A Proximity-Based Approach for Dynamically Matching Industrial Assets and Their Operators Using Low-Power IoT Devices

TL;DR

This paper presents a novel proximity-based system using low-power BLE IoT devices and algorithms to accurately match industrial assets with their operators, enhancing safety and efficiency in construction environments.

Contribution

It introduces a new framework combining wearable and asset-embedded BLE devices with EKF-based distance estimation and cloud algorithms for real-time asset-operator matching.

Findings

Median distance estimation accuracy of 0.49 meters

Correct matching accuracy of up to 98.6%

Validated through indoor and outdoor construction experiments

Abstract

Asset tracking solutions have proven their significance in industrial contexts, as evidenced by their successful commercialization (e.g., Hilti On!Track). However, a seamless solution for matching assets with their users, such as operators of construction power tools, is still missing. By enabling assetuser matching, organizations gain valuable insights that can be used to optimize user health and safety, asset utilization, and maintenance. This paper introduces a novel approach to address this gap by leveraging existing Bluetooth Low Energy (BLE)-enabled low-power Internet of Things (IoT) devices. The proposed framework comprises the following components: i) a wearable device, ii) an IoT device attached to or embedded in the assets, iii) an algorithm to estimate the distance between assets and operators by exploiting simple received signal strength indicator (RSSI) measurements via an Extended Kalman Filter (EKF), and iv) a cloud-based algorithm that collects all estimated distances to derive the correct asset-operator matching. The effectiveness of the proposed system has been validated through indoor and outdoor experiments in a construction setting for identifying the operator of a power tool. A physical prototype was developed to evaluate the algorithms in a realistic setup. The results demonstrated a median accuracy of 0.49m in estimating the distance between assets and users, and up to 98.6% in correctly matching users with their assets.