A Proximity-Based Approach for Dynamically Matching Industrial Assets and Their Operators Using Low-Power IoT Devices
Year: 2024
Type: article
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 asset-user matching, organizations gain valuable insights that can be used to optimize user health and safety, asset utilization, and maintenance. This article 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: 1) a wearable device; 2) an IoT device attached to or embedded in the assets; 3) 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 4) 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.
Source: IEEE Internet of Things Journal
Institutions: ETH Zurich, Hilti (Liechtenstein)
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W4403390455