2134/27121
Sarogini Pease
Sarogini
Pease
Paul Conway
Paul
Conway
Andrew West
Andrew
West
Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture
Loughborough University
2017
Indoor tracking
Wireless sensor network
Semantic web
Cross layer middleware
Internet of things
Communication networks
Mechanical Engineering not elsewhere classified
2017-10-26 08:20:44
Journal contribution
https://repository.lboro.ac.uk/articles/journal_contribution/Hybrid_ToF_and_RSSI_real-time_semantic_tracking_with_an_adaptive_industrial_internet_of_things_architecture/9563072
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, selfpowered
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.