{"ID":2872354,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.09837","arxiv_id":"2509.09837","title":"Remote Tracking with State-Dependent Sensing in Pull-Based Systems: A POMDP Framework","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 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.","short_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 ch...","url_abs":"https://arxiv.org/abs/2509.09837","url_pdf":"https://arxiv.org/pdf/2509.09837v2","authors":"[\"Jiapei Tian\",\"Abolfazl Zakeri\",\"Marian Codreanu\",\"David Gundlegård\"]","published":"2025-09-11T20:33:36Z","proceeding":"eess.SP","tasks":"[\"eess.SP\"]","methods":"[]","has_code":false}