A novel spatio-topological embeddings for efficient & model-free redundant node placement in 6G IoT networks

Abhishek (), Somesh Kumar () and Manisha Pattanaik ()
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Abhishek: ABV-Indian Institute of Information Technology and Management
Somesh Kumar: ABV-Indian Institute of Information Technology and Management
Manisha Pattanaik: ABV-Indian Institute of Information Technology and Management

Telecommunication Systems: Modelling, Analysis, Design and Management, 2025, vol. 88, issue 4, No 13, 15 pages

Abstract: Abstract The development of 6G has accelerated the usage of IoT for data collection. With continuous usage and ubiquitous connectivity, the batteries of the nodes soon deplete and create a coverage hole in the network, imposing connectivity challenges in the 6G IoT. The problem has been dealt with a novel set of spatial features with topological embeddings extraction using the graphical convolutional network (GCN). Further, the deep deterministic policy gradient in the continuous action space trains the agent for the optimal placement of redundant nodes. The complete methodology with spatio-topological features has seen an improvement of up to 13.1% in energy residual and 19.4% in the uniform load distribution compared to the state-of-the-art methods with stable network connectivity. In addition, the analysis was performed under various environmental conditions with varying hole density and sensor density. The proposed scheme has also shown improvement under adverse coverage conditions.

Keywords: 6G IoT; Topological features; GCN; DDPG; Reinforcement learning (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11235-025-01366-z

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