Abstract: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 method that yields a finite-state MDP which can be solved via standard methods such as relative value iteration. We further use a discounted reformulation to derive a theoretical lower bound for the optimal average cost, which is tightened via the incremental pruning algorithm (IPA) and also induces a comparison policy. Numerical results demonstrate that the performance of the proposed policy improves with the truncation depth at the expense of computational effort, and also outperforms low-complexity baselines across a wide range of system parameters. The results also reveal a switching-type structure of the truncation-based policy over the belief simplex and quantify the impact of key system parameters, highlighting the importance of accounting for state-dependent~sensing.
From: Jiapei Tian [view email]
[v1]
Thu, 11 Sep 2025 20:33:36 UTC (2,590 KB)
[v2]
Tue, 10 Mar 2026 15:39:42 UTC (7,954 KB)
[v3]
Tue, 16 Jun 2026 14:04:53 UTC (7,999 KB)
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