In heterogeneous sensing networks, agents previously could infer only their own state or rely on a central processor to aggregate measurements. A decentralized method called cluster-aware social learning now lets each agent use local observations and neighbor exchanges to identify connected groups sharing the same hidden state, maintain one belief per group, and map those beliefs back to individual agents, recovering the network-wide state configuration while exchanging only cluster-level information. Synthetic Gaussian experiments and real gas-sensor data both show beliefs concentrating on each cluster's true state once the cluster structure is frozen.
In heterogeneous sensing networks, agents previously could infer only their own state or rely on a central processor to aggregate measurements. A decentralized method called cluster-aware social learning now lets each agent use local observations and neighbor exchanges to identify connected groups sharing the same hidden state, maintain one belief per group, and map those beliefs back to individual agents, recovering the network-wide state configuration while exchanging only cluster-level information. Synthetic Gaussian experiments and real gas-sensor data both show beliefs concentrating on each cluster's true state once the cluster structure is frozen.