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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/27121

Title: Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture
Authors: Pease, Sarogini G.
Conway, Paul P.
West, Andrew A.
Keywords: Indoor tracking
Wireless sensor network
Semantic web
Cross layer middleware
Internet of things
Communication networks
Issue Date: 2017
Publisher: Elsevier
Citation: PEASE, S.G., CONWAY, P.P. and WEST, A.A., 2017. Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture. Journal of Network and Computer Applications, 99, pp. 98-109.
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, 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.
Description: This is an Open Access Article. It is published by Elsevier under the Creative Commons Attribution 4.0 International Licence (CC BY). Full details of this licence are available at: http://creativecommons.org/licenses/by/4.0/.
Sponsor: The authors would like to thank the EPSRC for the funding of the project Adaptive Informatics for Intelligent Manufacturing (EP/K014137/1).
Version: Published
DOI: 10.1016/j.jnca.2017.10.010
URI: https://dspace.lboro.ac.uk/2134/27121
Publisher Link: https://doi.org/10.1016/j.jnca.2017.10.010
ISSN: 1084-8045
Appears in Collections:Published Articles (Mechanical, Electrical and Manufacturing Engineering)

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