SPITFIRE: toward a semantic web of things

Pfisterer, Dennis; Römer, Kay Uwe; Bimschas, Daniel; Kleine, Oliver; Mietz, Richard; Truong, Cuong; Hasemann, Henning; Kröller, Alexander; Pagel, Max; Hauswirth, Manfred; Karnstedt, Marcel; Leggieri, Myriam; Passant, Alexandre; Richardson, Ryan

doi:10.1109/mcom.2011.6069708

Cited by 288 publications

(153 citation statements)

References 4 publications

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“…Currently, approaches in fundamental issues of semantics in IoT are at an early stage. SPITFIRE [80] is a system trying to give semantic annotations for smart objects through a 714 centralized service (server). This exposes many restrictions as the third-party services would be able to filter the information from the original smart object services, and again play a role of protocol translation gateways.…”

Section: Semantic Compositionmentioning

confidence: 99%

Service composition for IP smart object using realtime Web protocols: Concept and research challenges

Han

Khan

Lee

et al. 2016

Computer Standards & Interfaces

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Section: Semantic Compositionmentioning

confidence: 99%

Service composition for IP smart object using realtime Web protocols: Concept and research challenges

Han

Khan

Lee

et al. 2016

Computer Standards & Interfaces

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“…and other preferences (e.g., quality-over-price measures). In addition, data from the Internet of Things can be integrated in the penalty function to include user context data (e.g., using approaches, such as [47,48]), which uses the user's context (e.g., the current weather) or other IoT sources of data to calculate the penalty for the proposals, so that the best matching proposal is assigned the lowest penalty value. The ranking engine can therefore now assign an objective function value to each given proposal and order them by their objective function value.…”

Section: Functional View Of the Dmsmentioning

confidence: 99%

Business Model Design and Architecture for the Internet of Everything

Pfisterer

Radonjic-Simic

Reichwald

2016

JSAN

Self Cite

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Smart devices and cyber-physical systems, which are interconnected to IT systems and services, form the basis for the arising Internet of Everything, opening up new economic opportunities for its participants and users beyond its technological aspects and challenges. While today's e-business scenarios are mostly dominated by a few centralized online platforms, future business models, which will be feasible for the Internet of Everything, need to address special requirements. Such business models, e.g., leveraging the possibilities of smart cities, need to cope with arbitrary combinations of products and services orchestrated into complex products in a highly distributed and dynamic environment. Furthermore, these arbitrary combinations are influenced by real-time context information derived from sensor networks or IT systems, as well as the users' requirements and preferences. The complexity of finding the optimal product/service combination overstrains users and leads to decisions according to the principle of adverse selection (i.e., choosing good enough instead of optimal). Such e-business models require an appropriate underlying value generation architecture that supports users in this process. In this paper, we develop a business model that addresses these problems. In addition, we present the Distributed Market Spaces (DMS) software-system architecture as a possible implementation, which enables the aforementioned decentralized and context-centric e-business scenario and leverages the commercial possibilities of smart cities.

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“…[51]. In this context "things" refers to realworld entities, such as meeting rooms and parking spots [51].…”

Section: Using Linked Data To Create Human Digital Memoriesmentioning

confidence: 99%

“…[51]. In this context "things" refers to realworld entities, such as meeting rooms and parking spots [51]. This work is of significant importance because, when building a memory, incorporating as much data from the surrounding environment and smart objects, is vital.…”

Section: Using Linked Data To Create Human Digital Memoriesmentioning

confidence: 99%