Volume 07 Issue 06, November 2019

A Secure Vehicular Communication Using Road Side Unit Trust Management Scheme

Tejal Maheshwari

Page no:01-06

Abstract

the responsibility of every vehicle is to forward the traffic status to requester vehicles. As same as MANET due to open medium security is always the major concern in VANET. The attacker vehicle drops a huge amount of data packets that contain the information of traffic status. In this paper, we proposed the secure VANET communication in the presence of RSU. RSU identified the attacker vehicles by that unusual interference in communication. The RSU collects the information from vehicles and forwarded it to other vehicles or other RSU. The proposed Attack Prevention algorithm is applied to RSU to recognize the attacker vehicle activities. The RSU after identified it block their functionality of communication. We assume that RSUs are deployed along the highway which is at least several kilometers far from each other. On the highway, maybe some vehicles travel faster or slower than average, but we assume the majority of vehicles travel in normal or similar velocities. The aim of this research is to providing security against malicious attacks, to allow new proposed models to build their work on solid realistic models against packet dropping attack. The performance of the proposed secure algorithm is compared with the old Base-line scheme and ART scheme in VANET. In this proposed scheme, vehicles obtain traffic data when they pass by a roadside unit (RSU) and then share the data after They travel out of the RSU's coverage. The performance of the proposed scheme is better than an existing scheme in terms of providing security and overcome from the problem of congestion to manage traffic in a network.

Survey on Analysis of Error Minimization Based on Enhanced RangeBase Approaches in WSN

Sweety Kour, Rajesh Nema

Page no:07-10

Abstract

Analysis Localization error minimization primarily based on several applications of wireless sensing element networks (WSN) need data regarding the geographical location of every sensing element node. Selforganization and localization capabilities are one in every of the foremost necessary needs in sensing element networks. This paper provides a summary of centralized distance-based algorithms for estimating the positions of nodes in a very sensing element network. Secure localization of unknown nodes in a very Wireless sensing element Network (WSN) is a very important analysis subject Wireless sensing element Networks (WSN), a component of pervasive computing, are presently getting used on an oversized scale to observe period environmental standing but these sensors operate below extreme energy constraints and are designed by keeping an application in mind. Planning a brand new wireless sensing element node is a very difficult task and involves assessing a variety of various parameters needed by the target application. In the survey realize drawback not sense positioning of nodes .but planned formula realize the optimum location of nodes supported minimize error and very best answer in WSN. Localization algorithms are mentioned with their benefits and drawbacks. Lastly, a comparative study of localization algorithms supported the performance in WSN.

A Survey on Stock Market Analysis using Supervised Machine Learning

Raju Bilwal, Rajeev Gupta

Page no:11-14

Abstract

The Machine Learning algorithm handles previously logged data as training samples and makes supports for forecasting the stock price for future trends. Supervised learning is a powerful machine learning task. The basic idea of supervised learning is to classify and process data using Machine Learning. The stock market is widely used in investment schemes promising high returns but has some risk. Stock returns are very fluctuating in nature. They depend upon factors like previous stock prices, current market trends, financial news, social media etc. Many practices, like technical analysis, fundamental analysis, time series analysis, statistical analysis, etc., are used to predict the stock value. Still, none of these procedures is proven as an allowable prediction tool. The technical and fundamental or time series analysis is used by most stockbrokers while making stock predictions. Python is the programming language used to predict the stock market using machine learning. In this paper, we propose a machine learning (ML) approach that will be trained from the available stock data and gain intelligence and then uses the acquired knowledge for an accurate prediction.