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...

Full description

Saved in:

Bibliographic Details
Main Authors:	Sarogini Pease, Paul Conway, Andrew West
Format:	Default Article
Published:	2017
Subjects:	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
Online Access:	https://hdl.handle.net/2134/27121
Tags:	Add Tag No Tags, Be the first to tag this record!

id	rr-article-9563072
record_format	Figshare
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
_version_	1796738415875588096

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

Similar Items