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In current times, the intensity of Internet usage has been on a relentless rise, in pace with the release of numerous real-time applications like video transmission and non-real-time applications. Due to high error channels, the end-users will observe poor quality video over satellite network. The applications are competing themselves to inject their traffic into the network and hence end users get unfair shares of the bandwidth for the video transmissions. Also, the high transmission rate of video applications affects traditional Internet applications like e-mail. TCP-friendly rate control (TFRC), TFRC-satellite and multiple TFRC streams (MulTFRC), which are intended to provide congestion control, error control and increase bandwidth (BW) utilization respectively, may fail over the satellite networks with high error rate and high congestion. Besides, these connection-oriented protocols increase the applications overhead. In this paper, we present a cooperative flow regulation protocol (CFR), which provides fair access to both real-time and non-real-time applications, over networks with high congestion and high error. Verification of the proffered approach, by simulations, show that the CFR protocol reduces the loss of video frames by 30–40%, compared to DCCP and MulTFRC, and 50–60% compared to user datagram protocol (UDP) traffic, without compromise of the video quality.

Biomedical workflow applications’ necessities have increased with the progress of the biotechnology industry and it involves large volumes of data which require large-scale computation. Due to the ...

Autism spectrum disorder (ASD) is a very high-flying area of research in the current era owing to its limited and on-going exploration. This chapter aims to bridge the gap of such late realization of autistic feature through machine intervention commonly known as computer vision. In this chapter, basic summarization of important characteristic features of autism and how those features could be measured and altered before a human could recognize are proposed. The chapter proposes a model for activity identification of the autistic child through video recordings. The approach is modelled in a way that consists of two phases: 1) Optical flow method detects the unusual frames based on motion pattern. 2) Each of these detected frames are fed to convolution neural network, which is trained to extract features and exactly classify if the particular frame under consideration belongs to usual or unusual class. This examines the various activities, time delay, and factors influencing the motion of the autistic child under constrained scenarios proving maximum accuracy and performance.

Electronic health record (EHR) is a digital format of patient health information which typically contains patient contact information, vital signs, medical history, current and past medication that are outsourced to cloud. But the data leakage in cloud-based EHR causes serious privacy and security issues on various health care organizations. Existing cryptographic methods are not sufficient to address data breaches of EHR data in cloud. Blockchain technology is an emerging technology to solve privacy and security issues of EHR data in a decentralized manner on cloud. In this paper, we have designed a secure decentralized cloud-based medical block chain (CMBC) to solve privacy and security issues when exchanging the patient health care data to different medical organizations. Before uploading the EHR data to cloud-based block chain, the data are encrypted using lightweight authentication encryption algorithm AES_256_GCM to achieve better performance in health care. Based on the encrypted data on blockchain, Etherum based solidity smart code to restrict the access of EHR data on cloud. The medical blockchain also maintains the interoperability, traceability and anonymity of patients EHR data between various organizations. So the secure decentralized cloud-based medical block chain visualizes the patient EHR data in decentralized, distributed and immutable environment with improved security.

Autism spectrum disorder (ASD) is one of the most notable neurodevelopmental disorders that gained major notification among parents, clinicians and even in researchers in the current era. The early identification of autism is a much needed support for parents and clinicians. The proposed methodology aims in building a computational model for such easy and early diagnosis by analyzing and finding the correlations between features-to-class and feature-to-feature so as to maximize the former and minimize the latter. The correlation between features is analyzed using (i) chi square computation technique in filter method and (ii) information gain. On analyzing the correlations, the resultant attributes of every technique are trained separately under the standard linear SVM classifier and then tested for the models performance and accuracy. There are two major contributions of the proposed work; Method 1: to build a model that takes optimized features extracted from the chi square and information gain analysis from questionnaires on the application of genetic algorithm (GA). The optimized features are then trained and tested to classify autism in support of SVM linear classifier. Method 2: to build a model based on the application of back-propagation feed forward neural network to classify the presence of autism. The paper ensures better and faster convergence of the positive class label of autism with maximized accuracy, specificity, performance and minimized error. The novelty of the paper lies in the fact of extracting important features for modeling so as to make a prior analysis by any parents at home before approaching clinicians which supports the early intervention of autism.

Wireless sensor network is a group of organized sensor nodes that are limited to their resources such as limited bandwidth, energy etc. The data gathered from sensor nodes will be forwarded to the sink node to take proper actions. In order to avoid the failures, the sink nodes should be positioned in the better place for the resource optimization. In this regard, various researchers have concentrated on only energy efficiency and failed to focus on other performance parameters which would lead to network service performance degradation. This problem is resolved in the proposed research work by introducing the novel method namely life time and reliability concerned optimal sink relocation. Conserving the energy utilization of sensor nodes and improving the network lifetime with the concern to optimal clustering outcome is the major objective of this research work. In the proposed research work, Energy and Distance aware Clustering Technique is introduced to group the intermediate sensor/mobile nodes that are closer to both sink node and the sensor node which are ready to transfer the data. Here Weight K-means algorithm is adapted to cluster the nodes. To ensure the successful transmission of data without node failure, optimal cluster head selection is performed by using the cat swarm optimization algorithm with the objective of more remaining energy and available bandwidth. This helps to find the most suitable position for the sink node where it can be located in order to ensure better data transmission with resource optimization. This research work is implemented and evaluated in terms of varying performance measures. So, the presented research methods gives optimal outcome than the existing research method in regard to varying performance metrics called security level, energy utilization, false positive rate and delivery ratio.

The monitoring of personnel movements, package tracking and other constructional material based tracking is a top concern in pervasive smart environment. Wireless sensor network (WSN) has given its own individuality in tracking scenario. The challenges faced in this paper deals about an effective 2-dimensional movable system tracking and finding the possible prediction of its exact location of the object in the sensing area. An indoor based WSN with wireless sensor nodes has been created, in which RSSI based location sensing methodology is used. The sensing area is classified as shells and the movement of the node is judged with markov model. The proposed algorithm is tested with various speed conditions suitable for IoT applications. Real study shows the effectiveness of the proposed two dimensional algorithms. The obtained results show minimal location error and accurate location of the object. The proposed methodology serves as the better solution for IoT applications. The proposed algorithm outperforms the existing algorithm with reduced error rate and computing iterations or complexity. A cloud enabled IoT based application is developed to location the elderly and post-surgical people. The developed application serves as a better solution for monitoring the elderly people inside the smart home environment without disturbing their privacy.

Autism spectrum disorder (ASD) is one of the prominent and complex neurodevelopment disorders that are rising around the globe. In India, the autism rate has grown and now about 1 out of 88 children is prone to autism. ASD is associated with various problems such as low vision clarity, communication difficulties, repetitive behavior, insecure emotions, sensory issues, uninterested behavior, etc. Many researchers work towards the identification of major causes of autism and various impacts of the disorder. Although the basic causes and mechanism behind ASD could not be completely identified, it is obvious that the abnormal neurotransmission in brain regions prevents the child from normal behavior and motor actions and leads to an altered behavior. This impairment of mental difficulties could be identified with regular periodic analysis of the child communication behavior, social interaction, object identification, emotional sequence, linguistic ability and knowledge adaptation rate between the ages of 10 months to 3 years. The major pitfall in the ASD is that people from rural areas are not aware of the factors, causes and behavioral changes in the child with ASD. This leads to poor and misleading training and guiding the children for their future. Also the clinical analysis involves numerous trials to analyze the autistic characteristics across children age. Such clinical analysis could be made faster with the computer vision, thus resulting in early identification of autism. This chapter mainly focuses on the emotional sequence identification of children who are ASD positive and ASD negative (i.e., normal TD). The first screening is made through the facial expression analysis in children identified as ASD positive and TD. The methodology involved in facial detection and feature analysis is Viola–Jones algorithm and facial landmark vectorization, respectively. The facial expression is then classified using an SVM linear classifier. The facial expressions of ASD and TD are compared to identify the variations in facial expressions of children. This level of screening through expression identification is made in a contactless environment as children may get influenced by the object they face. The second level of improvement in screening involves a deep learning technique to identify the children facial expressions and emotion using facial landmark analysis. The former involves plotting up of 68 points on the face identified though landmark detection. In the later methodology, computational neural network technique is employed to identify the correlation between the points for facial emotion analysis. This improves the performance of analysis in emotion identification in children in the same contactless environment. The ultimate aim of the paper is to design a complete combinatorial model for the results from various screening experiments involving a multimodal deep learning technique that projects to a better solution of autism identification. The multimodal technique is planned to take input from the screenings 1 and 2 simultaneously as initial neurons, x, and form a hidden layer, h, by selecting the appropriate neuron in fine-tuning the cross-neuron activation function. The experimental analysis identifies (i) the expression showed by every autistic child, (ii) the major expression observed among the children with ASD, (iii) expression change in a child and the pattern of expression, and (iv) expression analysis through SVM and CNN learning methods. The experimental analysis boosts in training the autistic child on a proper streamline, which helps the parent and trainer in bringing up the child to reach heights.

The major challenge in the utilization of satellite network for the Internet access is its differing nature with wired network. To increase the performance of TCP-based applications over satellite network, performance enhancement proxies (PEP) had been incorporated. However, the existing PEP solutions have violated the end-to-end semantics of Internet applications. To solve this problem and to meet the future needs of the communication network, next-generation transport architecture (Iyengar and Ford in flow splitting with fate sharing in a next-generation transport services architecture, 2009; in A next-generation transport services architecture, 2009. http://tools.ietf.org/html/draft-iyengar-ford-tng-00) has been proposed. In this architecture, flow regulation functions (flow regulation layer) are factored out from end-to-end best delivery (endpoint layer). As a contribution to this research direction, we propose a novel flow regulation protocol to control the flows in terms of congestion, error and fairness over the Low Earth Orbit satellite environment. Simulation results reveal that the proposed protocol can provide optimized end-to-end performance (high throughput, high goodput and high information efficiency) without violating end-to-end semantics, and it also meets the network-level objective of fairness among the flows.

Fast and effective retrieval of relevant images has become an important requirement in applications that make use of digital images. The numerous sophisticated yet simple to use image editing tools available have made image tampering an easy task. This paper focuses on using spectral hashing to address aforesaid issues. Sparsification of graph Laplacian using spectral hashing is used for effective image retrieval. Copy-move forgery detection is done using Spectral-hashing-based Polar Cosine Transform (PCT). The proposed algorithm also addresses the identification of tampered regions that have been subjected to additional post-tampering manipulations. Simulation results show that the spectral-hashing-based identification of similar patches gives better results compared with some of the existing hashing techniques.

Mobile is getting increasingly popular and almost all applications are shifting into smartphones. Even though lots of advantages are there for smartphones, they are constrained by limitations in battery charge and the processing capacity. For running resource-intensive IoT applications like processing sensor data and dealing with big data coming from the IoT application, the capacity of existing smartphones is not enough, as the battery will be drained quickly, and it will be slow. Offloading is one of the major techniques through which mobile and cloud can be connected together and has emerged to reduce the complexity and increase the computation power of mobiles. Other than depending on the distant cloud for offloading, the extended version of cloud called fog computing can be utilized. Through offloading, the computationally intensive tasks can be shifted to the edge fog devices, and the results can be collected back at the mobile side reducing the burden. This chapter has developed mobile cloud offloading architecture for decision making using fuzzy logic where a decision is made as to whether we can shift the application to cloud or not depending on the current parameters of both cloud and the mobile side. Cloud computing introduces a number of variables depending on which offloading decision must be taken. In this chapter, the authors propose a fuzzy-logic-based algorithm which takes into consideration all the parameters at the mobile and cloud that will affect the offloading decision.

Encryption and decryption are the two most important complex methods for achieving security in any type of smart devices and systems/machines through transport layer security protocol (TLS). The symmetric key algorithms are the significant method for encrypting and decrypting the data/information using block cipher or stream cipher which is used for TLS protocol. The primary symmetric key block cipher algorithm used in TLS is Advanced Encryption standard (AES) and it provides security based on the key bits used in AES operation. The TLS protocol provides confidentiality(C), integrity (I) and Authenticity (A) in a single pass communication that is Authentication Encryption and Authentication Data (AEAD) between web browser and web server. It uses well known TLS cipher suite AES-GCM (Galois Counter mode) which is commonly used in TLS1.2. Suppose AES-NI hardware acceleration is not available in smart devices like tablets it causes performance issues in smart devices using TLS 1.2 protocol. If the smart device does not possess AES-NI, it can use software for running AES-GCM but it takes a lot of time for encryption/decryption of information, ergo causing the battery performance in smart devices. The newer symmetric Stream cipher CHACHA20-POLY1305 provides AEAD for securing the communication in smart devices thus reducing the battery cycles which is used for TLS 1.3. The paper discusses the pros and cons of AES-GCM authentication encryption used in TLS 1.2.

Owing to the data deluge of biomedical workflow applications, researchers consider cloud as a promising environment for deploying biomedical workflow applications. As the Workflow applications consist of precedence-constrained tasks, it requires high computing resources for its execution. Scheduling of pertinent resource to biomedical workflow applications is an appealing research area. The key concern is that the workflow applications should be scheduled with the appropriate resource such that the overall execution time and cost would be minimized and correspondingly resource utilization is maximized. The proposed Optimized Cost Scheduling Algorithm (OCSA) addresses this issue by scheduling the workflows to a resource in such a way that it efficiently reduces the time and cost. The proposed OCSA algorithm is simulated rigorously in WorkflowSim on real biomedical workflow application and the results are compared with the existing workflow scheduling approaches in terms of cost and time. The simulation result shows that the proposed scheduling algorithm appreciably reduces the execution time and cost than the existing scheduling algorithms.

The characteristics of Mobile Ad hoc Network (MANET) like error and reordering degrades the performance of TCP-based applications. Among the many proposals to reduce the impact of non-congestion events, TCP-NCE has been designed as the unified solution to discriminate between non-congestion and congestion events, and to respond to the events. Our initial analysis on TCP-NCE and other schemes (TCP-DCR and SACK-TCP) showed that the existing schemes including TCP-NCE fail to improve end-to-end performance in the presence of congestion, error, and reordering due to mobility and multipath routing. To overcome this problem, we designed “Enhanced TCP NCE” protocol to reduce the false differentiation on non-congestion events and to optimize the response procedure to those events. Our simulation results showed that the enhancement increased the performance by 15–20% over TCP-NCE. In addition, the consistency in yielding the higher performance throughout the simulation is observed for our protocol.

Many scientific applications are often modeled as workflows. The data and computational resource requirements are high for such workflow applications. Cloud provides a better solution to this problem by offering a promising environment for workflow execution. Modeling as workflows involves tremendous data computations and resources thereby creating the necessity to coordinate the workflow process. Workflow Management System (WMS) serves this purpose by orchestrating the tasks of the workflow application by executing it on distributed resources. Pegasus is a renowned WMS that has been generally used in complex large scale e-applications. This paper gives an overview of the Pegasus WMS and provides the description of the environmental setup with open-stack middleware. The paper further includes details about the workflow creation and execution in Pegasus and discusses about the scheduling of workflows in cloud with its issues.

Definitely the cloud journey is on the fast track. The cloud idea got originated and started to thrive from the days of server virtualization. Server machines are being virtualized in order to have multiple virtual machines, which are provisioned dynamically and kept in ready and steady state to deliver sufficient resources (compute, storage, and network) for optimally running any software application. That is, a physical machine can be empowered to run multiple and different applications through the aspect of virtualization. Resultantly, the utilization of expensive compute machines is steadily going up.

Workflow optimization is an approach to enhance the speed, robustness, and compactness of workflows by exploiting their structure, runtime, and output. This chapter initially highlights the significance of workflow optimization along with different possible levels of optimization. Further, it outlines the Taverna optimization framework over single and distributed infrastructure together with the optimization plug-ins that are validated using two scientific workflow executions.

Many scientific applications are often modeled as workflows. The data and computational resource requirements are high for such workflow applications. Cloud provides a better solution to this problem by offering the promising environment for the execution of these workflow. As it involves tremendous data computations and resources, there is a need to automate the entire process. Workflow management system serves this purpose by orchestrating workflow task and executing it on distributed resources. Pegasus is a well-known workflow management system that has been widely used in large-scale e-applications. This chapter provides an overview about the Pegasus Workflow Management System, describes the environmental setup with OpenStack and creation and execution of workflows in Pegasus, and discusses about the workflow scheduling in cloud with its issues.

Workflow orchestration is a method which smartly organizes the enterprise function with application, data, and infrastructure. The applications as well as their infrastructure can be dynamically scaled up or down using orchestration. On the contrary, integration enables the development of new applications with the capability to connect to any other application through specified interfaces. In this chapter, firstly, the opportunities and challenges in workflow orchestration and integration are explained. Following that, BioCloud, an architecture that demonstrates the task-based workflow orchestration using two bioinformatics workflows is explained in detail.

Workload consolidation is an approach to enhance the server utilization by grouping the VMs that are executing workflow tasks over multiple servers based on their server utilization. The primary objective is to optimally allocate the number of servers for executing the workflows which in turn minimize the cost and energy of data centers. This chapter consolidates the cost- and energy-aware workload consolidation approaches along with the tools and methodologies used in modern cloud data centers.

There are several noteworthy implications of the digitization movement. A myriad of digitation-enabling processes, platforms, products, patterns, and practices are hitting the market. There is a kind of convergence blurring the boundaries of physical and virtual worlds. Further on, all kinds of physical, mechanical, electrical, and electronics systems are adequately empowered to be instrumented and interconnected to exhibit intelligent behavior. Further on, all kinds of connected devices and digitized objects in our everyday environments are systematically integrated with remotely held and cloud-hosted applications, services, and data sources in order to be adaptive in their deeds, decisions, and deliveries.

Modeling and simulation of scientific workflow play a vital role in resource allocation in a distributed environment. Simulation is one of the methods to solve the complex scientific workflows in distributed environment. There are many scientific workflow simulation software frameworks that are available for grid and cloud environment. WorkflowSim is an open-source simulator. WorkflowSim Simulator extends the existing CloudSim Simulator. The architecture, components, and scheduling algorithms used and also the simulation results are explained for CloudSim Simulator and WorkflowSim Simulator.

Cloud infrastructures typically offer access to boundless virtual resources dynamically provisioned on demand for hosting, running, and managing a variety of mission-critical applications like scientific workflows, big data processing application, business intelligence-based applications, high-performance computing (HTC), and high transaction computing (HTC). Due to the surging popularity of the irresistible cloud idea, there are cloud datacenters spreading across the globe comprising heterogeneous cloud platforms and infrastructures catering to fast-evolving demands of worldwide businesses. The pervasive connectivity has enabled for the unprecedented success of the cloud concept. However, intensive automation is the key to the originally intended success of the cloud paradigm. Researchers across the world are focusing on unearthing powerful and pioneering tools and techniques for automated infrastructure life-cycle management. Similarly there are pathbreaking work-around approaches, algorithms, and architectures for workload consolidation. In short, there are many cloud-related aspects yearning for technologically sound automation, acceleration, and augmentation capabilities.

With the faster adoption of the cloud idea across industry verticals with all the elegance and the enthusiasm, the traditional IT is bound to enlarge its prospects and potentials. Thatis, the IT capabilities and capacities are being enhanced with the seamless and spontaneous association with the cloud paradigm in order to meet up fast-emerging and evolving business requirements. This is a kind of new IT getting enormous attention and garnering a lot of attraction among business executives and IT professionals lately. The systematic amalgamation of the cloud concepts with the time-tested and trusted enterprise IT environment is to deliver a bevy of significant advantages for business houses in the days ahead. This model of next-generation computing through the cognitive and collective leverage of enterprise and cloud IT environments is being touted as the hybrid IT. There are a variety of technologies and tools expressly enabling the faster realization of hybrid era. This chapter is specially crafted for digging deep and describing the various implications of the hybrid IT.

Today’s scientific application requires tremendous amount of computation-driven as well as data-driven supported resources. The scientific applications are represented as workflows. The workflow management systems are designed and developed to depict the workflows of complex nature. The workflow management systems are able to reliably and efficiently coordinate among various resources in a distributed environment. This chapter describes various workflow management software like Kepler, Taverna, Triana, Pegasus, and Askalon. The architecture and functionalities of these workflow management systems are explained in the following sections.

Workflow execution employs predictive analytics to extract significant, unidentified as well as precious insights from several stages of execution. Further, the operational analytics integrates these valuable insights directly into decision engine which enables analytical as well as machine learning-driven decision-making for an efficient workflow execution. This chapter highlights several analytical and machine learning approaches that are practiced in workflow predictions. Additionally, it explains the significance of hybrid approach which includes both analytical and machine learning models for workflow prediction. Finally, it describes the hybrid approach employed in PANORAMA architecture using two workflow applications.