The Delay Measurement and Analysis of Unreachable Hosts of Internet

Delay related metrics are significant quality of service criteria for the performance evaluation of networks. Almost all delay related measurement and analysis studies take into consideration the reachable sources of Internet. However, unreachable sources might also shed light upon some problems such as worm propagation. In this study, we carry out a delay measurement study of unreachable destinations and analyse the delay dynamics of unreachable nodes. 2. Internet Control Message Protocol (ICMP) destination unreachable Internet Control Message Protocol-Destination Unreachable (ICMP T3) packets are considered for the delay measurement according to their code types which shows network un reach ability, host un reach ability, port un reach ability, etc. Measurement results show that unreachable sources exhibit totally different delay behaviour compared to reachable IP hosts. A significant part of the unreachable hosts experiences extra 3 seconds Round Trip Time (RTT) delay compared to accessible hosts mostly due to host un reach ability. It is also seen that, approximately 79% of destination un reach ability causes from host un reach ability. Obtained Hurst parameter estimation results reveal that unreachable host RTTs show lower Hurst degree compared to reachable hosts which is approximately a random behaviour. Unreachable sources exhibit totally different distributional characteristic compared to accessible ones which is best fitted with Phased Bi-Exponential distribution.

GPS Receiver Position Augmentation Using Correntropy Kalman Filter in Low Latitude Terrain

If any Global Positioning System (GPS) receiver is operated in low latitude regions or urban canyons, the visibility further reduces. These system constraints lead to many challenges in providing precise GPS position accuracy over the Indian subcontinent. As a result, the standalone GPS accuracy does not meet the aircraft landing requirements, such as Category I (CAT-I) Precision Approaches. However, the required accuracy can be achieved by augmenting the GPS. Among all these issues, the predominant factors that significantly influence the receiver position accuracy are selecting a user/receiver position estimation algorithm. In this article, a novel method is proposed based on correntropy and designated as Correntropy Kalman Filter (CKF) for precise GPS applications and GPS Aided Geosynchronous equatorial orbit Augmented Navigation (GAGAN) based aircraft landings over the low latitude Indian subcontinent. The real-world GPS data collected from a dual-frequency GPS receiver located in the southern region of the Indian subcontinent (IISc), Bangalore with Lat/Long: 13.021°N/ 77.5°E) is used for the performance evaluation of the proposed algorithm. Results prove that the proposed CKF algorithm exhibits significant improvement (up to 34%) in position estimation compared to the traditional Kalman Filter.

Systematic Literature Review: Causes of Rework in GSD

Global Software Development (GSD) involves multiple sites which comprise of different cultures and time zones apart from geographical locations. It is a common software development approach adopted to achieve competitiveness. However, due to multiple challenges it can result in misunderstandings and rework. Rework raises the chance of project failure by delaying the project and increasing the estimated budget. The aim of this study is to identify and categorize the rework causes to reduce its frequency in GSD. To identify the empirical literature related to causes of rework, we performed a Systematic Literature Review (SLR). A total of 23 studies are included as a result of final inclusion. The empirical literature from the year 2009 to 2020 is searched. The overall identified causes of rework in GSD are categorized into 6 major categories which are communication, Requirement Management (RM), roles of stakeholders, product development/integration issues, documentation issues, and differences among stakeholders. The most reported rework causes are related to the category of communication & coordination and RM. Moreover, an industrial survey is conducted to validate the identified rework causes and their mitigation practices from practitioners. This study will help practitioners and researchers in addressing the identified causes and therefore reduce the chances of rework.

Energy Heterogeneity Analysis of Heterogeneous Clustering Protocols

In Wireless Sensor Networks the nodes have restricted battery power and the exhaustion of battery depends on various issues. In recent developments, various clustering protocols have been proposed to diminish the energy depletion of the node and prolong the network lifespan by reducing power consumption. However, each protocol is inappropriate for heterogeneous wireless sensor networks. The efficiency of heterogeneous wireless sensor networks declines as changing the node heterogeneity. This paper reviews cluster head selection criteria of various clustering protocols for heterogeneous wireless sensor networks in terms of node heterogeneity and compares the performance of these protocols on several parameters like clustering technique, cluster head selection criteria, nodes lifetime, energy efficiency under two-level and three-level heterogeneous wireless sensor networks protocols Stable Election Protocol (SEP), Zonal-Stable Election Protocol (ZSEP), Distributed Energy-Efficient Clustering (DEEC), A Direct Transmission And Residual Energy Based Stable Election Protocol (DTRE-SEP), Developed Distributed Energy-Efficient Clustering (DDEEC), Zone-Based Heterogeneous Clustering Protocol (ZBHCP), Enhanced Distributed Energy Efficient Clustering (EDEEC), Threshold Distributed Energy Efficient Clustering (TDEEC), Enhanced Stable Election Protocol (SEP-E), and Threshold Stable Election Protocol (TSEP). The comparison has shown that the TDEEC has very effective results over other over two-level and three-level heterogeneous wireless sensor networks protocols and has extended the unstable region significantly. From simulations, it can also be proved that adding node heterogeneity can significantly increase the network life.

Designing an Intelligent System to Detect Stress Levels During Driving

In addition to the devastating effects of anxiety and stress on the development and exacerbation of the cardiovascular disease, lack of stress control increases drivers' risk of accidents. This paper aims to identify the stress of drivers in various driving situations to warn the driver to control the tense conditions during driving. In order to detect stress while driving, we used the heart signals in the Physionet database. To analyze the conditions of the electrocardiogram (ECG) under various driving situations, linear and non-linear features were used. The characteristics of the RRIs are the only able to identify driver stress in different driving modes relative to rest periods, while the return mapping features, in addition to identifying driver stress while resting, have the ability to identify stress between different driving positions also brought. The results showed that driver's stress level during driving in city 1 and highway 1 with a P-value of 0.028 and also in city 3 and highway 2 with a P-value of 0.041 can be distinguished. The accuracy obtained from the proposed detection method is 98±2% for 100 iterations. The result indicated an efficiency of our proposed method and enhanced the reliability.

A Modified DBSCAN Algorithm for Anomaly Detection in Time-series Data with Seasonality

Anomaly detection concerns identifying anomalous observations or patterns that are a deviation from the dataset's expected behaviour. The detection of anomalies has significant and practical applications in several industrial domains such as public health, finance, Information Technology (IT), security, medical, energy, and climate studies. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) Algorithm is a density-based clustering algorithm with the capability of identifying anomalous data. In this paper, a modified DBSCAN algorithm is proposed for anomaly detection in time-series data with seasonality. For experimental evaluation, a monthly temperature dataset was employed and the analysis set forth the advantages of the modified DBSCAN over the standard DBSCAN algorithm for the seasonal datasets. From the result analysis, we may conclude that DBSCAN is used for finding the anomalies in a dataset but fails to find local anomalies in seasonal data. The proposed Modified DBSCAN approach helps to find both the global and local anomalies from the seasonal data. Using normal DBSCAN we are able to get 19 (2.16%) anomaly points. While using the modified approach for DBSCAN we are able to get 42 (4.79%) anomaly points. In comparison we can say that we are able to get 2.11% more anomalies using the modified DBSCAN approach. Hence, the proposed Modified DBSCAN algorithm outperforms in comparison with the DBSCAN algorithm to find local anomalies.

Voice Versus Keyboard and Mouse for Text Creation on Arabic User Interfaces

Voice User Interfaces (VUIs) are increasingly popular owing to improvements in automatic speech recognition. However, the understanding of user interaction with VUIs, particularly Arabic VUIs, remains limited. Hence, this research compared user performance, learnability, and satisfaction when using voice and keyboard-and-mouse input modalities for text creation on Arabic user interfaces. A Voice-enabled Email Interface (VEI) and a Traditional Email Interface (TEI) were developed. Forty participants attempted pre-prepared and self-generated message creation tasks using voice on the VEI, and the keyboard-and-mouse modal on the TEI. The results showed that participants were faster (by 1.76 to 2.67 minutes) in pre-prepared message creation using voice than using the keyboard and mouse. Participants were also faster (by 1.72 to 2.49 minutes) in self-generated message creation using voice than using the keyboard and mouse. Although the learning curves were more efficient with the VEI, more participants were satisfied with the TEI. With the VEI, participants reported problems, such as misrecognitions and misspellings, but were satisfied about the visibility of possible executable commands and about the overall accuracy of voice recognition.

Optimal Image Based Information Hiding with One-dimensional Chaotic Systems and Dynamic Programming

Information hiding is a technology aimed at the secure hiding of important information into digital documents or media. In this paper, a new approach is proposed for the secure hiding of information into gray scale images. The hiding is performed in two stages. In the first stage, the binary bits in the sequence of information are shuffled and encoded with a set of integer keys and a system of one-dimensional logistic mappings. In the second stage, the resulting sequence is embedded into the gray values of selected pixels in the given image. A dynamic programming method is utilized to select the pixels that minimize the difference between a cover image and the corresponding stego image. Experiments show that this approach outperforms other information hiding methods by 13.1% in Peak Signal to Noise Ratio (PSNR) on average and reduces the difference between a stego image and its cover image to 0 in some cases.

Sørensen-Dice Similarity Indexing based Weighted Iterative Clustering for Big Data Analytics

Big data is a collection of large volume of data and extract similar data points from large dataset. Clustering is an essential data mining technique for examining large volume of data. Several techniques have been developed for handling big dataset. However, with much time consumption and space complexity, accuracy is said to be compromised. In order to improve clustering accuracy with less complexity, Sørensen-Dice Indexing based Weighted Iterative X-means Clustering (SDI-WIXC) technique is introduced. SDI-WIXC technique is used for grouping the similar data points with higher clustering accuracy and minimal time. First, number of data points is collected from big dataset. Then, along with the weight value, the given dataset is partitioned into ‘X’ number of clusters. Next, based on the similarity measure, Weighted Iterated X-means Clustering (WIXC) is applied for clustering data points. Sørensen-Dice Indexing Process is used for measuring similarity between cluster weight value and data points. Upon similarity found between weight value of cluster and data point, data points are grouped into a specific cluster. Besides, the WIXC method also improves the cluster assignments through repeated subdivision using Bayesian probability criterion. This in turn helps to group all data points and hence, improving the clustering accuracy. Experimental evaluation is carried out with number of factors such as clustering accuracy, clustering time and space complexity with respect to the number of data points. The experimental results reported that the proposed SDI-WIXC technique obtains high clustering accuracy with minimum time as well as space complexity.

Ensemble based on Accuracy and Diversity Weighting for Evolving Data Streams

Ensemble classification is an actively researched paradigm that has received much attention due to increasing real-world applications. The crucial issue of ensemble learning is to construct a pool of base classifiers with accuracy and diversity. In this paper, unlike conventional data-streams oriented ensemble methods, we propose a novel Measure via both Accuracy and Diversity (MAD) instead of one of them to supervise ensemble learning. Based on MAD, a novel online ensemble method called Accuracy and Diversity weighted Ensemble (ADE) effectively handles concept drift in data streams. ADE mainly uses the following three steps to construct a concept-drift oriented ensemble: for the current data window, 1) a new base classifier is constructed based on the current concept when drift detect, 2) MAD is used to measure the performance of ensemble members, and 3) a newly built classifier replaces the worst base classifier. If the newly constructed classifier is the worst one, the replacement has not occurred. Comparing with the state-of-art algorithms, ADE exceeds the current best-related algorithm by 2.38% in average classification accuracy. Experimental results show that the proposed method can effectively adapt to different types of drifts.