A Distributed Multi-Hop Intra-Clustering Approach Based on Neighbors Two-Hop Connectivity for IoT Networks

Area of Science:

• Computer Science
• Network Engineering
• Internet of Things (IoT)

Background:

• Star topology in large IoT networks leads to energy waste and congestion.
• Existing clustering methods often use single-hop models, which do not significantly reduce energy consumption in large networks.
• Centralized k-hop clustering approaches lack robustness due to reliance on limited neighbor information.

Purpose of the Study:

• To propose a distributed approach for k-hop intra-clustering in IoT networks.
• To enhance network lifetime and reduce energy consumption through optimized cluster head selection.
• To improve the robustness of clustering algorithms by utilizing two-hop connectivity.

Main Methods:

• Developed Distributed Clustering based 2-Hop Connectivity (DC2HC), a distributed algorithm for k-hop intra-clustering.
• Utilized two-hop neighbors connectivity for electing cluster heads.
• Provided a convergence proof for the proposed distributed clustering algorithm.

Main Results:

• DC2HC reduces the number of generated cluster heads compared to existing approaches.
• The proposed protocol achieves a longer network lifetime.
• Simulation results demonstrate the effectiveness of DC2HC in optimizing cluster head selection and network performance.

Conclusions:

• Distributed k-hop clustering using two-hop connectivity is an effective strategy for large IoT networks.
• DC2HC offers a robust and energy-efficient solution for IoT network management.
• The proposed algorithm significantly improves upon existing clustering methods in terms of network lifetime and efficiency.