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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