Fsdss-548 May 2026

| Research Thread | Goal | Potential Impact | |-----------------|------|-------------------| | Adaptive Token Routing | Dynamically select next hop based on information gain or link quality. | Further reduce latency and improve robustness. | | Privacy‑Preserving Fusion | Homomorphic encryption of particle weights. | Enable cooperative surveillance across organizations with data‑sensitivity constraints. | | Cross‑Domain Transfer Learning | Leverage pre‑trained deep models for likelihood estimation, combined with particle‑filter belief. | Boost detection accuracy in novel environments without retraining on‑board. | | Multi‑Token Parallelism | Deploy several tokens simultaneously in disjoint sub‑graphs. | Scale to thousands of agents while preserving near‑optimal fusion. |

---

Given B‑connectivity of the communication graph and a token hop budget ( H \geq N \cdot B ), the token belief ( \beta_H ) converges almost surely to the exact posterior ( p(\mathbfxt \mid Z1:N) ), where ( Z_1:N ) denotes the union of all measurements up to time ( t ). FSDSS-548

Proof Sketch:

| Parameter | Value | |-----------|-------| | Swarm size ( N ) | 12, 24, 48, 96 | | Particle count ( M ) | 500 | | Token hops ( H ) | ( 2N ) | | Communication model | ns‑3 Wi‑Fi (802.11n), 20 Mbps max, packet loss 0‑20 % | | Scenario | Simulated wildfire spread over a 2 km² terrain; sensors: RGB camera, IR, acoustic mic, LiDAR | | Research Thread | Goal | Potential Impact

We compared FSDSS‑548 against three baselines: Given B‑connectivity of the communication graph and a

Metrics: Detection latency (time to achieve > 90 % true‑positive rate), communication overhead (bytes per agent per epoch), robustness (performance under node failure).