Experiments with Self-Stabilizing Distributed Data Fusion

Abstract

The Theory of Belief Functions is a formal frame-work for reasoning with uncertainty that is well suited for rep-resenting unreliable information and weak states of knowledge. In a previous work, a distributed algorithm for computing data fusion on-the-fly has been introduced, avoiding gathering the data on a single node before computation. In this paper, we present an experimental study of its properties. This algorithm is self-stabilizing and runs on unreliable message passing networks. It converges in finite time whatever is the initialization of the system and for any unknown topology. First we explain the algorithm implementation on an unreliable message passing environment and we implement a simple use-case. Then, by experimenting with this distributed application on a realistic network emulator, we show its interest for enforcing local confidence using close nodes, saving bandwidth and warning dangers. Moreover, we focus on the interesting connections between the data fusion operator and the self-stabilizing properties and we highlight the importance of the discounting.