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

TL;DR

This paper introduces the DANSF algorithm, an extension of the DASF framework, enabling distributed adaptive signal fusion tailored to node-specific objectives in wireless sensor networks, reducing communication costs while accommodating diverse local goals.

Contribution

The paper presents a novel distributed adaptive algorithm that extends existing methods to handle node-specific spatial filtering problems in sensor networks.

Findings

Reduces communication and energy costs in sensor networks.

Enables node-specific signal processing objectives.

Maintains optimality within a distributed framework.

Abstract

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