Reliable Identification Schemes for Asset and Production Tracking in Industry 4.0

Abstract

Revolutionizing logistics and supply chain management in smart manufacturing is one of the main goals of the Industry 4.0 movement. Emerging technologies such as autonomous vehicles, Cyber-Physical Systems and digital twins enable highly automated and optimized solutions in these fields to achieve full traceability of individual products. Tracking various assets within shop-floors and the warehouse is a focal point of asset management; its aim is to enhance the efficiency of logistical tasks. Global players implement their own solutions based on the state of the art technologies. Small and medium companies, however, are still skeptic toward identification based tracking methods, because of the lack of low-cost and reliable solutions. This paper presents a novel, working, reliable, low-cost, scalable solution for asset tracking, supporting global asset management for Industry4.0. The solution uses high accuracy indoor positioning-based on Ultra-Wideband (UWB) radio technology-combined with RFID-based tracking features. Identifying assets is one of the most challenging parts of this work, so this paper focuses on how different identification approaches can be combined to facilitate an efficient and reliable identification scheme.

Keywords: Industry 4.0; IoT; asset tracking; low-cost tracking.

Figure 1

An example system implementing time difference of arrivals (TDOA) and trailateration based localization, where $A_i$ -s are anchor points, $T_i$ -s are UWB tags and $r_{ij}$ -s are distances.

Figure 2

The core elements of the Arrowhead Framework are connected in a service-oriented way.

Figure 3

Local clouds with serving different industrial domains have their internal requirements (such as latency or security) guaranteed—and they can produce and consume services to/from other Local clouds as well. [51]

Figure 4

System-of-Systems established throughout the supply chain network can exchange information in a service oriented architecture. [7]

Figure 5

An example scenario for asset tracking with integrated UWB-based indoor-positioning and Ultra High Radio Frequency Identification (UHF-RFID)-based asset identification.

Figure 6

Detection tree of a START event.

Figure 7

A schematic map of the test environment for measurements, where purple thumbtacks are anchors.

Figure 8

Statistical characteristics of UWB ranging.

Figure 9

Position dataset created by a static device.

Figure 10

Using moving average with different window sizes.

Figure 11

Using moving average with different window sizes on a path tracked by a moving device.

Figure 12

The path of the forklift.

Figure 13

Inertial Measurement Unit (IMU) triggers the system by setting "1" its output.

Figure 14

RFID measurements. (a) The Read Count of the tracked RFID tags. (b) The RSSI value of the detected RFID tags.