Abstract
Real-time asset tracking in indoor mass production manufacturing environments can reduce losses associated with pausing a production line to locate an asset. Complemented by monitored contextual information, e.g. machine power usage, it can provide smart information, such as which components have been machined by a worn or damaged tool. Although sensor based Internet of Things (IoT) positioning has been developed, there are still key challenges when benchmarked approaches concentrate on precision, using computationally expensive filtering and iterative statistical or heuristic algorithms, as a trade-off for timeliness and scalability. Precise but high-cost hardware systems and invasive infrastructures of wired devices also pose implementation issues in the Industrial IoT (IIoT). Wireless, self-powered sensors are integrated in this paper, using a novel, communication-economical RSSI/ToF ranging method in a proposed semantic IIoT architecture. Annotated data collection ensures accessibility, scalable knowledge discovery and flexibility to changes in consumer and business requirements. Deployed at a working indoor industrial facility the system demonstrated comparable RMS ranging accuracy (ToF 6m and RSSI 5.1m with 40m range) to existing systems tested in non-industrial environments and a 12.6–13.8m mean positioning accuracy.
Highlights
Mass production in manufacturing puts greater emphasis on realtime asset location monitoring: pausing a production line to locate an asset can lead to significant losses when, for example, an engine is produced every 30 s at the Ford factory in Dagenham (Ford, 2014)
Indoor positioning systems have not taken a semantic web or future-proofed Internet of Things (IoT) approach: services and algorithms are embedded on devices and limited to unconventional architectures that are inflexible to business requirements and technological progress (Liu et al, 2007; Xu et al, 2014; Miorandi et al, 2012)
Based on a number of considerations we chose to use an ontology to investigate the development of a semantic architecture, as it is ontologies that describe service implementation and data access in the semantic web
Summary
Mass production in manufacturing puts greater emphasis on realtime asset location monitoring: pausing a production line to locate an asset can lead to significant losses when, for example, an engine is produced every 30 s at the Ford factory in Dagenham (Ford, 2014). The IoT supports resource sharing through a global network infrastructure of interconnected, intelligent devices for environmental, healthcare, military and industrial monitoring (Xu et al, 2014), often in the form of low-cost and deployed WSN These necessitate solutions to integrate common functionality and the large amounts of data output by these devices. The highest measurement accuracy shown by WSN ranging approaches tested indoors has been 4–6 m in office environments with static floor-plans and no significant obstacles (Pettinato et al, 2012; Mazomenos et al, 2011) and on a small scale with toy car tracking (Blumrosen et al, 2013) These approaches have used software, as opposed to precision hardware acknowledgement and time stamping, and large numbers of ranging measurements (1000–5000 per location at 250 kbps (Mazomenos et al, 2013) – 500 kbps (Pettinato et al, 2012)).
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