Measuring Connectivity Tolerance in Wireless Sensor Networks using Graph Theory Applications: A Fast Algorithm

Abstract — Wireless sensor networks today has been attracted many diverse areas in both academic and business domains because of their facility and applicability, low deployment cost and other factors. These networks aside from challenges that traditional networks have, face new challenges. These challenges can be tackled from other fields and by many tools. Since these networks can be seen from graph theory perspective as an abstract graph where sensors become nodes and links become edges in the graph, graph theory applications can be used to analyze and tackled some challenges in these networks. In this paper we first propose an exhaustive algorithm for measuring connectivity tolerance in WSNs then, since WSNs have a dynamic structure, we proposed a fast algorithm that in worst case has time complexity O(nlogn) and O(n) in normal case for measuring connectivity tolerance. Beside this parameters these algorithms can produce special data that is called meta-data, this meta-data can be used for other routing protocols or mechanisms in networks such that they do not need to run graph algorithm again, just need to operate on meta-data to obtain desire parameters. The organization of this paper is as follow, first we review the works that have been done common in both graph theory and wireless sensor networks, and in last we propose an fast algorithm for calculating connectivity tolerance for two arbitrary sensors in wireless sensor network due sensor corruption or link loss with the use of graph theory and graph mining techniques. This algorithm will test on most used sensors deployments, three sensors deployments namely, Uniform, Normal and Random distributions, then the results and conclusion will present according to these distributions. IJSE

A comprehensive study on Frequent Pattern Mining and Clustering categories for topic detection in Persian text stream

Topic detection is a complex process and depends on language because it somehow needs to analyze text. There have been few studies on topic detection in Persian, and the existing algorithms are not remarkable. Therefore, we aimed to study topic detection in Persian. The objectives of this study are: 1) to conduct an extensive study on the best algorithms for topic detection, 2) to identify necessary adaptations to make these algorithms suitable for the Persian language, and 3) to evaluate their performance on Persian social network texts. To achieve these objectives, we have formulated two research questions: First, considering the lack of research in Persian, what modifications should be made to existing frameworks, especially those developed in English, to make them compatible with Persian? Second, how do these algorithms perform, and which one is superior? There are various topic detection methods that can be categorized into different categories. Frequent pattern and clustering are selected for this research, and a hybrid of both is proposed as a new category. Then, ten methods from these three categories are selected. All of them are re-implemented from scratch, changed, and adapted with Persian. These ten methods encompass different types of topic detection methods and have shown good performance in English. The text of Persian social network posts is used as the dataset. Additionally, a new multiclass evaluation criterion, called FS, is used in this paper for the first time in the field of topic detection. Approximately 1.4 billion tokens are processed during experiments. The results indicate that if we are searching for keyword-topics that are easily understandable by humans, the hybrid category is better. However, if the aim is to cluster posts for further analysis, the frequent pattern category is more suitable