P2P Data Synchronization for Product Lifecycle Management

Abstract Intelligent products are an undeniable asset for efficient Product Lifecycle Management (PLM), providing ways to capture events related to physical objects at various locations and times. Today and more than ever before, PLM tools and systems must be built upon standards for enhancing interoperability among all product stakeholders and developing tools independent of specific vendors, applications, and operating systems. Based on this observation, this paper develops strategies to improve "information sustainability" in PLM environments using standardized communication interfaces defined by a recent standard proposal named Quantum Lifecycle Management (QLM) messaging standards. More concretely, data synchronization models based upon QLM standards are developed to enable the synchronization of product-related information among various systems, networks, and organizations involved throughout the product lifecycle. Our proposals are implemented and assessed based on two distinct platforms defined in the healthcare and home automation sectors

Micro-billing framework for IoT: Research & Technological foundations

In traditional product companies, creating value meant identifying enduring customer needs and manufacturing well-engineered solutions. Two hundred and fifty years after the start of the Industrial Revolution, this pattern of activity plays out every day in a connected world where products are no longer one-and-done. Making money is not anymore limited to physical product sales; other downstream revenue streams become possible (e.g., service-based information, Apps). Nonetheless, it is still challenging to stimulate the IoT market by enabling IoT stakeholders (from organizations to an individual persons) to make money out of the information that surrounds them. Generally speaking, there is a lack of micro-billing frameworks and platforms that enable IoT stakeholders to publish/discover, and potentially sell/buy relevant and useful IoT information items. This paper discusses important aspects that need to be considered when investigating and developing such a framework/platform. A high-level requirement analysis is then carried out to identify key technological and scientific building blocks for laying the foundation of an innovative micro-billing framework named IoTBnB (IoT puBlication aNd Billing)

Micro-billing framework for IoT: Research & Technological foundations

In traditional product companies, creating value meant identifying enduring customer needs and manufacturing well-engineered solutions. Two hundred and fifty years after the start of the Industrial Revolution, this pattern of activity plays out every day in a connected world where products are no longer one-and-done. Making money is not anymore limited to physical product sales; other downstream revenue streams become possible (e.g., service-based information, Apps). Nonetheless, it is still challenging to stimulate the IoT market by enabling IoT stakeholders (from organizations to an individual persons) to make money out of the information that surrounds them. Generally speaking, there is a lack of micro-billing frameworks and platforms that enable IoT stakeholders to publish/discover, and potentially sell/buy relevant and useful IoT information items. This paper discusses important aspects that need to be considered when investigating and developing such a framework/platform. A high-level requirement analysis is then carried out to identify key technological and scientific building blocks for laying the foundation of an innovative micro-billing framework named IoTBnB (IoT puBlication aNd Billing)

Quality-by-design-engineered pBFT consensus configuration for medical device development

International audienceHealth product development has been lately tainted by wariness in manufacturers, which has reduced trust in the system. It also affects Digital Health were patients' big data flows generated by numerous sensors are subject to increased security and confidentiality to lower the risks incurred. Our aim is to increase trust in the system again by implementing a dedicated Blockchain solution where data are automatically stored, and where each actor in the development process can access and host them. Blockchain has its downside, such as a subefficient management of big data flows. This study is a first step toward defining a Blockchain solution that will not deteriorate the Quality of Service in this particular context by using the Quality by Design approach. We will mainly focus on the time to consensus attribute which affects both of them. From our experiments' results generated after running screening design and surface response design on a practical Byzantine Fault Tolerance (pBFT) simulator, we find that the transmission time and the message processing time are the most impacting factors

Knowledge-based Consistency Index for Fuzzy Pairwise Comparison Matrices

Abstract—Fuzzy AHP is today one of the most used Multiple Criteria Decision-Making (MCDM) techniques. The main argument to introduce fuzzy set theory within AHP lies in its ability to handle uncertainty and vagueness arising from decision makers (when performing pairwise comparisons between a set of criteria/alternatives). As humans usually reason with granular information rather than precise one, such pairwise comparisons may contain some degree of inconsistency that needs to be properly tackled to guarantee the relevance of the result/ranking. Over the last decades, several consistency indexes designed for fuzzy pairwise comparison matrices (FPCMs) were proposed, as will be discussed in this article. However, for some decision theory specialists, it appears that most of these indexes fail to be properly “axiomatically” founded, thus leading to misleading results. To overcome this, a new index, referred to as KCI (Knowledge-based Consistency Index) is introduced in this paper, and later compared with an existing index that is axiomatically well founded. The comparison results show that (i) both indexes perform similarly from a consistency measurement perspective, but (ii) KCI contributes to significantly reduce the computation time, which can save expert’s time in some MCDM problems

Linked Vocabulary Recommendation Tools for Internet of Things: A Survey

The Semantic Web emerged with the vision of eased integration of heterogeneous, distributed data on the Web. The approach fundamentally relies on the linkage between and reuse of previously published vocabularies to facilitate semantic interoperability. In recent years, the Semantic Web has been perceived as a potential enabling technology to overcome interoperability issues in the Internet of Things (IoT), especially for service discovery and composition. Despite the importance of making vocabulary terms discoverable and selecting most suitable ones in forthcoming IoT applications, no state-of-the-art survey of tools achieving such recommendation tasks exists to date. This survey covers this gap, by specifying an extensive evaluation framework and assessing linked vocabulary recommendation tools. Furthermore, we discuss challenges and opportunities of vocabulary recommendation and related tools in the context of emerging IoT ecosystems. Overall, 40 recommendation tools for linked vocabularies were evaluated, both, empirically and experimentally. Some of the key ndings include that (i) many tools neglect to thoroughly address both, the curation of a vocabulary collection and e ective selection mechanisms; (ii) modern information retrieval techniques are underrepresented; and (iii) the reviewed tools that emerged from Semantic Web use cases are not yet su ciently extended to t today’s IoT projects

Towards Semantic Interoperability in an Open IoT Ecosystem for Connected Vehicle Services

A present challenge in today’s Internet of Things (IoT) ecosystem is to enable interoperability across hetero- geneous systems and service providers. Restricted access to data sources and services limits the capabilities of a smart city to improve social, environmental and economic aspects. Interoperability in the IoT is concerned with both, messaging interfaces and semantic understanding of heterogeneous data. In this paper, the first building blocks of an emerging open IoT ecosystem developed at the EU level are presented. Se- mantic web technologies are applied to the existing messaging components to support and improve semantic interoperability. The approach is demonstrated with a proof-of-concept for connected vehicle services in a smart city setting

Knowledge-based Consistency Index for Fuzzy Pairwise Comparison Matrices

Abstract—Fuzzy AHP is today one of the most used Multiple Criteria Decision-Making (MCDM) techniques. The main argument to introduce fuzzy set theory within AHP lies in its ability to handle uncertainty and vagueness arising from decision makers (when performing pairwise comparisons between a set of criteria/alternatives). As humans usually reason with granular information rather than precise one, such pairwise comparisons may contain some degree of inconsistency that needs to be properly tackled to guarantee the relevance of the result/ranking. Over the last decades, several consistency indexes designed for fuzzy pairwise comparison matrices (FPCMs) were proposed, as will be discussed in this article. However, for some decision theory specialists, it appears that most of these indexes fail to be properly “axiomatically” founded, thus leading to misleading results. To overcome this, a new index, referred to as KCI (Knowledge-based Consistency Index) is introduced in this paper, and later compared with an existing index that is axiomatically well founded. The comparison results show that (i) both indexes perform similarly from a consistency measurement perspective, but (ii) KCI contributes to significantly reduce the computation time, which can save expert’s time in some MCDM problems

A state-of the-art survey & testbed of Fuzzy AHP (FAHP) applications

As a practical popular methodology for dealing with fuzziness and uncertainty in Multiple Criteria Decision-Making (MCDM), Fuzzy AHP (FAHP) has been applied to a wide range of applications. As of the time of writing there is no state of the art survey of FAHP, we carry out a literature review of 190 application papers (i.e., applied research papers), published between 2004 and 2016, by classifying them on the basis of the area of application, the identified theme, the year of publication, and so forth. The identified themes and application areas have been chosen based upon the latest state-of-the-art survey of AHP conducted by Vaidya and Kumar (2006). To help readers extract quick and meaningful information, the reviewed papers are summarized in various tabular formats and charts. Unlike previous literature surveys, results and findings are made available through an online (and free) testbed, which can serve as a ready reference for those who wish to apply, modify or extend FAHP in various applications areas. This online testbed makes also available one or more fuzzy pairwise comparison matrices (FPCMs) from all the reviewed papers (255 matrices in total). In terms of results and findings, this survey shows that: (i) FAHP is used primarily in the Manufacturing, Industry and Government sectors; (ii) Asia is the torchbearer in this field, where FAHP is mostly applied in the theme areas of Selection and Evaluation; (iii) a significant amount of research papers (43% of the reviewed literature) combine FAHP with other tools, particularly with TOPSIS, QFD and ANP (AHP’s variant); (iv) Chang’s extent analysis method, which is used for FPCMs’ weight derivation in FAHP, is still the most popular method in spite of a number of criticisms in recent years (considered in 57% of the reviewed literature)