FIF: A fuzzy information fusion algorithm based on multi-criteria decision making

Ribeiro, Rita A.; Falcão, António J.; Mora, André; Fonseca, José

doi:10.1016/j.knosys.2013.08.032

Cited by 50 publications

(64 citation statements)

References 30 publications

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“…Ribiero et al [55] developed a fuzzy information algorithm using multi-criteria decision analysis and applied it to spacecraft landing safety. This technique could also be applied to IoT research areas.…”

Section: Fuzzy Systems Theory/artificial Neural Networkmentioning

confidence: 99%

Research Challenges for the Internet of Things: What Role Can OR Play?

Ryan

Watson²

2017

Systems

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Abstract:The Internet of Things (IoT) is an extension of the Internet in which large numbers of "things", including sensors, actuators and processors, in addition to human users, are networked and able to provide high resolution data on their environment and exercise a degree of control over it. It is still at an early stage of development, and many problems/research challenges must be solved before it is widely adopted. Many of these are technical, including interoperability and scalability, as billions of heterogeneous devices will be connected, but deciding on how to invest in the IoT is a challenge for business, and there are also major social, legal and ethical challenges, including security and privacy of data collection, which must be resolved. As the future IoT will be a multi-national, multi-industry, multi-technology infrastructure, the paper reviews the global standardization efforts that are underway to facilitate its worldwide creation and adoption. The main purpose of the paper is to give a broad survey, based on published literature, of the methods of Operations Research (OR), both the mathematical tools and techniques of "hard" OR, and the various approaches of Systems Thinking, including "soft" OR, which may assist in dealing with these problems. A subset of these is described in greater depth to better convey what might be involved in applying OR and Systems Thinking to the IoT. It is suggested that OR has a role to play in balancing the technical and non-technical research challenges which confront the IoT.

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Section: Fuzzy Systems Theory/artificial Neural Networkmentioning

confidence: 99%

Research Challenges for the Internet of Things: What Role Can OR Play?

Ryan

Watson²

2017

Systems

View full text Add to dashboard Cite

show abstract

“…In this work we assume there is some sort of uncertainty in the data available, therefore, fuzzy logic concepts will be used to normalize the criteria -thus enabling numerical and comparable data to be aggregated -and then an uncertainty filtering mechanism will be used to tackle the lack of confidence and existing interval deviations on past, current and future data. Specifically, we will use a complete data fusion process, proposed by Ribeiro et al, in 2013 [4], denoted FIF -a fuzzy information fusion algorithm. The authors propose a Data Fusion process, based on fuzzy multi-criteria decision making concepts and techniques, such as: fuzzy sets to normalize the variables; an uncertainty mechanism to handle data imprecision; and mixture operators with weighting functions to fuse the information into a composite of candidate alternatives.…”

Section: Uncertainty Contexts In Decision Makingmentioning

confidence: 99%

“…FIF [4] was not created to support selecting alternatives, but only to fuse information and to tackle data imprecision. Therefore, it does not include temporal considerations, which means that decisions will neither take into account historical trends nor future evolutions.…”

Section: Uncertainty Contexts In Decision Makingmentioning

confidence: 99%

“…Therefore, it does not include temporal considerations, which means that decisions will neither take into account historical trends nor future evolutions. Since here the focus is on how to handle spatial-temporal models with future information within uncertain contexts, our approach combines the FIF method [4] with a dynamic decision model, which integrates historical, current and forecasting information, to support business/supplier selection [5].…”

Section: Uncertainty Contexts In Decision Makingmentioning

confidence: 99%

“…The followed approach combines concepts from a general data fusion method capable of handling imprecision on data [4] with a spatial-temporal multi-criteria model [5], which integrates historical information, present status and forecasting about future information. Further we propose to use a simple fuzzification process to normalize the data [6].…”

Section: Introductionmentioning

confidence: 99%

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