Mobile Cloud Gaming and Today's World

Mobile cloud computing is the emerging field. Along-with different services being provided by the cloud like Platform as a Service, Infrastructure as a Service, Software as a Service; Game as a Service is new terminology for the cloud services. In this paper, we generally discussed the concept of mobile cloud gaming, the companies that provide the services as GaaS, the generic architecture, and the research work that has been done in this field. Furthermore, we highlighted the research areas in this field.

Big Data and Internet of Things for Analysing and Designing Systems Based on Hyperspectral Images

The recent development of sensors remote sensing is an important source of information for mapping and natural and man-made land covers. The increasing amounts of available hyperspectral data originates from AVIRIS, HyMap, and Hyperion for a wide range of applications in the data volume, velocity, and variety of data contributed to the term big data. Sensing is enabled by Wireless Sensor Network (WSN) technologies to infer and understand environmental indicators, from delicate ecologies and natural resources to urban environments. The communication network creates the Internet of Things (IoT) where sensors and actuators blend with the environment around us, and the information is shared across platforms in order to develop a common operating picture (COP). With RFID tags, embedded sensor and actuator nodes, the next revolutionary technology developed transforming the Internet into a fully integrated Future Internet. This chapter describes the use of Big Data and Internet of the Things for analyzing and designing various systems based on hyperspectral images.

Performance Improvement IoT Applications Through Multimedia Analytics Using Big Data Stream Computing Platforms

A Big Data Stream Computing (BDSC) Platform handles real-time data from various applications such as risk management, marketing management and business intelligence. Now a days Internet of Things (IoT) deployment is increasing massively in all the areas. These IoTs engender real-time data for analysis. Existing BDSC is inefficient to handle Real-data stream from IoTs because the data stream from IoTs is unstructured and has inconstant velocity. So, it is challenging to handle such real-time data stream. This work proposes a framework that handles real-time data stream through device control techniques to improve the performance. The frame work includes three layers. First layer deals with Big Data platforms that handles real data streams based on area of importance. Second layer is performance layer which deals with performance issues such as low response time, and energy efficiency. The third layer is meant for Applying developed method on existing BDSC platform. The experimental results have been shown a performance improvement 20%-30% for real time data stream from IoT application.

Centralized Fog Computing Security Platform for IoT and Cloud in Healthcare System

This chapter proposes an efficient centralized secure architecture for end to end integration of IoT based healthcare system deployed in Cloud environment. The proposed platform uses Fog Computing environment to run the framework. In this chapter, health data is collected from sensors and collected sensor data are securely sent to the near edge devices. Finally, devices transfer the data to the cloud for seamless access by healthcare professionals. Security and privacy for patients' medical data are crucial for the acceptance and ubiquitous use of IoT in healthcare. The main focus of this work is to secure Authentication and Authorization of all the devices, Identifying and Tracking the devices deployed in the system, Locating and tracking of mobile devices, new things deployment and connection to existing system, Communication among the devices and data transfer between remote healthcare systems. The proposed system uses asynchronous communication between the applications and data servers deployed in the cloud environment.

Internet of Things

Recently, Internet of Things (IoT) has aroused great interest among the educational, scientific research, and industrial communities. Researchers affirm that IoT environments will make people's daily life easier and will lead to superior services, great savings as well as a nifty use of resources. Consequently, IoT merchandise and services will grow exponentially in the upcoming years. The basic idea of IoT is to connect physical objects to the Internet and use that connection to provide some kind of useful remote monitoring or control of those objects. The chapter presents the overall IoT vision, the technologies for achieving it, IoT challenges and its applications. This chapter also attempts to describe and analyze threat types for privacy, security and trust in IoT as well as shows how big data is an important factor in IoT. This chapter will expose the readers and researchers who are interested in exploring and implementing the IoT and related technologies to the progress towards the bright future of the Internet of Things

Big Data Analytics in Aquaculture Using Hive and Hadoop Platform

In the field of Aquaculture with the help of digital advancements huge amount of data is constantly produced for which the data of the aquaculture has entered in the big data world. The requirement for data management and analytics model is increased as the development progresses. Therefore, all the data cannot be stored on single machine. There is need for solution that stores and analyzes huge amounts of data which is nothing but Big Data. In this chapter a framework is developed that provides a solution for shrimp disease by using historical data based on Hive and Hadoop. The data regarding shrimps is acquired from different sources like aquaculture websites, various reports of laboratory etc. The noise is removed after the collection of data from various sources. Data is to be uploaded on HDFS after normalization is done and is to be put in a file that supports Hive. Finally classified data will be located in particular place. Based on the features extracted from aquaculture data, HiveQL can be used to analyze shrimp diseases symptoms.

Big Data and Cloud Computing

Big data and cloud computing are transforming information technology. These comparable technologies are the result of dramatic developments in computational power, virtualization, network bandwidth, availability, storage capability, and cyber-physical systems. The crossroads of these two areas, involves the use of cloud computing services and infrastructure, to support large-scale data analytics research, providing relevant solutions or future possibilities for supply chain management. This chapter broadens the current posture of cloud computing and big data, as associate with the supply chain solutions. This chapter focuses on areas of significant technology and scientific advancements, which are likely to enhance supply chain systems. This evaluation emphasizes the security challenges and mega-trends affecting cloud computing and big data analytics pertaining to supply chain management.

Can LTE-A Support Real-Time Smart Meter Traffic in the Smart Grid?

The chapter investigates the scheduling load added on a long-term evolution (LTE) and/or LTE-Advanced (LTEA) network when automatic meter reading (AMR) in advanced metering infrastructures (AMI) is performed using internet of things (IoT) deployments of smart meters in the smart grid. First, radio resource management algorithms to perform dynamic scheduling of the meter transmissions are proposed and shown to allow the accommodation of a large number of smart meters within a limited coverage area. Then, potential techniques for reducing the signaling load between the meters and base stations are proposed and analyzed. Afterwards, advanced concepts from LTE-A, namely carrier aggregation (CA) and relay stations (RSs) are investigated in conjunction with the proposed algorithms in order to accommodate a larger number of smart meters without disturbing cellular communications.

Predictive Analytics to Support Clinical Trials Get Healthier

The concept of clinical big data analytics is simply the joining of two or more previously disparate sources of information, structured in such a way that insights are prescribed from examination of the new expanded data set. The combination with Internet of Things (IoT), can provide multivariate data, if healthcare organizations build the infrastructure to accept it. Many providers are able to integrate financial and utilization data to create a portrait of organizational operations, but these sources do not give a clear idea of what patients do on their own time. Embracing the centrality of the IoT would relinquish the idea that provider is the only pillar around which healthcare revolves. This chapter provides deeper insights into the four major challenges: costly protocol amendments, increasing protocol complexity and investigator site burden. It also provides recommendations for streamlining clinical trials by following a two dimension approach-optimization at a program level (clinical development plan) as well as at the individual trial candidate level.

A Study on Performance of E-Commerce Web Applications

The growth of World Wide Web and technologies has made business functions to be executed fast and easier. E-commerce has provided a cost efficient and effective way of doing business. In this paper the importance of e-commerce web applications and how Internet of Things is related to e-commerce is well discussed. In the end-user perspective, the performance of e-commerce application is mainly connected to the web application design and services provided in the e-commerce website. A grading system is used to evaluate the performance of each e-commerce website.