IoT System Selection as a Fuzzy Multi-Criteria Problem

As an important content in fuzzy mathematics, similarity measure is used to measure the similarity degree between two fuzzy sets. Considering the existing similarity measures, most of them do not consider the hesitancy degree and some methods considering the hesitancy degree are based on the intuitionistic fuzzy sets, intuitionistic fuzzy values. It may cause some counterintuitive results in some cases. In order to make up for the drawback, we present a new approach to construct the similarity measure between two interval‐valued intuitionistic fuzzy sets using the entropy measure and considering the hesitancy degree. In particular, the proposed measure was demonstrated to yield a similarity measure. Besides, some examples are given to prove the practicality and effectiveness of the new measure. We also apply the similarity measure to expert system to solve the problems on pattern recognition and the multicriteria group decision making. In these examples, we also compare it with existing methods such as other similarity measures and the ideal point method.

Atanassov (Intuitionistic fuzzy set, Fuzzy Sets and Systems 20 (1986) 87–96) proposed the intuitionistic fuzzy set (A-IFS) that is a generalization of Zadeh's fuzzy set. The essential elements of an A-IFS are intuitionistic fuzzy values (IFVs), each of which is depicted by a membership degree and a non-membership degree. The existing literature only gives the explanation of the meaning and the aggregation operators of IFVs, which is very hard and non-visualized for us to comprehend. To overcome the above shortcomings, this paper puts forward the diagram method to analyze the structures of the basic aggregation operations of IFVs. In addition, this paper utilizes the diagram method which is similar to joint probability distribution to describe the components of aggregation operations of IFVs, and each IFV depicts a 3-dimensional vector which is regarded as the marginal distributions of the joint probability distribution.

A novel technique for parameter estimation in intuitionistic fuzzy logistic regression model

We first propose a series of similarity measures for intuitionistic fuzzy values (IFVs) based on the intuitionistic fuzzy operators (Atanassov 1995). The parameters in the proposed similarity measures can control the degree of membership and the degree of non-membership of an IFV, which can reflect the decision maker’s risk preference. Moreover, we can obtain some known similarity measures when some fixed values are assigned to the parameters. Furthermore, we apply the similarity measures to aggregate IFVs and develop some aggregation operators, such as the intuitionistic fuzzy dependent averaging operator and the intuitionistic fuzzy dependent geometric operator, whose prominent characteristic is that the associated weights only depend on the aggregated intuitionistic fuzzy arguments and can relieve the influence of unfair arguments on the aggregated results. Based on these aggregation operators, we develop some group decision making methods, and finally extend our results to interval-valued intuitionistic fuzzy environment.

Best-worst method (BWM) has attracted increasing attention. It has been generalized to different fuzzy environments and applied to various real-life decision problems. This article develops a new intuitionistic fuzzy (IF) BWM (IFBWM) for multicriteria decision-making. When a decision maker (DM) makes comparisons, there may be some hesitancies. Thus, the reference comparisons are represented as intuitionistic fuzzy values (IFVs), the Best-to-Others vector and the Others-to-Worst vector are IF vectors. According to the multiplicative consistency of intuitionistic fuzzy preference relation, this article gives the consistency equations and views them as IF equations. The derivation of optimal IF weights of criteria is formulated as an IF decision-making problem. Thereby, a mathematical programming model is constructed to assure that the derived optimal IF weights of criteria is a normalized IF weight vector. Depending on the risk preference of DM, four linear programming models are presented to obtain the optimal IF weights based on the constructed mathematical programming model for the optimistic DM, the pessimistic DM, and the neutral DM, respectively. Furthermore, this article investigates the process of improving the consistency. Several examples are demonstrated to show the application and effectiveness of the proposed IFBWM.

Smart connected consumer devices employing IoT as the backbone are becoming a part of our day-to-day life. These products are fascinating to everyone but bear a dark side of becoming a threat to consumers and vendors. Users and smart devices manufacturers are losing finances as well as confidential data to the adversary, mainly due to employing insecure authentication methods. There are several reported incidences of such security breaches in IoT-enabled systems. The IoT industry finds it difficult to catch up with fast developments and innovations in lightweight protocols, hardware devices, and authentication mechanisms proposed explicitly for IoT-based products. Furthermore, they are reluctant to embed the evolutions, fearing that consumers may not find the new product budget-friendly. Smart cities, smart homes, smart cars and smart grids are gaining attention from various sections of society with no knowledge of the vulnerabilities of these revolutionary systems. The chapter considers IoT systems from the perspectives of a consumer, a vendor, and a researcher to figure out the present scenario, and give future directions to the authentication-related security issues in IoT subsystems. The device-to-user authentication while accessing connected consumer devices is the area in IoT systems that needs serious attention from academia, the biometric community, and the biometric industry. Also, automated payment systems implemented in smart consumer products are at risk of threats from malicious attackers. Even though there are alliances to brainstorm the specific problem and standardization of protocols for IoT infrastructure, there is slow growth in incorporating the most secure and cost-effective solution to security issues in IoT. We must understand the vulnerabilities and loopholes in IoT infrastructure and correctly design mitigation techniques to build a robust system. The chapter provides a precise investigation of the current scenario to integrate biometric authentication in IoT applications and systems along with the required techniques to mitigate software and hardware-level vulnerabilities in these systems. The study classifies IoT systems and applications into seven categories. The chapter reviews the current research outcomes in this direction and pinpoints their pros and cons while implementing them into future IoT products. It is also required to decide the best biometric modality and implementation mechanisms that are convenient and pocket friendly to the consumers. In addition, the chapter also discusses various biometric traits from the IoT perspective and suggests the best modalities for such systems.

The purpose of this paper is to enhance the applicability of the fuzzy sets for developing mathematical models for decision making under uncertainty, In general a decision making process consist of four stages, namely collection of information from various sources, compile the information, execute the information and finally take the decision/action. Only fuzzy sets theory is capable to quantifying the linguistic expression to mathematical form in complex situation. Intuitionistic fuzzy set (IFSs) which reflects the fact that the degree of non membership is not always equal to one minus degree of membership. There may be some degree of hesitation. Thus, there are some situations where IFS theory provides a more meaningful and applicable to cope with imprecise information present for solving multiple criteria decision making problem. This paper emphasis on IFSs, which is help for solving real world problem in uncertainty situation.

Multicriteria Decision-making Method Based on Risk Attitude under Interval-valued Intuitionistic Fuzzy Environment

In this manuscript, nine similarity measures of T-spherical fuzzy set (TSFS) considering the membership degree, the hesitancy degree, the non-membership degree and the refusal degree are developed according to the cosine function. Besides, the generalizations of existing similarity measures are the similarity measures of TSFS proposed in this paper, which indicates the breadth and novelty of the proposed similarity measures. More importantly, the nine similarity measures of TSFSs are applied to pattern recognition. Then, we make a comparative study, that is, we apply the nine similarity measures of TSFSs developed in this manuscript to picture fuzzy environment, and the results obtained are consistent with the previous results. This application make the problem of building material recognition better solved in the real world. Finally, two numerical examples show the validity of the proposed similarity measure between TSFSs.

Fundamental properties of intuitionistic fuzzy calculus

The lack of interoperability among IoT platforms has led to a fragmented environment, where the users and society as a whole suffer from lock-ins, lack of privacy, and reduced functionality. This paper presents SOFIE, a solution for federating the existing IoT platforms in an open and secure manner using Distributed Ledger Technologies (DLTs) and without requiring modifications to the IoT platforms, and describes how SOFIE is used to enable two complex real life pilots: food supply chain tracking from field to fork and electricity distribution grid balancing with guided electrical vehicle (EV) charging. SOFIE's main contribution is to provide interoperability between IoT systems while also enabling new functionality and business models

The intuitionistic fuzzy preference relation (IFPR), whose elements are intuitionistic fuzzy values (IFVs), is more powerful than the traditional multiplicative preference relation and the fuzzy preference relation in expressing comprehensive preference information of a decision maker. The aim of this paper is to investigate a new approach to derive the priority weights from an IFPR. To do so, we give a new definition of multiplicative consistent IFPR, which is based on the membership and nonmembership degrees of the intuitionistic fuzzy judgments. After that, a formula, which involves the underlying intuitionistic fuzzy weights of the IFPR, is proposed to construct such a multiplicative consistent IFPR. Based on the formula, some fractional programming models are built to generate the intuitionistic fuzzy priority weighting vector of the IFPR. Several numerical examples are given to illustrate the validity and applicability of the proposed method.

L.A.Zadeh introduced the concept of fuzzy set theory as the generalization of classical set theory in 1965 and further it has been generalized to intuitionistic fuzzy sets (IFSs) by Atanassov in 1983 to model information by the membership, nonmembership and hesitancy degree more accurately than the theory of fuzzy logic. The notions of intuitionistic fuzzy numbers in different contexts were studied in literature and applied in real life applications. Problems in different fields involving qualitative, quantitative and uncertain information can be modeled better using intutionistic fuzzy numbers introduced in [17] which generalizes intuitionistic fuzzy values [1,7,17], interval valued intuitionistic fuzzy number (IVIFN) [10] than with usual IFNs [5,11,21]. Ranking of fuzzy numbers have started in early seventies in the last century and a complete ranking on the class of fuzzy numbers have achieved by W.Wang and Z.Wang only on 2014. A complete ranking on the class of IVIFNs, using axiomatic set of membership, non membership, vague and precise score functions has been introduced and studied by Geetha et al.[10]. In this paper, a total ordering on the class of IFNs [17] using double upper dense sequence in the interval [0; 1] which generalizes the total ordering on fuzzy numbers (FNs) is proposed and illustrated with examples. Examples are given to show the proposed method on this type of IFN is better than existing methods and this paper will give the better understanding over this new type of IFNs.

Operations of intuitionistic fuzzy values have been widely studied and have attracted significant interest. In this paper, some other operations on intuitionistic fuzzy values on the basis of Archimedean copulas and corresponding co-copulas are introduced. Such novel operations can show the relevance between intuitionistic fuzzy values. A family of weighted aggregation operators are developed according to the proposed operations, i.e., the intuitionistic fuzzy copula aggregation operator. The properties of the novel operations and the weighted aggregation operators are also considered. In the end, we provide a modified maximizing deviation decision procedure for multiple attributes decision making under intuitionistic fuzzy environment, and show a case study to illustrate the application of the proposed approach.

The Internet of Things is expected to radically reshape many processes in a broad range of domains, from personal to industrial. In many of these heterogeneous scenarios, IoT systems will need to guarantee required levels of reliability and latency in order to provide high-quality services to end users. Quality of service support in IoT system will demand for explicit support at different levels. At the network level, on the one hand, specific technical communication standards will be necessary to ensure timed and reliable data delivery. At the application level, instead, dedicated support from application protocols and design of novel resource allocation algorithms will be mandatory to cope with concurrent access and implement proper management of resources. In this chapter, an overview of the current solutions for ensuring QoS in IoT systems is provided. Specifically, we first survey the current approaches at the network level through a summary of all the mechanisms included in the main communication standards for IoT. We then deliver an analysis of the current solutions available in IoT protocols and platforms to enforce QoS at the application level.