Study on Evaluating Wireless Sensor Network Security Based on Uncertain Linguistic Information

Wireless sensor network (WSN), as an integrated network which can perform information gathering, processing and delivering, can connect the real world and logistic information world. It is greatly changing the interaction between people and nature. There are wide potential applications for wireless sensor network, such as industry, agriculture, military affairs, environment monitoring, biomedicine, city managing and disaster succoring. The problem of evaluating security of Wireless Sensor Network (WSN) with uncertain linguistic information is the multiple attribute group decision making (MAGDM). In this paper, we investigate the multiple attribute group decision making (MAGDM) problems for evaluating the wireless sensor network (WSN) security with uncertain linguistic information. We utilize the uncertain linguistic weighted averaging (ULWA) operator to aggregate the uncertain linguistic information corresponding to each alternative and get the overall value of the alternatives, then rank the alternatives and select the most desirable one(s) by using the formula of the degree of possibility for the comparison between two uncertain linguistic variables. Finally, an illustrative example is given

A Linguistic Recommender System For University Digital Libraries To Help Users In Their Research Resources Accesses

The Web is one of the most important information media and it is influencing in the development of other media, as for example, newspapers, journals, books, libraries, etc. Moreover, in recent days people want to communicate and collaborate. So, libraries must develop services for connecting people together in information environments. Then, the library staff needs automatic techniques to facilitate that a great number of users can access to a great number of resources. Recommender systems are tools whose objective is to evaluate and filter the great amount of information available on the Web. We present a model of a fuzzy linguistic recommender system to help University Digital Library users in their research resources accesses. This system recommends researchers specialized and complementary resources in order to discover collaboration possibilities to form multi-disciplinaryy groups. In this way, this system increases social collaboration possibilities in a university framework and contributes to improve the services provided by a University Digital Library

Aggregation Weights for Linguistic Hybrid Geometric Averaging Operator

This paper tries to point out that the aggregation weights in linguistic hybrid geometric averaging operator will dominate the final result of the ranking for alternatives. We examined the linguistic hybrid geometric averaging operator that was proposed by previous studies and found it contained several questionable results. The major defect of the previous approach was that it failed to demonstrate two core factors: accuracy and speed, both of which have been explicitly uncovered and discussed in the study. With previous work the pivotal and dominant element, distribution of weights, in finding subjectively by decision maker of linguistic hybrid geometric averaging operators for group decision-making problems, lacks solid foundation and is unjustified. Here we provide the mathematical rationale and reliable advices, to point out that deficiency. In addition, we have detected and rectified some redundancies of operational laws in the procedure of previous study due to the improper utilization of negative operators. It certainly should be noted that the careless applications of those highly dependant operators may significantly diminish the efficiency and performance of entire mechanism for decision making under fuzzy environment. We develop an easy aggregation approach based on the arithmetic mean to solve the most favorable alternative problem. A comprehensive numerical examination of 1296 tests supports our result

Induced aggregation operators in decision making with the Dempster-Shafer belief structure

We study the induced aggregation operators. The analysis begins with a revision of some basic concepts such as the induced ordered weighted averaging (IOWA) operator and the induced ordered weighted geometric (IOWG) operator. We then analyze the problem of decision making with Dempster-Shafer theory of evidence. We suggest the use of induced aggregation operators in decision making with Dempster-Shafer theory. We focus on the aggregation step and examine some of its main properties, including the distinction between descending and ascending orders and different families of induced operators. Finally, we present an illustrative example in which the results obtained using different types of aggregation operators can be seen.aggregation operators, dempster-shafer belief structure, uncertainty, iowa operator, decision making

Choquet integrals of weighted triangular fuzzy linguistic information and their applications to multiple attribute decision making

We investigate the multiple attribute decision making problems in which attribute values take the form of triangular fuzzy linguistic information. Firstly, the definition and some operational laws of triangular fuzzy linguistic are introduced. Then, we have developed three fuzzy linguistic Choquet integral aggregation operators: fuzzy linguistic choquet ordered averaging operator, fuzzy linguistic choquet ordered geometric operator and fuzzy linguistic choquet ordered harmonic mean operator. The prominent characteristic of the operators is that they cannot only consider the importance of the elements or their ordered positions, but also reflect the correlation among the elements or their ordered positions. We have studied some desirable properties of these operators, such as commutativity, idempotency and monotonicity, and applied these operators to multiple attribute decision making with triangular fuzzy linguistic information. Finally an illustrative example has been given to show the developed method

A Multiple Attribute Decision Making Method Based on Uncertain Linguistic Heronian Mean

The Heronian mean is a useful aggregation operator which can capture the interrelationship of the input arguments. In this paper, we develop some Heronian means based on uncertain linguistic variables, such as the generalized uncertain linguistic Heronian mean (GULHM) and uncertain linguistic geometric Heronian mean (ULGHM), and some of their desirable properties are also investigated. Considering the different importance of the input arguments, we define the generalized uncertain linguistic weighted Heronian mean (GULWHM) and uncertain linguistic weighted geometric Heronian mean (ULWGHM). Then, a method of multiple attribute decision making under uncertain linguistic environment is presented based on the GULWHM or the ULWGHM. In the end, an example is given to demonstrate the effectiveness and feasibility of the proposed method

Semantic discovery and reuse of business process patterns

Patterns currently play an important role in modern information systems (IS) development and their use has mainly been restricted to the design and implementation phases of the development lifecycle. Given the increasing significance of business modelling in IS development, patterns have the potential of providing a viable solution for promoting reusability of recurrent generalized models in the very early stages of development. As a statement of research-in-progress this paper focuses on business process patterns and proposes an initial methodological framework for the discovery and reuse of business process patterns within the IS development lifecycle. The framework borrows ideas from the domain engineering literature and proposes the use of semantics to drive both the discovery of patterns as well as their reuse