INTEGRATED SOLUTION SCHEME FOR HANDOVER LATENCY DIMINUTION IN PROXY MOBILE IPV6

ABSTRAC

An IP-based seamless handover communication architecture for high speed mobile host

With the rapid developments and convergence in the communication and personal computing technology fields, the overall quality of ubiquitous communication has improved significantly. This is especially true with the invention of portable mobile devices that can be connected almost everywhere at any time. However, the recent explosion on the usage of mobile devices, it has also generated several issues in terms of performance and quality of service. Nowadays, mobile users demand high quality performance, best quality of services and seamless connections that support real-time applications such as audio and video streaming. Therefore, in recent years, Network-based Localized Mobility Management working group has proposed a protocol that is actively standardized by the Internet Engineering Task Force which is called Proxy Mobile IPv6 (PMIPv6). PMIPv6 is an effective mobility management protocol for next generation wireless networks that has salient features and is expected to expedite the real deployment of IP mobility management. It has also attracted much attention among the telecommunications and Internet communities. Even though PMIPv6 offers many advantages, it still suffers from lengthy handover latency and huge packet loss. The motivation of this research is to reduce the handover latency and packet loss of mobile host/client during the high speed movement of mobile host. This thesis paper presents a new approach which is a network-based mobility management protocol that aims to reduce the lengthy handover latency, jitter, high packet loss, and increase throughput and the performance of video transmission during the high speed mobility. The proposed approach as a seamless handover integrated solution includes mobility prediction method, a set of Internet Control Message Protocol version 6 messages and effective handover optimization

Impact of the Mobility Management Protocols on the Handover Performance Assessment Under Real-Time Video Steaming Network

In recent years, wireless networks have evolved tremendously especially in heterogeneous systems in order to effectively and efficiently support multiple access technologies. Heterogeneous networks will be paramount in the next generation wireless networks where devices are able to roam under various radio access technologies that lead to utilization of an IP-based core network. With the increasing accessibility and performance of different radio access technologies which are WiMAX (Worldwide Interoperability for Microwave Access), Wi-Fi (Wireless Fidelity) and UMTS (Universal Mobile Telecommunications System) have stimulated the mobile users towards increased of utilization. In such network, it is imperative for handovers between networks in order to maintain the seamless connectivity and a satisfactory quality of service for mobile users. Therefore, providing the quality of service (QoS) and seamless handover processes is one of the major matters for real-time services. This paper proposes a unified handover scheme to reduce the latency period of vertical handover during the video transmission. The proposed scheme is evaluated based simulation work on the handover performance under real-time video streaming networks

Analyzing the Classification Accuracy of Deep Learning and Machine Learning for Credit Card Fraud Detection

The purpose of this study is to classify a dataset of credit card security problems by employing six different machine learning (ML) approaches. The Support Vector Machine (SVM), Random Forest (RF), Bagged Tree, K-Nearest Neighbor (KNN), Naive Biased Classifier, and Extreme Gradient Boosting were selected as the classifiers to use (XGBoost). The classification accuracy of the machine learning algorithms was compared with that of a technique for categorization that is based on deep learning called Long Short-Term Memory (LSTM). The KNN machine learning approach had a maximum accuracy of 97.50 percent, while the LSTM machine learning method had an accuracy of more than 96 percent and promised to give biologically appropriate control of upper-limb movement. In addition to enhancing accuracy, the research has investigated how the effects of removing the channel with the most noise from the algorithms can have on accuracy. This was done in an effort to handle data in a more effective manner

Data of vertical and horizontal handover on video transmission in Proxy Mobile IPv6

The Internet Engineering Task Force provides a network-based mobility management solution to execute handover in heterogeneous networks on network-side called Proxy Mobile IPv6 (PMIPv6). In this data article, data are presented during the horizontal and vertical handover on video communication in PMIPv6 mobility protocols. The handover data are gathered using several measurement factors, which are latency, jitter, cumulative measured, and peak signal noise ratio under network simulation software, for both horizontal and vertical handover

A smart device of data acquisition with emergency safety features for laboratory furnaces

Laboratory furnaces or ovens are found in scientific, forensic, medicinal, and material processing labs. They provide precise temperature control and homogenous heating for samples in industrial and research applications but cannot measure the sample's interior temperature and most of them do not have the facility to store temperature data. Moreover, a furnace cannot promptly shut off if a sample or the furnace catches fire due to overheating or an electrical failure. To solve these problems, in this work, an intelligent, automated add-on device with wireless monitoring and emergency safety features to use with any commercial furnace or oven is presented. The device can individually measure furnace and sample temperatures at the same time using a two-channel K-type thermocouple unit. The system can store the real-time temperature data generated by the thermocouples, display it on LCD (Liquid Crystal Display) and give the facility to observe this real-time data from anywhere within an area of 800–1000-m radius using the portable wireless monitor device without the need for Wi-Fi or internet. Additionally, the device can quickly detect fire or overheating, immediately cuts the power automatically, activates a loud alarm and dials the user's phone itself using Global System for Mobile Communication (GSM) technology to warn them

Elucidation of anti-hyperglycemic activity of Psidium guajava L. leaves extract on streptozotocin induced neonatal diabetic Long-Evans rats

Background: Psidium guajava L (Guava) belongs to the Myrtaceae family and has been claimed to possess several pharmacological properties including antidiabetic. Objective: This study was designed to evaluate the anti-hyperglycemic activity of P guajava L leaves aqueous extract on neonatal streptozotocin-induced type 2 diabetic model rats. Methods: Streptozotocin was induced (90 mg/kg) intraperitoneally to 48 h old Long Evans rat pups. After three months, 18 male type-2 diabetic model rats were confirmed by OGTT (FG > 7 mmol/L). Therefore, experimental rats were divided into three groups 2) Diabetic water control (10 ml/kg), 3) Gliclazide treated (20 mg/kg), and 4) Extract treated group (1.25g/kg)] Six normal female rats comprised group 1 [Non-diabetic water control (10 ml/kg)]. All rats were treated orally with their respective treatment for 28 consecutive days. Blood samples were collected on 0 days (by tail cut method) and the end day (by cardiac puncture) of the experiment. The anti-hyperglycemic activity was evaluated by measuring fasting glucose, serum insulin, lipid profile, hepatic glycogen content, and intestinal glucose absorption by standard methods. Results: The serum glucose level of extract treated group was decreased by 16% as well as significantly (p<0.05) increased the serum insulin level (M±SD, 0 day vs 28thday; 0.319 ± 0.110 vs 0.600 ± 0.348, μg/L). Moreover, the extract-treated group also significantly (p<0.05) enhanced liver glycogen content and inhibited glucose absorption from the upper intestine. Besides, a significant (p < 0.05) reduction of LDL-cholesterol level was found in the extract-treated group (M±SD, 55 ± 33 vs 14 ± 9, mg/dl) compared with baseline values where other groups did not show any statistically remarkable changes. Conclusion: Current study concludes that P guajava leaves aqueous extract enhances insulin secretion from pancreatic beta-cells and promotes glycogen synthesis in the liver. The extract also inhibits glucose absorption from the upper intestine and improves dyslipidemia to some extent. Therefore, possesses the potential for drug development against T2DM

A Comparative Study, Prediction and Development of Chronic Kidney Disease Using Machine Learning on Patients Clinical Records

Abstract Chronic Kidney Disease (CKD) has become a major problem in modern times, and it is dubbed the silent assassin due to its delayed signs. To overcome these critical issues, early identification may minimize the prevalence of chronic diseases, though it is quite difficult because of different kinds of limitations in the dataset. The novelty of our study is that we extracted the best features from the dataset in order to provide the best classification models for diagnosing patients with chronic kidney disease. In our study, we used CKD patients’ clinical datasets to predict CKD using some popular machine learning algorithms. After handling missing values, K-means clustering has been performed. Then feature selection was done by applying the XGBoost feature selection algorithm. After selecting features from our dataset, we have used a variety of machine learning models to determine the best classification models, including Neural Network (NN), Random Forest (RF), Support Vector Machine (SVM), Random Tree (RT), and Bagging Tree Model (BTM). Accuracy, Sensitivity, Specificity, and Kappa values were used to evaluate model performance