Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

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A Break in the Clouds: Towards a Cloud Definition

The Grid 2: Blueprint for a New Computing Infrastructure

The Internet of Things (IoT) is defined as a paradigm in which objects equipped with sensors, actuators, and processors communicate with each other to serve a meaningful purpose. In this paper, we survey state-of-the-art methods, protocols, and applications in this new emerging area. This survey paper proposes a novel taxonomy for IoT technologies, highlights some of the most important technologies, and profiles some applications that have the potential to make a striking difference in human life, especially for the differently abled and the elderly. As compared to similar survey papers in the area, this paper is far more comprehensive in its coverage and exhaustively covers most major technologies spanning from sensors to applications.

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Internet of Things: Architectures, Protocols, and Applications

Cloud computing environments allow customers to dynamically scale their applications. The key problem is how to lease the right amount of resources, on a pay-as-you-go basis. Application re-dimensioning can be implemented effortlessly, adapting the resources assigned to the application to the incoming user demand. However, the identification of the right amount of resources to lease in order to meet the required Service Level Agreement, while keeping the overall cost low, is not an easy task. Many techniques have been proposed for automating application scaling. We propose a classification of these techniques into five main categories: static threshold-based rules, control theory, reinforcement learning, queuing theory and time series analysis. Then we use this classification to carry out a literature review of proposals for auto-scaling in the cloud.

A Review of Auto-scaling Techniques for Elastic Applications in Cloud Environments

Degradation of air quality in cities is the result of a complex interaction between natural and anthropogenic environmental conditions. With the increase in urbanization and industrialization and due to poor control on emissions and little use of catalytic converters, a great amount of particulate and toxic gases are produced [1]. The objective of this paper is to monitor air pollution on roads and track vehicles which cause pollution over a specified limit. Increasing number of automobiles is a serious problem that has been around for a very long time. This paper proposes use of Internet of Things(IoT)[2] to address this problem. Here, combination of Wireless Sensor Network and Electrochemical Toxic Gas Sensors and the use of a Radio Frequency Identification (RFID) tagging system to monitor car pollution records anytime anywhere.

Vehicular pollution monitoring using IoT

One of the key factors behind successful deployment of Cloud for on demand services is the optimal utilization of its virtual resources. A poorly managed cloud application may lead to huge cost which is even more than the cost of physical deployment. The most important issues in Cloud are the scalability and availability. A highly scalable deployment may lead to poor resource utilization whereas a low scalable deployment may lead to unavailability of services. This paper proposes architecture for optimal utilization of such resources considering both scalability and availability. The proposed architecture, named as Monitoring & Optimizing Virtual Resources (MOVR) architecture, manages and optimizes the usage of the resources required by a cloud application considering auto deployment, auto scaling and auto recovery of the provisioned resources for the application.

Optimizing the utilization of virtual resources in Cloud environment

Data transmission is one of the major challenges in wireless sensor network (WSN). Different routing protocols have been proposed to save energy during data transmission in WSN. Routing protocols based on data-centric approach are suitable in this context that performs in-network aggregation of data to yield energy saving data dissemination. In this paper we propose a modified version of SPIN protocol named M-SPIN and compare its performance with traditional SPIN protocol using broadcast communication, which is a well known protocol as benchmark. We evaluate the M-SPIN protocol using simulation in TOSSIM environment. We find that, M-SPIN exhibits significant performance gains than traditional SPIN routing.

A modified SPIN for wireless sensor networks

With the advent of Smart City, quality of life is bound to be better. But huge need arises to provide proper health-care services as the population is increasingly becoming urban-centric worldwide. The need-gap may be augmented with the help of modern technologies. Providing remote health-care services is a step forward. Successful diagnosis of health problems requires continuous monitoring of several health parameters. Health monitoring devices are power constrained and with limited communication capability. The devices are equipped with powerful microprocessors which are capable enough to take intelligent decisive actions by processing the received data. In order to prevent faster energy dissipation and constrained communication, selective data collection is an option. In this paper, a fuzzy assisted data gathering and alert scheme is proposed for healthcare services. Thus unnecessary waste of energy by transmission of unnecessary information is avoided. We have implemented it using Arduino and eHealth sensor kit.

iHealth: A fuzzy approach for provisioning intelligent health-care system in smart city

In late 2009, Amazon introduced spot instances to offer their unused resources at lower cost with reduced reliability. Amazon's spot instances allow customers to bid on unused Amazon EC2 capacity and run those instances for as long as their bid exceeds the current spot price. The spot price changes periodically based on supply and demand of spot instances, and customers whose bid exceeds it gain access to the available spot instances. Customers may expect their services at lower cost with spot instances compared to on-demand or reserved. However the reliability is compromised since the instances (IaaS) providing the service (SaaS) may become unavailable at any time without any notice to the customer. In this paper, we study various checkpointing schemes to increase the reliability over spot instances. Also we devise a novel checkpointing scheme on top of application-centric resource provisioning framework that increases the reliability while reducing the cost significantly.

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Nandini Mukherjee

Papers

Vehicular pollution monitoring using IoT

Optimizing the utilization of virtual resources in Cloud environment

A modified SPIN for wireless sensor networks

iHealth: A fuzzy approach for provisioning intelligent health-care system in smart city

Application-Centric resource provisioning for amazon EC2 spot instances