MLBGCA) for QoS–aware applications. Both algorithms adopt a localized greedy strategy that minimizes communication overhead by requiring only neighbourhood-level information. LBGCA enables nodes to self-organize into balanced clusters, whereas MLBGCA integrates application-specific QoS constraints by dynamically adjusting cluster head load profiles. Simulation results across grid, Gaussian, and random deployments demonstrate that LBGCA reduces load imbalance by 20–40% and energy consumption by 70–90%, while MLBGCA further improves load imbalance by 30–50% and decreases energy use by 60–90% relative to existing algorithms.

  Keywords

IoT sensor network, 5G, Energy efficiency, Distributed algorithm, Localized greedy load balancing, QoS aware load balancing, Multi modal load balancing.