scholarly journals FMPM: Futuristic Mobility Prediction Model for Mobile Adhoc Networks Using Auto-Regressive Integrated Moving Average

2018 ◽  
Vol 29 (2) ◽  
pp. 7-17 ◽  
Author(s):  
Prasanna venkatesan Theerthagiri ◽  
◽  
T. Menakadevi ◽  
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Mobility Prediction ◽  
Mobile Adhoc Networks ◽  
Auto Regressive ◽  
Adhoc Networks

The Prediction Model Research for Network Traffic Based on ARMA

2014 ◽  
Vol 530-531 ◽  
pp. 760-763
Author(s):  
Zhao Ji Zhang
Keyword(s):  
Prediction Model ◽  
Network Traffic ◽  
Detection System ◽  
Moving Average ◽  
Third Party ◽  
Testing System ◽  
Network Resources ◽  
Network Intrusion ◽  
Model Research ◽  
Auto Regressive

This paper presents a new WIA-PA network intrusion detection system -- Auto Regressive and Moving Average (ARMA) network traffic prediction model. This model can predict the network traffic quickly and accurately, and because this is a third party testing system, it does not need to take network resources, the security of the WIA-PA network design is of vital importance. The simulation results show that our proposed system can effectively detect intrusion attack, improve the performance of the entire network, prolonging the life of the network.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

2021 ◽  
Vol 8 (1) ◽  
pp. 1111-1126
Author(s):  
Aba Diop ◽  
Abdourahmane Ndao ◽  
Cheikh Tidiane Seck ◽  
Ibrahima Faye
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Arima Model ◽  
Short Term ◽  
Ministry Of Health ◽  
Term Prediction ◽  
Daily Data ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Second Wave

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

Short-term prediction model for daily COVID-19 reported positive cases in Senegal

2021 ◽  
Vol 8 (1) ◽  
pp. 1507-1523
Author(s):  
Aba Diop ◽  
Abdourahmane Ndao ◽  
Cheikh Tidiane Seck ◽  
Ibrahima Faye
Keyword(s):  
Prediction Model ◽  
Moving Average ◽  
Arima Model ◽  
Short Term ◽  
Ministry Of Health ◽  
Term Prediction ◽  
Daily Data ◽  
Auto Regressive ◽  
Short Term Prediction ◽  
Second Wave

In this work, we use an Auto-Regressive Integrated Moving Average (ARIMA) model to study the evolution of COVID-19 disease in Senegal and then make short-term predictions about the number of people likely to be infected by the coronavirus. We are dealing with daily data provided by the Senegalese Ministry of Health during the period from March 2, 2020 to March 2, 2021.Our results show that the peak of the disease appearsduring the second wave seems to be reached on February 12 2021. But they also show that the number of COVID-19 infections will be around 200 cases per day during the next 30 days if the trend of the total number of tests performed is maintained.

A Comparative study on secure routing algorithms SAODV and A-SAODV in Mobile AdHoc Networks (MANET) – The Enhancements of AODV

2012 ◽  
Vol 3 (2) ◽  
pp. 419-423
Author(s):  
JARUPULA RAJESHWAR ◽  
Dr G NARSIMHA
Keyword(s):  
Routing Protocol ◽  
Routing Protocols ◽  
Dynamic Network ◽  
Secure Routing ◽  
Mobile Adhoc Networks ◽  
Security Challenges ◽  
Basic Protocol ◽  
Freely Moving ◽  
Adhoc Networks ◽  
Secure Routing Protocols

A freely moving nodes forming as group to communicate among themselves are called as Mobile AdHoc Networks (MANET). Many applications are choosing this MANET for effective commutation due to its flexible nature in forming a network. But due to its openness characteristics it is posing many security challenges. As it has highly dynamic network topology security for routing is playing a major role. We have very good routing protocols for route discovery as well as for transporting data packers but most of them lack the feature of security like AODV. In this paper we are studying the basic protocol AODV and identify how it can be made secure. We are studying a protocol S-AODV which is a security extension of AODV which is called Secure AODV (S-AODV) and we are studying enhanced version of S-AODV routing protocol a Adaptive Secure AODV (A-SAODV). Finally we have described about the parameter to be taken for performance evaluation of different secure routing protocols

Prognosticating the spread of covid-19 pandemic based on optimal arima estimators

2020 ◽  
Vol 20 ◽  
Author(s):  
Venuka Sandhir ◽  
Vinod Kumar ◽  
Vikash Kumar
Keyword(s):  
South Africa ◽  
Statistical Analysis ◽  
Moving Average ◽  
Arima Model ◽  
Information Criterion ◽  
Stable Case ◽  
Johns Hopkins University ◽  
Modelling Techniques ◽  
Auto Regressive ◽  
Global Threat

Background: COVID-19 cases have been reported as a global threat and several studies are being conducted using various modelling techniques to evaluate patterns of disease dispersion in the upcoming weeks. Here we propose a simple statistical model that could be used to predict the epidemiological extent of community spread of COVID-19from the explicit data based on optimal ARIMA model estimators. Methods: Raw data was retrieved on confirmed cases of COVID-19 from Johns Hopkins University (https://github.com/CSSEGISandData/COVID-19) and Auto-Regressive Integrated Moving Average (ARIMA) model was fitted based on cumulative daily figures of confirmed cases aggregated globally for ten major countries to predict their incidence trend. Statistical analysis was completed by using R 3.5.3 software. Results: The optimal ARIMA model having the lowest Akaike information criterion (AIC) value for US (0,2,0); Spain (1,2,0); France (0,2,1); Germany (3,2,2); Iran (1,2,1); China (0,2,1); Russia (3,2,1); India (2,2,2); Australia (1,2,0) and South Africa (0,2,2) imparted the nowcasting of trends for the upcoming weeks. These parameters are (p, d, q) where p refers to number of autoregressive terms, d refers to number of times the series has to be differenced before it becomes stationary, and q refers to number of moving average terms. Results obtained from ARIMA model showed significant decrease cases in Australia; stable case for China and rising cases has been observed in other countries. Conclusion: This study tried their best at predicting the possible proliferate of COVID-19, although spreading significantly depends upon the various control and measurement policy taken by each country.

A Novel Defense Scheme against Flooding Attack in Mobile Adhoc Networks

2018 ◽  
Vol 12 (1) ◽  
pp. 15-22 ◽  
Author(s):  
T. Bhatia ◽  
A.K. Verma ◽  
G. Sharma ◽  
S. Bala
Keyword(s):  
Mobile Adhoc Networks ◽  
Flooding Attack ◽  
Adhoc Networks
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