A B S T R A C TAs 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, O., & Kumar, S. (2006). Analytic hierarchy process: An overview of applications. European Journal of operational research, 169(1), 1-29.]. 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).

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International audienceBy connecting devices, people, vehicles and infrastructures everywhere in a city, governments and their partners can improve community wellbeing and other economic and financial aspects (e.g., cost and energy savings). Nonetheless, smart cities are complex ecosystems that comprise many different stakeholders (network operators, managed service providers, logistic centers. . . ) who must work together to provide the best services and unlock the commercial potential of the so-called IoT. This is one of the major challenges that faces today’s smart city movement, and the emerging “API economy”. Indeed, while new smart connected objects hit the market every day, they mostly feed “vertical silos” (e.g., vertical apps, siloed apps. . . ) that are closed to the rest of the IoT, thus hampering developers to produce new added value across multiple platforms and/or application domains. Within this context, the contribution of this paper is twofold: (i) present the strategic vision and ambition of the EU to overcome this critical vertical silos’ issue; (ii) introduce the first building blocks underlying an open IoT ecosystem developed as part of an EU (Horizon 2020) projet and a joint project initiative (IoT-EPI). The practicability of this ecosystem, along with a performance analysis, are carried out considering a proof-of-concept for enhanced sporting event management in the context of the forthcoming FIFA World Cup 2022 in Qatar

During recent years, more and more Open Data becomes available and used as part of the Open Data movement. However, there are reported issues with the quality of the metadata in data portals and the data itself. This is a serious risk that could disrupt the Open Data project, as well as e-government initiatives since the data quality needs to be managed to guarantee the reliability of e-government to the public. First quality assessment frameworks emerge to evaluate the quality for a given dataset or portal along various dimensions (e.g., information completeness). Nonetheless, a common problem with such frameworks is to provide meaningful ranking mechanisms that are able to integrate several quality dimensions and user preferences (e.g., a portal provider is likely to have different quality preferences than a portal consumer). To address this multi-criteria decision making problem, our research work applies AHP (Analytic Hierarchy Process), which compares 146 active Open Data portals across 44 countries, powered by the CKAN software.

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