Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture

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 be...

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Main Authors: Sarogini Pease, Paul Conway, Andrew West
Format: Default Article
Published: 2017
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Online Access:https://hdl.handle.net/2134/27121
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id rr-article-9563072
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spelling rr-article-95630722017-10-11T00:00:00Z Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture Sarogini Pease (1257978) Paul Conway (1249635) Andrew West (1259121) Mechanical engineering not elsewhere classified Indoor tracking Wireless sensor network Semantic web Cross layer middleware Internet of things Communication networks Mechanical Engineering not elsewhere classified 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. 2017-10-11T00:00:00Z Text Journal contribution 2134/27121 https://figshare.com/articles/journal_contribution/Hybrid_ToF_and_RSSI_real-time_semantic_tracking_with_an_adaptive_industrial_internet_of_things_architecture/9563072 CC BY 4.0
institution Loughborough University
collection Figshare
topic Mechanical engineering not elsewhere classified
Indoor tracking
Wireless sensor network
Semantic web
Cross layer middleware
Internet of things
Communication networks
Mechanical Engineering not elsewhere classified
spellingShingle Mechanical engineering not elsewhere classified
Indoor tracking
Wireless sensor network
Semantic web
Cross layer middleware
Internet of things
Communication networks
Mechanical Engineering not elsewhere classified
Sarogini Pease
Paul Conway
Andrew West
Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture
description 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.
format Default
Article
author Sarogini Pease
Paul Conway
Andrew West
author_facet Sarogini Pease
Paul Conway
Andrew West
author_sort Sarogini Pease (1257978)
title Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture
title_short Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture
title_full Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture
title_fullStr Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture
title_full_unstemmed Hybrid ToF and RSSI real-time semantic tracking with an adaptive industrial internet of things architecture
title_sort hybrid tof and rssi real-time semantic tracking with an adaptive industrial internet of things architecture
publishDate 2017
url https://hdl.handle.net/2134/27121
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