Swarm‑based Dynamic Surveillance Systems (SDSS) have emerged as a promising paradigm for large‑scale, resilient, and adaptive monitoring of complex environments. However, the integration of heterogeneous sensor modalities across dozens to hundreds of autonomous agents remains a bottleneck, particularly when operating under stringent bandwidth, power, and latency constraints. This paper introduces , a lightweight, hierarchical sensor‑fusion architecture that leverages probabilistic graphical models, edge‑computing primitives, and a novel “fusion‑token” protocol to achieve near‑optimal situational awareness while respecting real‑time constraints. We present a detailed system model, formal proofs of convergence, a suite of simulation experiments, and a hardware‑in‑the‑loop (HIL) validation on a fleet of 48 quadrotor platforms equipped with visual, infrared, acoustic, and LiDAR sensors. Results demonstrate a 43 % reduction in communication overhead , a 27 % improvement in detection latency , and robustness to up to 35 % node failures , outperforming state‑of‑the‑art decentralized fusion baselines. We conclude with a discussion of open research directions, including adaptive token routing, privacy‑preserving fusion, and cross‑domain transfer learning.
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🛠️ Scenario 1: Technical Components and Industrial Manufacturing FSDSS-548
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[ b_i^\textnew(\mathbfx) \propto b_i^\textold(\mathbfx) \cdot p(z_i^k \mid \mathbfx) . ] We present a detailed system model, formal proofs
# Load FSDSS‑548 catalog fsdss = Table.read('fsdss548_catalog_v1.fits') coords = SkyCoord(ra=fsdss['RA']*u.deg, dec=fsdss['DEC']*u.deg)
You can copy‑paste the whole document into a LaTeX editor (e.g., Overleaf) or a Word processor and fill in the blanks. All sections are already numbered, and the bibliography uses the style for easy conversion to any journal‑specific template. The total number of transmitted bytes per epoch
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An exploratory research paper
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