Resource-Aware Topology Management for ISAC-Enabled TDOA Localization in IoUT Networks
TL;DR
Numerical results show that the proposed ISAC-TM-MAE improves localization accuracy, resource efficiency, scalability, and robustness compared with centralized, distributed, and benchmark TDOA localization schemes.
Abstract
Reliable localization in the Internet of Underwater Things (IoUT) is hindered by limited acoustic bandwidth, long propagation delays, multipath, Doppler shifts, and energy-constrained nodes. This letter proposes an integrated sensing and communication (ISAC)-enabled topology management and multi-stage adaptive estimation (ISAC-TM-MAE) framework for time-difference-of-arrival (TDOA) localization in IoUT networks. In the proposed framework, heterogeneous underwater sensors, seabed anchors, autonomous underwater vehicles (AUVs), and surface buoys exploit acoustic ISAC packets for joint communication and localization. The IoUT network is modeled as a graph, in which informative node pairs are selected subject to acoustic bandwidth, computation, energy, and link-reliability constraints. A D-optimal criterion maximizes the Fisher information matrix (FIM) over the underwater region of interest, while the MAE stage refines the source estimate under acoustic noise and channel uncertainty. Numerical results show that the proposed ISAC-TM-MAE improves localization accuracy, resource efficiency, scalability, and robustness compared with centralized, distributed, and benchmark TDOA localization schemes.