Sensei: Self-Supervised Sensor Name Segmentation

TL;DR

Sensei is a self-supervised neural framework that automatically segments sensor names in smart buildings, reducing manual effort and handling diverse naming conventions effectively.

Contribution

Sensei introduces a novel self-supervised approach using neural language models to segment sensor names without human annotations, adaptable to various building-specific naming schemes.

Findings

Outperforms baseline segmentation methods on real-world datasets

Effective across multiple buildings with diverse naming conventions

Reduces manual annotation effort significantly

Abstract

A sensor name, typically an alphanumeric string, encodes the key context (e.g., function and location) of a sensor needed for deploying smart building applications. Sensor names, however, are curated in a building vendor-specific manner using different structures and vocabularies that are often esoteric. They thus require tremendous manual effort to annotate on a per-building basis; even to just segment these sensor names into meaningful chunks. In this paper, we propose a fully automated self-supervised framework, Sensei, which can learn to segment sensor names without any human annotation. Specifically, we employ a neural language model to capture the underlying sensor naming structure and then induce self-supervision based on information from the language model to build the segmentation model. Extensive experiments on five real-world buildings comprising thousands of sensors demonstrate the superiority of Sensei over baseline methods.