scholarly journals Enhancing the Security of Cloud Computing via Unscented Kalman Filter; with Statistical Analysis About Power Consumption

2021 ◽  
Vol 15 ◽  
pp. 103-110
Author(s):  
Mohamadreza Mohamadzadeh
Keyword(s):  
Cloud Computing ◽  
Kalman Filter ◽  
Mathematical Analysis ◽  
Unscented Kalman Filter ◽  
Prediction Algorithm ◽  
Know How ◽  
Advantages And Disadvantages ◽  
Estimation And Prediction ◽  
Special Part ◽  
New Generation

To migrate to cloud computing or other new generation of computational and communicational network we should making some laws about limitations on such networks. We should clarify clearly the level of accessions to resources and/or the ability to sending or receiving files through such network. We can avoid unpredicted and undesired works by using of law makings. Furthermore, besides of law making we can use evolutionary and/or intelligence techniques for estimation and prediction about parameters that deal with controlling of our network – for example in this paper we introduce one type of such intelligence algorithms which named as unscented Kalman filter. By giving some raw inputs about treatments and resources of such networks we’re able to estimate and predict about lots of different conditions of these networks. For example, we should have an estimation and prediction algorithm to be able to track and trace the hackers or crackers, if they’re permeating to our network. Or we should estimate the amount of users that will use particular software or application on a specific hour through such network. Also, if we study users treatment on a specific network, like cloud computing, we’re able to estimate and predict their keen and eagerness about new released software or application; because if we know how people treat and face and react with new emerging software we’re able to construct and program our applications more precisely. In this paper, at first we tell about cloud computing – we define it completely and discuss about all aspects of this new generation of internet – also in the first section we discuss about efficiency of cloud computing in saving of energy and tell differences between the amount of energy that cloud resources use and the amount of energy that other computerized frames were use. We proof our suggestions and ideas by the means of analytical and mathematical analysis; after that when we understand about all of its concepts, a brief review about Microsoft decisions on cloud computing were present, we review new systems and software’s of Microsoft that equipped with cloud computing and study the advantages and disadvantages of these new services which are equipped with cloud computing. In the next section by the knowing of whole concepts and key features of this network and also by knowing the critical uses of this network on different industries we introduce fixedinterval smoothers; which can be used for estimation and prediction of different parameters in such network. For example, we can use this for estimation and prediction about permeating of hackers or for predicting the amount of users that will use special part of our network in a particular period of time or by tracking and tracing the packets or even users. By performing such activities we’re able to eliminate malicious and spyware treatments from the beginning points of our network. Also by the means of mathematical analysis in this paper, we demonstrate all of our suggestions and according to these proofs we conclude about workability and liability of such network.

Information Fusion for State Estimation of Power Battery in Electric Vehicle Based on Unscented Kalman Filter

2013 ◽  
Vol 303-306 ◽  
pp. 975-978
Author(s):  
Hong Yu Zheng ◽  
Chang Fu Zong
Keyword(s):  
Kalman Filter ◽  
Electric Vehicle ◽  
Electric Vehicles ◽  
Information Fusion ◽  
Unscented Kalman Filter ◽  
Accurate Estimate ◽  
Fusion Algorithm ◽  
Power Battery ◽  
Battery State Of Charge ◽  
New Generation

The power battery state of charge (SOC) in electric vehicles is not easy to measure accurately or apply a sensor but the expense is increased. However the variable of SOC is great importance to control of electric vehicles. A power battery model is built by the Partnership for a New Generation of Vehicles (PNGV) model to estimate the state of SOC. In order to make a high accurate estimate for SOC value, an information fusion algorithm based on unscented kalman filter (UKF) is introduced to design an observer. The test results show that the observer based information fusion and UKF are effective and accuracy, so it is may apply it the electric vehicle control and observation.

Online Calibration of Traffic Prediction Models

2005 ◽  
Vol 1934 (1) ◽  
pp. 235-245 ◽  
Author(s):  
Constantinos Antoniou ◽  
Moshe Ben-Akiva ◽  
Haris N. Koutsopoulos
Keyword(s):  
Kalman Filter ◽  
Prediction Models ◽  
Unscented Kalman Filter ◽  
State Space Model ◽  
The United States ◽  
Sensor Data ◽  
Solution Technique ◽  
Online Calibration ◽  
Estimation And Prediction ◽  
Innovative Solution

A methodology for the online calibration of the speed–density relationship is formulated as a flexible state–space model. Applicable solution approaches are discussed and three of them–-the extended Kalman filter (EKF), the iterated EKF, and the unscented Kalman filter (UKF)–-are selected and presented in detail. An application of the methodology with freeway sensor data from two networks in Europe and the United States is presented. The improvement in the estimation and prediction of speeds due to online calibration (compared with the speeds obtained from the relationship calibrated offline) is demonstrated. EKF provided the most straightforward solution to this problem and, indeed, achieved considerable improvements in estimation and prediction accuracy. The benefits obtained from the use of the more computationally expensive iterated EKF algorithm are shown. An innovative solution technique (UKF) is also presented.

scholarly journals Rocket tracking impact point prediction using α-β, standard Kalman, extended, Kalman, and unscented Kalman filters: a comparative analysis

2020 ◽  
Vol 9 (3) ◽  
pp. e42932022
Author(s):  
José Alano Peres de Abreu ◽  
Roberto Célio Limão de Oliveira ◽  
João Viana da Fonseca Neto
Keyword(s):  
Mathematical Model ◽  
Kalman Filter ◽  
Comparative Analysis ◽  
Unscented Kalman Filter ◽  
Target Position ◽  
Accurate Information ◽  
Impact Point ◽  
Advantages And Disadvantages ◽  
Tracking Algorithms ◽  
The Impact

Accurate information about the impact point (IP) of a suborbital rocket on Earth’s surface during a launch is an important requirement for range safety operations. Four different estimators, i.e., the α-β filter, standard Kalman filter (SKF), extended Kalman filter (EKF), and unscented Kalman filter (UKF), are considered for the suborbital rocket tracking problem, whose data are used specifically for improving the accuracy of the IP prediction (IPP) of these vehicles. This paper presents a comparative analysis between the results of the estimators. Rocket flight data are discussed to demonstrate the advantages and disadvantages of the estimators and to determine the inherent limitations in predicting the aerodynamic effects found in certain flight situations. We discuss the appropriate mathematical model of a filter capable of running the real-time algorithm for the estimation of target position and velocity. This work uses actual data from a radar sensor to evaluate the tracking algorithms. We insert the filter result into the mathematical model developed to predict the rocket IP on Earth's surface. The main goal of this study is to evaluate the performance of four different estimators when specifically applied for the improvement of the IPP of suborbital rockets. It is demonstrated that the UKF outperforms all other tracking algorithms in terms of the accuracy and robustness of IP estimation.

Data Integrity Techniques in Cloud Computing: An Analysis

2017 ◽  
Vol 7 (8) ◽  
pp. 121 ◽  
Author(s):  
Neha Thakur ◽  
Aman Kumar Sharma
Keyword(s):  
Cloud Computing ◽  
Cloud Storage ◽  
Data Integrity ◽  
Advantages And Disadvantages ◽  
Security Models ◽  
Cloud Storage Service ◽  
Storage Service ◽  
Cloud Users ◽  
It Enterprises

Cloud computing has been envisioned as the definite and concerning solution to the rising storage costs of IT Enterprises. There are many cloud computing initiatives from IT giants such as Google, Amazon, Microsoft, IBM. Integrity monitoring is essential in cloud storage for the same reasons that data integrity is critical for any data centre. Data integrity is defined as the accuracy and consistency of stored data, in absence of any alteration to the data between two updates of a file or record.  In order to ensure the integrity and availability of data in Cloud and enforce the quality of cloud storage service, efficient methods that enable on-demand data correctness verification on behalf of cloud users have to be designed. To overcome data integrity problem, many techniques are proposed under different systems and security models. This paper will focus on some of the integrity proving techniques in detail along with their advantages and disadvantages.

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