DISTRIBUTED MULTI-HOP ROUTING ALGORITHM FOR WIRELESS SENSOR NETWORKS

In a Wireless Sensor Network (WSN), routing is the process of finding a cost-effective route in terms of power consumption. As an evaluation criterion for the WSN performance, network lifetime is directly affected by the routing method. In a wide variety of WSNs, different techniques are used as routing methods, such as shortest distance path. In this paper, we propose a novel algorithm, optimizing power consumption in WSN nodes, based on the shortest path algorithm. In this approach, the energy level of nodes and their geographical distance from each other contribute to the weight of the connecting path. The proposed algorithm is used as a data dissemination method in WSNs with randomly scattered nodes. We also apply Dijkstra’s shortest path algorithm to the same networks. The results showed that the proposed algorithm increases the network lifetime up to 30 % by preventing nodes with low charge levels from early disconnection

A NEW ANOMALOUS TEXT DETECTION APPROACH USING UNSUPERVISED METHODS

Increasing size of text data in databases requires appropriate classification and analysis in order to acquire knowledge and improve the quality of decision-making in organizations. The process of discovering the hidden patterns in the data set, called data mining, requires access to quality data in order to receive a valid response from the system. Detecting and removing anomalous data is one of the pre-processing steps and cleaning data in this process. Methods for anomalous data detection are generally classified into three groups including supervised, semi-supervised, and unsupervised. This research tried to offer an unsupervised approach for spotting the anomalous data in text collections. In the proposed method, a combination of two approaches (i.e., clustering-based and distance-based) is used for detecting anomaly in the text data. In order to evaluate the efficiency of the proposed approach, this method is applied on four labeled data sets. The accuracy of Na¨ıve Bayes classification algorithms and decision tree are compared before and after removal of anomalous data with the proposed method and some other methods such as Density-based spatial clustering of applications with noise (DBSCAN). Our proposed method shows that accuracy of more than 92.39% can be achieved. In general, the results revealed that in most cases the proposed method has a good performance

Pairwise document similarity measure based on present term set

Abstract Measuring pairwise document similarity is an essential operation in various text mining tasks. Most of the similarity measures judge the similarity between two documents based on the term weights and the information content that two documents share in common. However, they are insufficient when there exist several documents with an identical degree of similarity to a particular document. This paper introduces a novel text document similarity measure based on the term weights and the number of terms appeared in at least one of the two documents. The effectiveness of our measure is evaluated on two real-world document collections for a variety of text mining tasks, such as text document classification, clustering, and near-duplicates detection. The performance of our measure is compared with that of some popular measures. The experimental results showed that our proposed similarity measure yields more accurate results

Energy based analytical modelling of ANCH clustering algorithm for wireless sensor networks

Abstract—Wireless Sensor Networks (WSNs) have had re-markable advances in the past couple of decades due to their fast growth and flexibility. In order to supervise an area, hundreds or thousands of sensors can be established and collaborate with each other in the environment. The sensors ’ sensed and collected data can be delivered to the base station. Energy optimisation is crucial in WSN’s efficiency. Organising sensor nodes into small clusters helps save their initial energy and thus increases their lifetime. Also, the number and distribution of Cluster Heads (CHs) are fundamental for energy saving and flexibility of clustering methods. Avoid Near Cluster Heads (ANCH) is one of the most recent energy-efficient clustering algorithms proposed for WSNs in order to extend their lifetime by uniform distributing of CHs through the network area. In this manuscript, we suggest an analytical approach to model the energy consumption of the ANCH algorithm. The results of our comprehensive research show a 95.4 % to 98.6 % accuracy in energy consumption estima-tion using the proposed analytical model under different practical situations. The suggested analytical model gives a number of indications concerning the impact of different factors on the energy depletion pattern of the ANCH clustering algorithm

Analytical Modelling of ANCH Clustering Algorithm for WSNs

In this thesis, I am critically assessing contemporary environmental discourse at the governmental level in China. Taking the perspective of the Copenhagen School’s securitization theory, this thesis dwells on the question of how the Chinese government might attempt to use discourse in official policies in order to securitize the question of climate change. To answer this question, I have collected policy documents from three parts of the central government – the Ministry of Ecology and Environment, the State Council, and the National Development and Reform Commission as well as speeches from President Xi Jinping. In this thesis I seek not only to discover how climate change is represented by the Chinese government, but also who or what they believe the referent object ought to be. This thesis also aims at critically describing, interpreting, and explaining the ways in which these discourses might construct, maintain, and legitimize social inequalities. The findings imply that the government has attempted to securitize the issue of climate change for controlling the Chinese society and further consolidating their power. The Communist Party of China (CPC) utilized security speech acts which represented climate change as a threat to natural resources, the environment, and human health which served as proxies for the de facto referent object – which was interpreted to be the development process and the legitimacy of the CPC

Using machine learning and computer vision to estimate the angular velocity of wind turbines in smart grids remotely

Today, power generation from clean and renewable resources such as wind and solar is of great salience. Smart grid technology efficiently responds to the increasing demand for electric power. Intelligent monitoring, control, and maintenance of wind energy facilities are indispensable to increase the performance and efficiency of smart grids (SGs). Integration of state-of-the-art machine learning algorithms and vision sensor networks approaches pave the way toward enhancing the wind farms performance. The generating power in a wind turbine farm is the most critical parameter that should be measured accurately. Produced power is highly related to weather patterns, and a new farm in a near area is also likely to have similar energy generation. Therefore, accurate and perpetual prediction models of the existing wind farms can be led to develop new stations with lower costs. The paper aims to estimate the angular velocity of turbine blades using vision sensors and signal processing. The high wind in the wind farm can cause the camera to vibrate in successive frames, and the noise in the input images can also strengthen the problem. Thanks to couples of solid computer vision algorithms, including FAST (Features from Accelerated Segment Test), SIFT (Scale-Invariant Feature Transform), SURF (Speeded Up Robust Features), BF (Brute-Force), FLANN (Fast Library for Approximate Nearest Neighbors), AE (Autoencoder), and SVM (support vector machines), this paper accurately localizes the Hub and track the presence of the Blade in consecutive frames of a video stream. The simulation results show that determining the hub location and the blade presence in sequential frames results in an accurate estimation of wind turbine angular velocity with 95.36% accuracy. (C) 2021 The Authors. Published by Elsevier Ltd