Submodularity and Optimality of Fusion Rules in Balanced Binary Relay Trees

TL;DR

This paper investigates the optimal fusion strategies in balanced binary relay trees for distributed detection, demonstrating that the error reduction function is stringsubmodular and that greedy strategies are near-optimal.

Contribution

It introduces the concept of stringsubmodularity to analyze fusion strategies and proves the near-optimality of greedy algorithms for error reduction in relay trees.

Findings

Error reduction is a stringsubmodular function.

Greedy fusion strategies are within a factor of the optimal.

Dynamic programming formulation for fusion strategy optimization.

Abstract

We study the distributed detection problem in a balanced binary relay tree, where the leaves of the tree are sensors generating binary messages. The root of the tree is a fusion center that makes the overall decision. Every other node in the tree is a fusion node that fuses two binary messages from its child nodes into a new binary message and sends it to the parent node at the next level. We assume that the fusion nodes at the same level use the same fusion rule. We call a string of fusion rules used at different levels a fusion strategy. We consider the problem of finding a fusion strategy that maximizes the reduction in the total error probability between the sensors and the fusion center. We formulate this problem as a deterministic dynamic program and express the solution in terms of Bellman's equations. We introduce the notion of stringsubmodularity and show that the reduction in the total error probability is a stringsubmodular function. Consequentially, we show that the greedy strategy, which only maximizes the level-wise reduction in the total error probability, is within a factor of the optimal strategy in terms of reduction in the total error probability.