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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.09837 |
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Table of Contents:
- We consider real-time remote tracking of a Markov source observed by multiple heterogeneous sensors with state-dependent sensing accuracy, motivated by distributed camera networks with overlapping coverage and spatial blind spots. Upon commands from a remote sink, sensors transmit their observations over error-prone channels. We aim to minimize the long-term average of a weighted sum of goal-aware distortion and transmission costs. The problem is formulated as a partially observable Markov decision process (POMDP) and cast into an equivalent belief-MDP. To address the intractability of the infinite and continuous belief space, we develop a truncation-based approximation that yields a finite-state MDP solved via the relative value iteration algorithm (RVIA). We further reformulate the original belief-MDP into a discounted version and solve it using incremental pruning algorithm (IPA). Numerical results demonstrate that the performance of the RVIA-based policy improves with the truncation depth at the expense of computational effort, and both proposed methods outperform low-complexity baselines across a wide range of system parameters. The results also reveal a switching-type structure of the RVIA-based policy over the belief simplex and quantify the impact of key system parameters, highlighting the importance of accounting for state-dependent sensing.