Internet of things patterns

Reinfurt, Lukas; Breitenbücher, Uwe; Falkenthal, Michael; Leymann, Frank; Riegg, Andreas

doi:10.1145/3011784.3011789

Cited by 59 publications

(30 citation statements)

References 8 publications

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“…This pattern propagates a centralized data storage on the platform tier [19]. Reinfurt et al (2016) proposed five design patterns for IoT systems whereas Qanbari et al (2016) developed four design patterns for the creation of IoT edge applications. Both approaches focus on the edge tier and a more distributed data storage [20,21].…”

Section: Related Workmentioning

confidence: 99%

“…Reinfurt et al (2016) proposed five design patterns for IoT systems whereas Qanbari et al (2016) developed four design patterns for the creation of IoT edge applications. Both approaches focus on the edge tier and a more distributed data storage [20,21]. Considering decentralized stored data, Mendis et al (2018) created a decentralized DL architecture using BCT services where decentralized DL models are trained with private data and shared among the contributors [22].…”

Section: Related Workmentioning

confidence: 99%

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Design Patterns based on Deep Learning analyzing Distributed Data

Burkhardt¹,

Agyei-Kena²,

Frey³

et al. 2020

WI2020 Zentrale Tracks

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Data silos in many system landscapes complicate the creation of comprehensive information. Distributed Ledger Technology enables trust between assets via a distributed, secure and immutable storage of transactions. Deep Learning realizes intelligence to make decisions, conduct them and analyze their results based on the gathered data. In order to counteract the limitations of current system landscapes, an integrative implication of both Distributed Ledger Technology and Deep Learning is needed. Many considerations arise during the design of information systems integrating the named technologies. Transparency over the training and deployment of various Deep Learning methods on a distributed data landscape needs to be achieved. Using both a literature review and a qualitative research approach, this paper describes the development of design patterns and their selection criteria with different dimensions taken into consideration. The evaluation phase comprises of semi-structured interviews with experts from different disciplines. The result of this paper guides stakeholders in the selection of a suitable technical solution.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Design Patterns based on Deep Learning analyzing Distributed Data

Burkhardt¹,

Agyei-Kena²,

Frey³

et al. 2020

WI2020 Zentrale Tracks

View full text Add to dashboard Cite

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“…These platforms, and the subject of the migration of legacy software into them, have become subjects of dedicated research (e.g., [13,17,28,23,8]). Major cloud platform providers, such as Amazon Web Services (AWS) also provide some guidance in their publications (e.g., [1,18,3,21]).…”

Section: Background and Motivationmentioning

confidence: 99%

Software Migration and Architecture Evolution with Industrial Platforms: A Multi-case Study

Plakidas

Schall

Zdun

2018

Software Architecture

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“…While their analysis considers many present state-of-the-art protocols for connecting IoT devices to middleware systems and applications, the approach to overcome the ever-present difficulty of addressing and connecting IoT devices as presented in this work extends their list of investigated protocols. Reinfurt et al (Reinfurt et al, 2016;Reinfurt et al, 2017) capture the conceptual essence of different fields of IoT research into patterns. Thereby, they provide proven nuggets of advice about the design and characteristics of IoT systems.…”

Section: Related Workmentioning

confidence: 99%

Utilising the Tor Network for IoT Addressing and Connectivity

Baumann

Odefey

Hudert

et al. 2018

Proceedings of the 8th International Conference on Cloud Computing and Services Science

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For Internet of Things (IoT) devices and cyber-physical systems (CPS), it is required to connect them securely and reliably to some form of cloud environment or computing entity for control, management and utilisation. The Internet is a suitable, standardized, and proven means for the connection of IoT devices in various scenarios. Connection over the Internet utilises existing protocols, standards, technologies and avoids investment in new, specialised concepts. Thereby, this connection requires a transparent addressing schema which is commonly TCP/IP, using domain names and IP addresses. However, in industrial, commercial and private networks, the addressability and connectability/connectivity is often limited by firewalls, proxies and router configurations utilising NAT. Thus, the present network configurations hinder the establishment of connections between IoT devices across different locations. Therefore, the method for connecting IoT devices in a client-server configuration proposed herein utilises the Tor (previously: The onion router/routing) network for addressing of and secured communication to IoT and CPS devices. It is an overlay protocol that was designed to allow for robust and anonymous communication. The benefit of this approach is to enable addressability and connectivity of IoT devices in firewalled and potentially unknown and changing network environments, thus allowing for IoT devices to be used reliably behind firewalls as long as outgoing communication is not blocked.

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Internet of things patterns

Cited by 59 publications

References 8 publications

Design Patterns based on Deep Learning analyzing Distributed Data

Design Patterns based on Deep Learning analyzing Distributed Data

Software Migration and Architecture Evolution with Industrial Platforms: A Multi-case Study

Utilising the Tor Network for IoT Addressing and Connectivity

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