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Wireless sensor networks consist of sensor nodes that are physicallydistributed over different locations. Spatial filtering procedures exploit thespatial correlation across these sensor signals to fuse them into a filteredsignal satisfying some optimality condition. However, gathering the raw sensordata in a fusion center to solve the problem in a centralized way would lead tohigh energy and communication costs. The distributed adaptive signal fusion(DASF) framework has been proposed as a generic method to solve these signalfusion problems in a distributed fashion, which reduces the communication andenergy costs in the network. The DASF framework assumes that there is a commongoal across the nodes, i.e., the optimal filter is shared across the network.However, many applications require a node-specific objective, while all thesenode-specific objectives are still related via a common latent data model. Inthis work, we propose the DANSF algorithm which builds upon the DASF framework,and extends it to allow for node-specific spatial filtering problems.

A Distributed Adaptive Algorithm for Node-Specific Signal Fusion Problems in Wireless Sensor Networks