Seema Bawa

A systematic review on routing protocols for Vehicular Ad Hoc Networks

Vehicular Ad Hoc Networks (VANETs) have emerged as a new powerful technology with an aim of providing safety for the persons sitting in the vehicles. Vehicles may be connected to the Internet with/without the existing infrastructure using various IEEE standards such as IEEE 802.11p. But as nodes in VANETs have very high mobility, so there are lots of challenges to route the packets to their final destination which need to be addressed by existing/proposing new solutions for the same. Keeping in view of the above, this paper provides a detailed description of various existing routing techniques in literature with an aim of selecting a particular strategy depending upon its applicability in a particular application. A detailed categorization of various routing techniques is provided in the paper with critical discussion on each categorization with respect to its advantages, disadvantages, various constraints and applications. Finally, numbers of parameters are selected for comparison and analysis of all the existing routing schemes in the literature.

Automated Coin Recognition System using ANN

Coins are integral part of our day to day life. We use coins everywhere like grocery store, banks, buses, trains etc. So it becomes a basic need that coins can be sorted and counted automatically. For this it is necessary that coins can be recognized automatically. In this paper we have developed an ANN (Artificial Neural Network) based Automated Coin Recognition System for the recognition of Indian Coins of denomination Rs. 1, 2, 5 and 10 with rotation invariance. We have taken images from both sides of coin. So this system is capable of recognizing coins from both sides. Features are extracted from images using techniques of Hough Transformation, Pattern Averaging etc. Then, the extracted features are passed as input to a trained Neural Network. 97.74% recognition rate has been achieved during the experiments i.e. only 2.26% miss recognition, which is quite encouraging.

Comparative Analysis of Resource Discovery Approaches in Grid Computing

Grid technologies enable the sharing of a wide variety of distributed resources. To utilize these resources, effective Resource Management systems are needed. Resource Management system performs resource discovery to obtain information about the available resources. However, the complex and dynamic nature of grid resources make sharing and discovery, a challenging issue. Resource Discovery is initiated by a network application to find suitable resources with in the Grid. Resource Discovery process is critical for efficient resource allocation and management. For making the Resource Discovery more efficient and reliable large numbers of Approaches are there. This paper analyses some of existing Approaches for Resource Discovery, which can search for the preferred resources quickly and efficiently (return the correct results quickly and reduce network complexity) in Grid computing. Finally a qualitative comparison between these Approaches based on the factors that affect Grid Resource Discovery process, has been done and results are presented.

A review of ranking approaches for semantic search on Web

With ever increasing information being available to the end users, search engines have become the most powerful tools for obtaining useful information scattered on the Web. However, it is very common that even most renowned search engines return result sets with not so useful pages to the user. Research on semantic search aims to improve traditional information search and retrieval methods where the basic relevance criteria rely primarily on the presence of query keywords within the returned pages. This work is an attempt to explore different relevancy ranking approaches based on semantics which are considered appropriate for the retrieval of relevant information. In this paper, various pilot projects and their corresponding outcomes have been investigated based on methodologies adopted and their most distinctive characteristics towards ranking. An overview of selected approaches and their comparison by means of the classification criteria has been presented. With the help of this comparison, some common concepts and outstanding features have been identified.

Establishing and Evaluating Trust in a Grid Environment

The migration of processing of computational jobs from centralized systems to open distributed systems have resulted in many communication channels and transactions. These crucial and confidential jobs span a range of domains and organizations, not all of which can be trusted to the same extent. Inconsistencies in current trust relationships highlight the need for a flexible, general-purpose trust management system. In this paper, we have proposed a Trust Model for establishing and evaluating trust in Grid environments. The proposed Trust model computes reputation and trustworthiness of the transacting domain on the basis of number of past transactions and rated feedback score.

A MapReduce-based scalable discovery and indexing of structured big data

Various methods and techniques have been proposed in past for improving performance of queries on structured and unstructured data. The paper proposes a parallel B-Tree index in the MapReduce framework for improving efficiency of random reads over the existing approaches. The benefit of using the MapReduce framework is that it encapsulates the complexity of implementing parallelism and fault tolerance from users and presents these in a user friendly way. The proposed index reduces the number of data accesses for range queries and thus improves efficiency. The B-Tree index on MapReduce is implemented in a chained-MapReduce process that reduces intermediate data access time between successive map and reduce functions, and improves efficiency. Finally, five performance metrics have been used to validate the performance of proposed index for range search query in MapReduce, such as, varying cluster size and, size of range search query coverage on execution time, the number of map tasks and size of Input/Output (I/O) data. The effect of varying Hadoop Distributed File System (HDFS) block size and, analysis of the size of heap memory and intermediate data generated during map and reduce functions also shows the superiority of the proposed index. It is observed through experimental results that the parallel B-Tree index along with a chained-MapReduce environment performs better than default non-indexed dataset of the Hadoop and B-Tree like Global Index (Zhao etal., 2012) in MapReduce. Solution for the lack of indexing in the MapReduce frameworksHadoop is proposed.Parallel implementation of existing B-Tree index is carried out in MapReduce.Parallel implementation of range search query is carried out in MapReduce.Cluster size, query coverage, number of map tasks and size of I/O data are analyzed.HDFS block size and, heap memory and intermediate data generated are analyzed.Proposed parallel B-Tree index provides high scalability and efficient data search.

Image Processing Based Systems and Techniques for the Recognition of Ancient and Modern Coins

Coins are frequently used in everyday life at various places like in banks, grocery stores, supermarkets, automated weighing machines, vending machines etc. So, there is a basic need to automate the counting and sorting of coins. For this machines need to recognize the coins very fast and accurately, as further transaction processing depends on this recognition. Three types of systems are available in the market: Mechanical method based systems, Electromagnetic method based systems and Image processing based systems. This paper presents an overview of available systems and techniques based on image processing to recognize ancient and modern coins.

Recognition of Handwritten Character of Manipuri Script

In this paper a backpropagation neural network based handwritten characters (Mapum Mayek ) recognition system of Manipuri Script is investigated. This paper presents various steps involved in the recognition process. It begins with thresholding of gray level image into binarised image, then from the binarised image the character pattern is segmented using connected component analysis and from the resized character matrix, its probabilistic features and fuzzy features are extracted. Using these features the network is trained and recognition tests are performed. Experiments indicate that the proposed recognition system performs well with the combined features and is robust to the writing variations that exist between persons and for a single person at different instances, thus being promising for user independent character recognition.

Web Service Categorization Using Normalized Similarity Score

 Abstract—Service discovery is one of challenging issues in Service-Oriented computing. Currently, most of the existing service discovering and matching approaches are based on keywords-based strategy. However, this method is inefficient and time-consuming. Based on the current dominating mechanisms of discovering and describing Web services with UDDI and WSDL, a novel approach for Web service categorization is proposed, where WSDLs documentation tag is used as only means to describe information pertaining to the entire Web services functionality which is used in conjunction with the current Web service standards, to automatically categorize a Web service into a one of the pre-defined categories. The words are extracted from WSDL of a Web service and Nearest Similarity Score (NSS), a Measure of Semantic Relatedness (MSR) of each word is calculated with every pre-defined category. Total value of all the words is calculated through the NSS and then Web service is assigned a category based on the sum of MSR of all the words provided in the Web service description tag. This work enables automatic semantic categorization of Web services.

RITS-MHT

Cloud computing is a recent phrase in marketing for a concept which has been known for years: Outsourcing. Cloud computing provides cornucopian of gratuities for both individual users and business organizations. Cloud is more of a notion where files and data are hosted online and can be accessed when required via a number of methods, anywhere, and at any time. That is the gist of it. Providing storage space to cloud users is an appealing service of cloud computing. Although cloud storage provides benefits of location independence access to data, reduced burden for hardware and software maintenance and many more, yet this service has several challenges in the range of security and preserving data. Cloud servers may exist in white puffy shapes in the azure, but these are not immune to temporal errors. To ensure that outsourced data is secure and is not tampered, cloud provider must allow data proprietor to periodically audit data integrity. Numerous Remote Data Auditing (RDA) protocols have been proposed by researchers so far. In presented work, to cope with this problem we analyze the efficiency issues of a current protocol for data integrity auditing in cloud storage and propose an approach based on Relative Index and Time Stamped Merkle Hash Tree (RITSMHT) which integrates MHT with relative index of a node resulting in reduction of computation cost of searching a data block from O(n) in Wangs protocol to O(log n) and time of last modification to data, thereby guarantying freshness of data respectively. RITSMHT ensures that the outsourced data has not been polluted as well as it assures that the recent copy of data is reclaimed. This protocol supports public auditing of data, and efficiently supports data dynamic operations like insertion, modification, deletion of outsourced data at minimal computational cost. The security of proposed protocol has been proved in Random Oracle Model (ROM). As compared to Wangs protocol, RITSMHT is more efficient in terms of computation cost.