Dynamic Bayesian diffusion estimation

TL;DR

This paper introduces a scalable, decentralized Bayesian diffusion estimation method for wireless ad-hoc networks, reducing communication overheads and avoiding single points of failure by enabling local node communication.

Contribution

It proposes a universal Bayesian diffusion estimation framework applicable to various models, including a detailed Gaussian regression case, enhancing distributed estimation reliability.

Findings

Decentralized Bayesian diffusion estimation reduces communication overhead.

The method is scalable and applicable to diverse models.

A specific Gaussian regression case is derived.

Abstract

The rapidly increasing complexity of (mainly wireless) ad-hoc networks stresses the need of reliable distributed estimation of several variables of interest. The widely used centralized approach, in which the network nodes communicate their data with a single specialized point, suffers from high communication overheads and represents a potentially dangerous concept with a single point of failure needing special treatment. This paper's aim is to contribute to another quite recent method called diffusion estimation. By decentralizing the operating environment, the network nodes communicate just within a close neighbourhood. We adopt the Bayesian framework to modelling and estimation, which, unlike the traditional approaches, abstracts from a particular model case. This leads to a very scalable and universal method, applicable to a wide class of different models. A particularly interesting case - the Gaussian regressive model - is derived as an example.