Vehicular Ad hoc Network could manage the various critical issues of road transport. That is why it is the most crucial field of research for most of the researchers. This survey paper discusses various issues related to Security Challenges, Security Architecture actors, Security Authentication, Application Constraints, various trust models etc. this paper encourages you to think about various fields of work need to be carried out in this field for the better VANET environment. Various schemes have been mentioned which could be improved further as per considering various real time conditions.

In recent time, with the rapid development of web 2.0 the number of online user-generated review of product is increases very rapidly. It is very difficult for user to read all reviews and handle all websites to make a valuable decision at feature level. The feature level opinion mining has become very infeasible when people write same feature with contrary words or phrases. To produce a relevant feature based summary of domain synonyms words and phrase, need to be group into same feature group. In this work, we focus on feature based opinion mining and proposed a dynamic system for generate feature based summary of specific feature with specific polarity of opinion according to customer demand on periodic base and changed the summary after a span of period according to customer demand. First a method for feature (frequent and infrequent) extraction using the probabilistic approach at word-level. Second identify the corresponding opinion word and make feature-opinion pair. Third we designed an algorithm for final polarity detection of opinion. Finally, assigning the each feature-opinion pair into the respective feature based cluster (positive, negative or neutral) to generate the summary of specific feature with specific opinion on periodic base which are helpful for user. The experiment results show that our approach can achieves 96%accuracy in feature extraction and 92% accuracy in final polarity detection of feature-opinion pair in feature based summary generation task.

In this paper, we have evaluated the performance of flooding and probabilistic flooding broadcast methods in a VANET using real city map. A comparative analysis between the performance of these methods with varying traffic density and nodes speed has been conducted. Here, we have considered real city maps extracted from US census TIGER database. Node movements are generated using Intelligent Driving Model for lane Changing (IDM_LC) through VanetMobiSim mobility generator. A different probability for message dissemination is considered for the probabilistic broadcast method to investigate an appropriate probability value that may give best results. Different node densities, Sparse, Intermediate and Dense are considered. The results obtained show that in dense traffic scenario probabilistic flooding method achieves maximum packet delivery ratio for a specific value of p (i.e. 0.1). In sparsely populated network, the PDR is low as compared to other traffic conditions. In a sparse traffic density both the method perform better at high node mobility. But in intermediate and dense traffic scenario performance of both the methods is better in low node mobility.

At a recent time, the web has become a valuable source of online consumer review however as the number of reviews is growing in high speed. It is infeasible for user to read all reviews to make a valuable or satisfying decision because the same features, people can write it contrary words or phrases. To produce a useful summary of domain synonyms words and phrase, need to be a group into same feature group. We focus on feature-based opinion mining problem and this paper mainly studies feature based product categorization from the number of users - generated review available on the different website. First, a multi-feature segmentation method is proposed which segment multi-feature review sentences into the single feature unit. Second part of speech dictionary and context information is used to consider the irrelevant feature identification, sentiment words are used to identify the polarity of feature and finally an unsupervised clustering based product feature categorization method is proposed. Clustering is unsupervised machine learning approach that groups feature that have a high degree of similarity in a same cluster. The proposed approach provides satisfactory results and can achieve 100% average precision for clustering based product feature categorization task. This approach can be applicable to different product.