On the Fundamental Limits of Heterogeneous Distributed Detection: Price of Anonymity
Journal
IEEE International Symposium on Information Theory - Proceedings
Journal Volume
2018-June
Pages
1056-1060
Date Issued
2018
Author(s)
Abstract
In this paper, we explore the fundamental limits of heterogeneous distributed detection in an anonymous sensor network with sensors and a single fusion center. The fusion center collects the single observation from each of the sensors to detect a binary parameter. The sensors are clustered into multiple groups, and different groups follow different discrete distributions under a given hypothesis. The key challenge for the fusion center is the anonymity of sensors - although it knows the exact number of sensors and the distribution of observations in each group, it does not know which group each sensor belongs to. It is hence natural to consider it as a composite hypothesis testing problem. We focus on the Neyman-Pearson setting and give upper and lower bounds of the error exponent of the worst-case type-II probability of error as tends to infinity, assuming the number of sensors in each group is proportional to n. Our results elucidate the price of anonymity in heterogeneous distributed detection. The results are also applied to distributed detection under Byzantine attacks, which hints that the conventional simple hypothesis testing approach might be too pessimistic. A full version of this paper is accessible at: http://homepage.ntu.edu.tw/~ihwanglEprint/isit18hd.pdf
SDGs
Type
conference paper