Evolution of IoT-enabled connectivity and applications in automotive industry: A review

With the recent advances in cloud computing and the capabilities of mobile devices, the state-of-the-art of mobile computing is at an inflection point, where compute-intensive applications can now run on today’s mobile devices with limited computational capabilities. This is achieved by using the communications capabilities of mobile devices to establish high-speed connections to vast computational resources located in the cloud. While the execution scheme based on this mobile-cloud collaboration opens the door to many applications that can tolerate response times on the order of seconds and minutes, it proves to be an inadequate platform for running applications demanding real-time response within a fraction of a second. In this chapter, the authors describe the state-of-the-art in mobile-cloud computing as well as the challenges faced by traditional approaches in terms of their latency and energy efficiency. They also introduce the use of cloudlets as an approach for extending the utility of mobile-cloud computing by providing compute and storage resources accessible at the edge of the network, both for end processing of applications as well as for managing the distribution of applications to other distributed compute resources. Accelerating MobileCloud Computing: A Survey

Accelerating Mobile-Cloud Computing: A Survey

We first describe a metric for uncertain probabilities called opinion, and subsequently a set of logical operators that can be used for logical reasoning with uncertain propositions. This framework which is called subjective logic uses elements from the Dempster-Shafer belief theory and we show that it is compatible with binary logic and probability calculus.

A logic for uncertain probabilities

Job scheduling is a necessary prerequisite for performance optimization and resource management in the cloud computing system. Focusing on accurate scaled cloud computing environment and efficient job scheduling under Virtual Machine (VM) resource and Server Level Agreement (SLA) constraints, we introduce the architecture of cloud computing platform and optimization job scheduling scheme in this study. The system model is comprised of clearly defined separate constituent parts, including portal, job scheduler, and resources pool. By analyzing the execution process of user jobs, we designed a novel job scheduling scheme based on reinforcement learning to minimize the makespan and Average Waiting Time (AWT) under the VM resource and deadline constraints, and employ parallel multi-age parallel technologies to balance the exploration and exploitation in learning process and accelerate the convergence of Q -learning algorithm. Both simulation and real cloud platform experiment results demonstrate the efficiency of the proposed job scheduling scheme.

A Reinforcement Learning-Based Mixed Job Scheduler Scheme for Grid or IaaS Cloud

The applications of artificial intelligence in education have increased in recent years. However, further conceptual and methodological understanding is needed to advance the systematic implementation of these approaches. The first objective of this study is to test a systematic procedure for implementing artificial neural networks to predict academic performance in higher education. The second objective is to analyze the importance of several well-known predictors of academic performance in higher education. The sample included 162,030 students of both genders from private and public universities in Colombia. The findings suggest that it is possible to systematically implement artificial neural networks to classify students’ academic performance as either high (accuracy of 82%) or low (accuracy of 71%). Artificial neural networks outperform other machine-learning algorithms in evaluation metrics such as the recall and the F1 score. Furthermore, it is found that prior academic achievement, socioeconomic conditions, and high school characteristics are important predictors of students’ academic performance in higher education. Finally, this study discusses recommendations for implementing artificial neural networks and several considerations for the analysis of academic performance in higher education.

Artificial neural networks in academic performance prediction: Systematic implementation and predictor evaluation

Background/Objectives: To evaluate the prediction accuracy of Neural Network algorithms for host CPU load prediction and evaluate their performance compared to actual values. Methods/Statistical Analysis: The speed of execution of job at the scheduled host is directly proportional to its CPU load. Therefore, target node load prediction plays an important role in job scheduling decisions. It is learnt that Neural Networks are capable of predicting the future values based on the training given on the past data. We designed a multilayer neural network and trained with learning algorithms for the input patterns collected from the load traces and predicted the future load statistics. The Mean and Standard Deviation of the predicted values are computed and analyzed against the Mean and Standard Deviation of actual values for all the ANN algorithms. Findings: We analyzed the prediction accuracy of Back Propagation, Quick Propagation, Back Propagation with Momentum and Resilient Propagation algorithm for the load traces collected from variety of computers connected in a network. Existing reports shows that Back Propagation algorithm exhibits better prediction accuracy compared to statistical approaches like linear regression and polynomial regression. In this paper, we have shown that Resilient Propagation algorithm has better prediction accuracy compared to other ANN algorithms. Application/Improvements: Job scheduling and resource selection algorithms can employ neural network algorithms to predict the load for the sharable resources connected in the network for more accurate and faster scheduling/resource selection decision.

A Comparative Study on CPU Load Predictions in a Computational Grid using Artificial Neural Network Algorithms

Cloud computing offers internet-based services to access various on-demand resources by overcoming the necessity of centralized computing. There are plenty of challenges, like privacy, security, load balancing, resource provisioning, existing in this virtualized environment. Among them, security is one of the major complications. Concerning the security and privacy issues, distributed denial of service (DDoS) attacks can cause great damage to the availability of cloud services and resources. Till now, attack mitigation strategies residues is an on-going research challenge due to the attack tendency to advance in sophistication and ease of implementation. Moreover, cloud service customers, as well as providers, need to be observant in understanding the menaces of DDoS attacks. The crux of such attack mitigation mechanism is to assure early and fast detection of illegitimate entries into the network. To address such monitoring and detecting mechanism, this paper aims to address various nature-inspired algorithms which are having the propensity to resolve malware issues, such as DDoS attacks, naturally by offering an optimized solution.

A Review of Biologically Inspired Algorithms in a Cloud Environment to Combat DDoS Attacks

Smart home is a practical technique to build the simplicity of life. It can be utilized to give assistance and fulfill the necessities of the elderly and the handicapped at houses. Home automation framework will enhance the ordinary living status at houses. The aim of this paper is to implement a central control framework which utilizes a remote Bluetooth gadget and gives wireless access to smart phones. This framework is intended to control electrical gadgets all through the house with ease of installing it, ease of use, and cost-effective design and implementation.

Design of Smart Home Using Internet of Things

Desktop grid aims to harvest a number of idle desktop computers owned by individuals on the edge of internet. Now days, Desktop grids are gaining increasing popularity because of the advances in the technology and availability of high computing power from the desktops. Volunteer nodes in a Desktop Grid encounter two types of failures: volatility failure and interference failure. Volatile failures are due to periodic maintenance, machine breakdown, system crash or shutdown etc that make node inaccessible to the Desktop Grid user. Interference failures are due to volunteer autonomic nature that the node owner can withdraw participation from public execution due to the need to execute the private jobs. This makes the node inaccessible to the Desktop Grid user and may cause partial or entire loss of the public job execution. Volunteer interferences cause slowdown in the execution of the jobs. In this paper the author present a job scheduling algorithm that analyze the nature of volunteer interference failures for effective scheduling of jobs.

Dynamic Job Scheduling Strategy for Unreliable Nodes in a Volunteer Desktop Grid

Objective: To create awareness about the breast cancer which has become one of the most common diseases among women that leads to death if not recognized at early stage. Methods: The technique of acquiring breast image is called mammography and is a diagnostic and screening tool to detect cancer. A cascade algorithm based on these statistical parameters is implemented on these mammogram images to segregate normal, benign, and malignant diseases. R esults: Statistical features - such as mean, median, standard deviation, perimeter, and skewness - were extracted from mammogram images to describe their intensity and nature of distribution using ImageJ. C onclusion: A noninvasive technique which includes statistical features to determine and classify normal, benign, and malignant images are identified. Ke ywords: Breast cancer, Benign, Malignant , Mammogram image, ImageJ.

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Shaik Naseera

Papers

A Comparative Study on CPU Load Predictions in a Computational Grid using Artificial Neural Network Algorithms

A Review of Biologically Inspired Algorithms in a Cloud Environment to Combat DDoS Attacks

Design of Smart Home Using Internet of Things

Dynamic Job Scheduling Strategy for Unreliable Nodes in a Volunteer Desktop Grid

Statistical detection of breast cancer by mammogram image