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

Pease, Sarogini Grace; Conway, Paul; West, Andrew A.

doi:10.1016/j.jnca.2017.10.010

Cited by 20 publications

(5 citation statements)

References 28 publications

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“…Even if technological advancements reduce the costs for embedded computing platforms and tags steadily, the computation power and storage increase simultaneously. This shows that the costs are a great barrier, particularly for high-quality sensors (Miorandi et al , 2012; Pease et al , 2017; Pang et al , 2015; Lee and Lee, 2015). Small and medium enterprises (SMEs) in particular face the challenge of high commitment without maximizing the utility of IoT (Yan, 2017; Neirotti et al , 2018).…”

Section: Frameworkmentioning

confidence: 99%

Impact of IoT challenges and risks for SCM

Birkel

Hartmann

2019

SCM

117

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Purpose The purpose of this paper is to offer a comprehensive overview of challenges and risks of the Internet of Things (IoT) in supply chain management (SCM) and provide a structured framework for classifying and analyzing the relevant literature to deduce insights for research and practice. Design/methodology/approach A systematic literature review (SLR) of 102 peer-reviewed journal articles on the topic of IoT in SCM was conducted. Findings This review identifies, categorizes and describes the relevant literature regarding the dimensions time and specificity. The resulting framework contains a holistic overview including focus areas and relations of macro-environmental, network-related and organizational challenges and risks. Furthermore, this review represents a conceptual framework for future research, considering the multidisciplinary body of the subject and provides an analysis of the timeline of literature, journals and used methodologies. Practical implications This study offers valuable insights into IoT and its impact on applications, processes and the structure of organizations and supply chains (SCs). The identification of challenges and risks helps to prepare for changes in the future because of the disruptive nature of this technology. Originality/value The originality of this paper lies in the synthesis of the fragmented body of literature of IoT challenges and risks for SCM. The derived framework and identification of relations and research gaps provide the foundation for further research to evaluate the relations between challenges and risks and offer solutions.

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Section: Frameworkmentioning

confidence: 99%

Impact of IoT challenges and risks for SCM

Birkel

Hartmann

2019

SCM

117

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“…(Pelka and Hellbrück, 2016) presents a unified architecture for location systems to integrate hardware, software and algorithms. (Pease, Conway and West, 2017) proposes a semantic IIoT architecture using a communication economical RSSI/ToF ranging method.…”

Section: Inroductionmentioning

confidence: 99%

Design and Implementation of Real-Time Localization System (RTLS) based on UWB and TDoA Algorithm

Zhang¹,

Yang²,

Liu³

et al. 2021

Preprint

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“…When location information can be associated with monitored contextual information, e.g. machine power usage and vibration, it can be used to provide smart monitoring information, such as which components have been machined by a worn or damaged tool [58]. RFID is the most commonly utilized product tracking and automation technology, especially useful in the supply chain industry [59], as well as in more specialized industries of asset tracking like identification of individual farm animals [61].…”

Section: H Product-service Systemsmentioning

confidence: 99%

Data Management in Industry 4.0: State of the Art and Open Challenges

2019

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Information and communication technologies are permeating all aspects of industrial and manufacturing systems, expediting the generation of large volumes of industrial data. This article surveys the recent literature on data management as it applies to networked industrial environments and identifies several open research challenges for the future. As a first step, we extract important data properties (volume, variety, traffic, criticality) and identify the corresponding data enabling technologies of diverse fundamental industrial use cases, based on practical applications. Secondly, we provide a detailed outline of recent industrial architectural designs with respect to their data management philosophy (data presence, data coordination, data computation) and the extent of their distributiveness. Then, we conduct a holistic survey of the recent literature from which we derive a taxonomy of the latest advances on industrial data enabling technologies and data centric services, spanning all the way from the field level deep in the physical deployments, up to the cloud and applications level. Finally, motivated by the rich conclusions of this critical analysis, we identify interesting open challenges for future research. The concepts presented in this article thematically cover the largest part of the industrial automation pyramid layers. Our approach is multidisciplinary, as the selected publications were drawn from two fields; the communications, networking and computation field as well as the industrial, manufacturing and automation field. The article can help the readers to deeply understand how data management is currently applied in networked industrial environments, and select interesting open research opportunities to pursue.

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

Cited by 20 publications

References 28 publications

Impact of IoT challenges and risks for SCM

Impact of IoT challenges and risks for SCM

Design and Implementation of Real-Time Localization System (RTLS) based on UWB and TDoA Algorithm

Data Management in Industry 4.0: State of the Art and Open Challenges

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