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Obstacle-Resilient Topology Optimization for Industrial IoT via DRL-Driven Potential Field
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DOI：10.1109/tii.2026.3686998.png)
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Abstract
En 中文
In industrial Internet of Things (IIoT), the nonline-of-sightconstraints caused by ubiquitous obstacles pose severe challenges to wireless communication. Existing clustering strategies suffer from problems of relying on idealized geometric prior information, leading to obstacle-ignorant structures, and easily causing topological discontinuities. These problems result in a severe mismatch between network topology and convergent traffic, thereby compromising both network connectivity and energy balance. To address these problems, a topology-adaptive clustering (TAC) framework based on deep reinforcement learning (DRL)-driven potential field is proposed. TAC reformulates the clustering problem as a generalized Voronoi tessellation constrained by physical potential fields. By utilizing a DRL agent to dynamically tune potential parameters within a nonconvex solution space, the framework achieves a posteriori topological awareness and adaptive obstacle avoidance. Furthermore, a generic mechanism is introduced to derive a theoretical closed-form solution for traffic load via graph theory. This graph-theoretic mechanism constructs a deterministic reward function decoupled from specific routing protocols, ensuring precise alignment between the clustering topology and intrinsic traffic demands to guarantee energy balance across various downstream protocols. Simulation results demonstrate that TAC adaptively generates obstacle-resilient topological structures, outperforming existing baseline protocols in terms of network connectivity and lifespan, offering a general, obstacle-resilient, and energy-efficient topology optimization framework for IIoTs.
Keywords:
Clustering strategy
deep reinforcement learning (DRL)
graph theory
Industrial Internet of Things (IIoT)
nonline-of-sight (nLoS)
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