Mobile Cloud Computing

Mobile Cloud Computing (MCC) which combines mobile computing and cloud computing, has become one of the industry ring words and a major conversation thread in the IT world with an explosive development of the mobile applications and emerging of cloud computing idea, the MCC has become a possible technology for the mobile service users. The concepts of Cloud computing are naturally meshed with mobile devices to allow on-the-go functionalities and benefits. The mobile cloud computing is emerging as one of the most important branches of cloud computing and it is expected to expand the mobile ecosystems. As more mobile devices enter the market and evolve, certainly security issues will grow as well. Also, enormous growth in the variety of devices connected to the Internet will further drive security needs. MCC provides a platform where mobile users make use of cloud services on mobile devices. The use of MCC minimizes the performance, compatibility, and lack of resources issues in mobile computing environment.

Role of Security Mechanisms in the Building Blocks of the Cloud Infrastructure

This chapter describes how security plays a vital role in cloud computing, as the name itself specifies the data can be stored from any place and can be owned by anyone. Even though the cloud offers many benefits such as flexibility, scalability and agility, security issues are still backlog the cloud infrastructure. Much research is being done on cloud security equal to the scheduling problems in the cloud environment. The customers under the cloud providers are very concerned about their data, which has been stored in the cloud environment. In this regard, it is essential for a cloud provider to implement some powerful tools for security, to provide a secure cloud infrastructure to the customers. Generally speaking, there are some foundational needs to be attained and some actions to be combined to ensure data security in both cloud, as well as, non-cloud infrastructure. This book chapter concentrates only on the security issues, security measures, security mechanisms, and security tools of the cloud environment.

Data Classification and Prediction

This chapter describes how we live in the era of data, where every event in and around us creates a massive amount of data. The greatest challenge in front of every data scientist is making this raw data, a meaningful one to solve a business problem. The process of extracting knowledge from the large database is called as Data mining. Data mining plays a wrestling role in all the application like Health care, education and Agriculture, etc. Data mining is classified predictive and descriptive model. The predictive model consists of classification, regression, prediction, time series analysis and the descriptive model consists of clustering, association rules, summarization and sequence discovery. Predictive modeling associates the important areas in the data mining called classification and prediction.

High Efficient Data Embedding in Image Steganography Using Parallel Programming

Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the Internet. For hiding secret information in images, there exist a large variety of steganographic techniques some are more complex than others and all of them have respective strong and weak points.. As embedding data in an image, is independent of one another. Parallelism can be used to achieve considerable time gain. nography, although it has made communication safe, it has its own drawbacks. One among it is time required to embed data in pixels of the image. This problem is bugging computer scientists from long time. This paper discusses a method which makes OpenMP parallel library to parallelize embedding of data, which basically reduces the time by almost fifty percent and to achieve PSNR ranging from 30 to 50 after embedding data in the pixels of the image.

Amelioration of Big Data Analytics by Employing Big Data Tools and Techniques

This chapter describes how in the digital data era, a large volume of data became accessible to data science engineers. With the reckless growth in networking, communication, storage, and data collection capability, the Big Data science is quickly growing in each engineering and science domain. This paper aims to study many numbers of the various analytics ways and tools which might be practiced to Big Data. The important deportment in this paper is step by step process to handle the large volume and variety of data expeditiously. The rapidly evolving big data tools and Platforms have given rise to numerous technologies to influence completely different Big Data portfolio.In this paper, we debate in an elaborate manner about analyzing tools, processing tools and querying tools for Big datahese tools used for data analysis Big Data tools utilize numerous tasks, like Data capture, storage, classification, sharing, analysis, transfer, search, image, and deciding which might also apply to Big data.

Location-Based Advertising Using Location-Aware Data Mining

This chapter describes how mobile advertisements are critical for both mobile users and businesses as people spend more time on mobile devices than on PCs. However, how to send relevant advertisements and avoid unnecessary ones to specific mobile users is always a challenge. For example, a concert-goer may like to visit restaurants or parks before the concert and may not like the advertisements of grocery stores or farmers' markets. This research tries to overcome the challenge by using the methods of location-aware data mining. Furthermore, privacy is always a great concern for location-based advertising (LBA) users because their location information has to be shared in order to receive the services. This chapter also takes the concern into serious consideration, so the user privacy will not be compromised. Preliminary experiment results show the proposed methods are effective and user-privacy is rigorously preserved.

A Recent Study on High Dimensional Features Used in Stego Image Anomaly Detection

This chapter describes how ample feature extraction techniques are available for detecting hidden messages in digital images. In the recent years, higher dimensional features are extracted to detect the complex and advanced steganographic algorithms. To improve the precision of steganalysis, many combinations of high dimension feature spaces are used by recent steganalyzers. In this chapter, we present a summary of several methods existing in literature. The aim is to provide a broad introduction to high dimensional features space used so for and to state which the most accurate and best feature extraction methods is.

Energy Optimization in Cryptographic Protocols for the Cloud

In this chapter, we study the energy consumption by various modern cryptographic protocols for the cloud from the algorithmic perspective. The two categories of protocols we consider are (1) hash functions and (2) symmetric key encryption protocols. We identify various parameters that moderate energy consumption of these hashes and protocols. Our work is directed towards redesigning or modifying these algorithms to make them consume lesser energy. As a first step, we try to determine the applicability of the asymptotic energy complexity model by Roy on these hashes and protocols. Specifically, we try to observe whether parallelizing the access of blocks of data in these algorithms reduces their energy consumption based on the energy model. Our results confirm the applicability of the energy model on these hashes and protocols. Our work is motivated by the importance of cryptographic hashes and symmetric key protocols for the cloud. Hence the design of more energy efficient hashes and protocols will contribute in reducing the cloud energy consumption that is continuously increasing.

Ontology Based Feature Extraction From Text Documents

This article describes how semantic annotation is the most important need for the categorization of labeled or unlabeled textual documents. Accuracy of document categorization can be greatly improved if documents are indexed or modeled using the semantics rather than the traditional term-frequency model. This annotation has its own challenges like synonymy and polysemy in the document categorization problem. The model proposes to build domain ontology for the textual content so that the problems like synonymy and polysemy in text analysis are resolved to greater extent. Latent Dirichlet Allocation (LDA), the topic modeling technique has been used for feature extraction from the documents. Using the domain knowledge on the concept and the features grouped by LDA, the domain ontology is built in the hierarchical fashion. Empirical results show that LDA is the better feature extraction technique for text documents than TF or TF-IDF indexing technique. Also, the proposed model shows improvement in the accuracy of document categorization when domain ontology built using LDA has been used for document indexing.

Analysis of Mobile Cloud Computing

This chapter describes how cloud computing is an emerging concept combining many fields of computing. The foundation of cloud computing is the delivery of services, software and processing capacity over the Internet, reducing cost, increasing storage, automating systems, decoupling of service delivery from underlying technology, and providing flexibility and mobility of information. However, the actual realization of these benefits is far from being achieved for mobile applications and open many new research questions. Together with an explosive growth of the mobile applications and emerging of cloud computing concept, mobile cloud computing (MCC) has been introduced to be a potential technology for mobile services. With this importance, this chapter provides an overview of mobile cloud computing in which its definitions, architecture, and advantages have been presented. It presents an in-depth knowledge of various aspects of Mobile Cloud Computing (MCC). We give a definition of mobile cloud computing and provide an overview of its features.