Will Intelligent Assets Take Off? Toward Self-Serving Aircraft

Brintrup, Alexandra; McFarlane, Duncan; Ranasinghe, Damith C.; López, Tomás Sánchez; Owens, Kenneth

doi:10.1109/mis.2009.89

Cited by 35 publications

(23 citation statements)

References 4 publications

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“…The increased interactivity and multi-networked nature of physical entities is fuelled by Internet of Things technologies (IoT) which empower physical product and asset entities to become intelligent (Brintrup et al 2011;Främling et al 2013;Kiritsis 2011). The physical IoT entities interactivity is supported by internet-working and web-based programming and is essentially supporting a more proactive engineering approach to be put in place (Demoly, Pels, and Gomes 2013).…”

Section: Context-aware Infrastructuresmentioning

confidence: 99%

Current trends on ICT technologies for enterprise information systems

Kadiri

Grabot

Thoben

et al. 2016

Computers in Industry

119

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Section: Context-aware Infrastructuresmentioning

confidence: 99%

Current trends on ICT technologies for enterprise information systems

Kadiri

Grabot

Thoben

et al. 2016

Computers in Industry

119

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“…Beyond a significant focus on manufacturing control, we note that DI has also been examined in other industrial domains such as logistics [29][30] [31] and maintenance and service [32] [33]. Many of the comments made in this section regarding manufacturing could equally apply to these other domains.…”

Section: Distributed Intelligence For Manufacturing Control: a Reviewmentioning

confidence: 99%

The Role of Distributed Intelligence in Warehouse Management Systems

Giannikas

McFarlane

et al. 2014

Studies in Computational Intelligence

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Abstract. Third-party-logistics warehouses have been gaining popularity in recent years mainly due to their ability to buffer the material flow along a supply chain and consolidate the products from various suppliers, which has a major impact on supply chain efficiency. The issues and challenges in warehouse management are similar in many ways to those faced in manufacturing control, resilience and adaptability being two such issues. Since distributed intelligence (DI) approaches have been extensively studied to address such issues in manufacturing control, this paper examines the possible adoption of DI approaches in warehouse management systems (WMS). Further, the paper discusses the challenges in warehouse management and compares these challenges with the characteristics of manufacturing problems that DI approach is suited to.

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“…For a recent review of intelligent product definitions please see Holmstöm et al (2009). Although there has been many variations on this definition, and debates on what we expect from an intelligent product, the last decade saw examples of autonomous products that manufacture themselves (Bussmann and Sieverding 2001) and monitor themselves, ordering maintenance when needed (Brintrup et al 2010). More primitive "intelligent" products have encompassed other parts of the product lifecycle, such as retail, service, and recycling, where products had no autonomy, but users gave decisions upon them using a combination of sensory data and decision support software.…”

Section: Software Agentsmentioning

confidence: 99%

“…More primitive "intelligent" products have encompassed other parts of the product lifecycle, such as retail, service, and recycling, where products had no autonomy, but users gave decisions upon them using a combination of sensory data and decision support software. For a detailed review of the intelligent product research landscape, please see Brintrup et al (2008).…”

Section: Software Agentsmentioning

confidence: 99%

“…This method promotes a service view rather than a product based view. On the other hand, academic literature and industrial frontrunners in the area argue that a product centric view is an intuitive one that distributes risk and reduces bottlenecks (Brintrup et al 2010). Given that multiple organisations and objects will use the Internet of Things architecture throughout a product's lifecycle, it is important that a scalable and interoperable architecture is proposed, which reduces reliance on centralised databases and processing.…”

Section: Product To User Communicationmentioning

confidence: 99%

See 1 more Smart Citation

Resource Management in the Internet of Things: Clustering, Synchronisation and Software Agents

López

Brintrup

Isenberg

et al. 2011

Architecting the Internet of Things

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Abstract. The objects of the Internet of Things will be empowered by embedded devices whose constrained resources will need to be managed efficiently. It is envisioned that these devices will be able to form ad-hoc networks, and that the connection from these networks to the Internet of Things infrastructure will not always be possible. In this chapter we propose the use of clustering, software agents and synchronisation techniques in order to overcome the challenges of managing the resources of the Internet of Things objects. We argue that clustering will be beneficial to reduce the energy expenditure and improve the scalability and robustness of the object networks. Software agents will aide in the automation of task, both for the objects and the Internet of Things users. Finally, synchronisations techniques will be necessary to address the various challenges of harmonising plenty of copies of object data with potentially partially disconnected Internet of Things architecture components. BIBA -Bremer

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Will Intelligent Assets Take Off? Toward Self-Serving Aircraft

Cited by 35 publications

References 4 publications

Current trends on ICT technologies for enterprise information systems

Current trends on ICT technologies for enterprise information systems

The Role of Distributed Intelligence in Warehouse Management Systems

Resource Management in the Internet of Things: Clustering, Synchronisation and Software Agents

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