Exploring Energy Consumption Issues for video Streaming in Mobile Devices: a Review

The proliferation of high-end mobile devices, such as smart phones, tablets, together have gained the popularity of multimedia streaming among the user. It is found from various studies and survey that at end of 2020 mobile devices will increase drastically and Mobile video streaming will also grow rapidly than overall average mobile traffic. The streaming application in Smartphone heavily depends on the wireless network activities substantially amount of data transfer server to the client. Because of very high energy requirement of data transmitted in wireless interface for video streaming application considered as most energy consuming application. Therefore to optimize the battery USAge of mobile device during video streaming it is essential to understand the various video streaming techniques and there energy consumption issues in different environment. In this paper we explore energy consumption in mobile device while experiencing video streaming and examine the solution that has been discussed in various research to improve the energy consumption during video streaming in mobile devices . We classify the investigation on a different layer of internet protocol stack they utilize and also compare them and provide proof of fact that already exist in modern Smartphone as energy saving mechanism

Power module Data Management System (DMS) study

Computer trades and analyses of selected Power Module Data Management Subsystem issues to support concurrent inhouse MSFC Power Study are provided. The charts which summarize and describe the results are presented. Software requirements and definitions are included

Towards a human-centric data economy

Spurred by widespread adoption of artificial intelligence and machine learning, “data” is becoming a key production factor, comparable in importance to capital, land, or labour in an increasingly digital economy. In spite of an ever-growing demand for third-party data in the B2B market, firms are generally reluctant to share their information. This is due to the unique characteristics of “data” as an economic good (a freely replicable, non-depletable asset holding a highly combinatorial and context-specific value), which moves digital companies to hoard and protect their “valuable” data assets, and to integrate across the whole value chain seeking to monopolise the provision of innovative services built upon them. As a result, most of those valuable assets still remain unexploited in corporate silos nowadays. This situation is shaping the so-called data economy around a number of champions, and it is hampering the benefits of a global data exchange on a large scale. Some analysts have estimated the potential value of the data economy in US$2.5 trillion globally by 2025. Not surprisingly, unlocking the value of data has become a central policy of the European Union, which also estimated the size of the data economy in 827C billion for the EU27 in the same period. Within the scope of the European Data Strategy, the European Commission is also steering relevant initiatives aimed to identify relevant cross-industry use cases involving different verticals, and to enable sovereign data exchanges to realise them. Among individuals, the massive collection and exploitation of personal data by digital firms in exchange of services, often with little or no consent, has raised a general concern about privacy and data protection. Apart from spurring recent legislative developments in this direction, this concern has raised some voices warning against the unsustainability of the existing digital economics (few digital champions, potential negative impact on employment, growing inequality), some of which propose that people are paid for their data in a sort of worldwide data labour market as a potential solution to this dilemma [114, 115, 155]. From a technical perspective, we are far from having the required technology and algorithms that will enable such a human-centric data economy. Even its scope is still blurry, and the question about the value of data, at least, controversial. Research works from different disciplines have studied the data value chain, different approaches to the value of data, how to price data assets, and novel data marketplace designs. At the same time, complex legal and ethical issues with respect to the data economy have risen around privacy, data protection, and ethical AI practices. In this dissertation, we start by exploring the data value chain and how entities trade data assets over the Internet. We carry out what is, to the best of our understanding, the most thorough survey of commercial data marketplaces. In this work, we have catalogued and characterised ten different business models, including those of personal information management systems, companies born in the wake of recent data protection regulations and aiming at empowering end users to take control of their data. We have also identified the challenges faced by different types of entities, and what kind of solutions and technology they are using to provide their services. Then we present a first of its kind measurement study that sheds light on the prices of data in the market using a novel methodology. We study how ten commercial data marketplaces categorise and classify data assets, and which categories of data command higher prices. We also develop classifiers for comparing data products across different marketplaces, and we study the characteristics of the most valuable data assets and the features that specific vendors use to set the price of their data products. Based on this information and adding data products offered by other 33 data providers, we develop a regression analysis for revealing features that correlate with prices of data products. As a result, we also implement the basic building blocks of a novel data pricing tool capable of providing a hint of the market price of a new data product using as inputs just its metadata. This tool would provide more transparency on the prices of data products in the market, which will help in pricing data assets and in avoiding the inherent price fluctuation of nascent markets. Next we turn to topics related to data marketplace design. Particularly, we study how buyers can select and purchase suitable data for their tasks without requiring a priori access to such data in order to make a purchase decision, and how marketplaces can distribute payoffs for a data transaction combining data of different sources among the corresponding providers, be they individuals or firms. The difficulty of both problems is further exacerbated in a human-centric data economy where buyers have to choose among data of thousands of individuals, and where marketplaces have to distribute payoffs to thousands of people contributing personal data to a specific transaction. Regarding the selection process, we compare different purchase strategies depending on the level of information available to data buyers at the time of making decisions. A first methodological contribution of our work is proposing a data evaluation stage prior to datasets being selected and purchased by buyers in a marketplace. We show that buyers can significantly improve the performance of the purchasing process just by being provided with a measurement of the performance of their models when trained by the marketplace with individual eligible datasets. We design purchase strategies that exploit such functionality and we call the resulting algorithm Try Before You Buy, and our work demonstrates over synthetic and real datasets that it can lead to near-optimal data purchasing with only O(N) instead of the exponential execution time - O(2N) - needed to calculate the optimal purchase. With regards to the payoff distribution problem, we focus on computing the relative value of spatio-temporal datasets combined in marketplaces for predicting transportation demand and travel time in metropolitan areas. Using large datasets of taxi rides from Chicago, Porto and New York we show that the value of data is different for each individual, and cannot be approximated by its volume. Our results reveal that even more complex approaches based on the “leave-one-out” value, are inaccurate. Instead, more complex and acknowledged notions of value from economics and game theory, such as the Shapley value, need to be employed if one wishes to capture the complex effects of mixing different datasets on the accuracy of forecasting algorithms. However, the Shapley value entails serious computational challenges. Its exact calculation requires repetitively training and evaluating every combination of data sources and hence O(N!) or O(2N) computational time, which is unfeasible for complex models or thousands of individuals. Moreover, our work paves the way to new methods of measuring the value of spatio-temporal data. We identify heuristics such as entropy or similarity to the average that show a significant correlation with the Shapley value and therefore can be used to overcome the significant computational challenges posed by Shapley approximation algorithms in this specific context. We conclude with a number of open issues and propose further research directions that leverage the contributions and findings of this dissertation. These include monitoring data transactions to better measure data markets, and complementing market data with actual transaction prices to build a more accurate data pricing tool. A human-centric data economy would also require that the contributions of thousands of individuals to machine learning tasks are calculated daily. For that to be feasible, we need to further optimise the efficiency of data purchasing and payoff calculation processes in data marketplaces. In that direction, we also point to some alternatives to repetitively training and evaluating a model to select data based on Try Before You Buy and approximate the Shapley value. Finally, we discuss the challenges and potential technologies that help with building a federation of standardised data marketplaces. The data economy will develop fast in the upcoming years, and researchers from different disciplines will work together to unlock the value of data and make the most out of it. Maybe the proposal of getting paid for our data and our contribution to the data economy finally flies, or maybe it is other proposals such as the robot tax that are finally used to balance the power between individuals and tech firms in the digital economy. Still, we hope our work sheds light on the value of data, and contributes to making the price of data more transparent and, eventually, to moving towards a human-centric data economy.This work has been supported by IMDEA Networks InstitutePrograma de Doctorado en Ingeniería Telemática por la Universidad Carlos III de MadridPresidente: Georgios Smaragdakis.- Secretario: Ángel Cuevas Rumín.- Vocal: Pablo Rodríguez Rodrígue

Business strategy and information systems alignment : a study of the use of enterprise architectures in Australian Government

This thesis investigates the use of Enterprise Architectures ("the logical structuring and classification of descriptive representations of an enterprise") as enablers of alignment between business strategy and information systems in public sector agencies. The scope of this study has been shaped by Australian government policies that have set firm directions for the delivery of community products and services in the electronic domain. Foundation management and information systems theories, empirical studies and public management literature -have been used extensively in grounding this research study. A substantial body of literature has been reviewed, and this study positioned in the context of these prior literary works. In particular, the principal alignment theories have been adopted and the research model developed from the published works of eminent management and information systems researchers. The primary research question asks whether Enterprise Architectures are enablers of business strategy and information systems alignment, and if so, what are the associated alignment enabling processes? The study's four research themes are: (i) Enterprise Architecture frameworks and methods; (ii) architectural completeness; (iii) the social aspects of alignment (management support, business planning style, business plan communications); and (iv) the formal high level alignment mechanisms used by public agencies. The study has used an exploratory qualitative case_study research method that includes semi-structured and unstructured interviews, archival research and document discovery, public announcement and presentation information, organisational observations, and system demonstrations for the collection and triangulation of data. The case studies at four government agencies are presented as metastories of how Enterprise Architectures and other alignment mechanisms are used within the contextual frame of each public organisation. The research shows that Enterprise Architectures can be enablers of alignment within a public organization environment. Architectures possess the ability to define and describe the states of the agency business and technology domains, and the intimate domain relationships and processes that inform the agency's state of alignment. Changes in the agencies or their operating environments are reflected in the architecture and its subsequent evolutionary changes (such as new business requiring new supporting information systems and technology). Enterprise Architectures were considered as important enablers of alignment with each agency dedicating specialist corporate resources for architecture development and maintenance. The case studies showed that the origin (either internally developed or commercially acquired) of the agency Enterprise Architecture was not necessarily important for the enabling of alignment. However, organizations would do well to concentrate their resources on developing and implementing architectures that accurately represent and integrate the agency business and technology domains. The research used an architectural requirements framework, adapted from an International Standard (ISO 15704), to gauge architecture completeness. The study found that substantially complete architectures integrated the business and information systems entities, included the necessary components (such as the governance frameworks) to achieve strategic alignment, and offered opportunities for agency alignment. Architectures that were deficient in their business, technology or managerial orientations could display reduced clarity of the business and technology states, placing the organisations at risk of misalignment. The case research allowed the comparison of centralised and decentralised agency business structures and information systems, allowing explanations to be developed for the longer architecture implementation periods, and reduced architecture completeness at the decentralised agencies. In particular, the research findings point to the non-uniform application of decentralised resources, and the reduced corporate visibility of decentralised systems, as reasons for long architecture implementation periods, reduced completeness, and impaired alignment. The case studies identified that architectures develop and evolve over time and possess specific characteristics that assist the alignment process. Architectures acted as focal points for business entities and processes that are enabled by the supporting information systems. Architectures provided a mechanism for information systems and technology governance that jointly support business and information systems requirements. Architectures enabled agency information structuring and sharing for the support of business operations. Architectures supported the reuse of systems and technologies for the delivery of business strategies and plans. Other characteristics, such as using architecture as a corporate philosophy, were agency-specific and reflected the agency's culture, people, business capabilities, and corporate history. The detailed examination of management support, business planning styles and business plan communications, showed that the social aspects of alignment were important. In particular the study showed that executive managers must support business and technical directions through demonstrable understanding of the important business and information systems issues, and cohesive decision-making that is built on sound relationships between business and technically oriented executives. The case studies also showed that business plans that are horizontally and vertically integrated, and are well communicated and understood by stakeholders, assisted the enabling of alignment. Finally, the study uncovered several formal alignment mechanisms (such as corporate boards, agency plans, balanced score cards) that were consistent with alignment and governance theory and government management literature. The findings of the case research placed this study of alignment in a process or system frame, while empirically demonstrating that alignment is a continuous and dynamic process that combines several enabling mechanisms. The study showed that any research or conceptual analysis of alignment should consider the alignment mechanisms to operate in combination with each other. Future directions for alignment and architecture research were also described

Network control for a multi-user transputer-based system.

A dissertation submitted to the Faculty of Engineering, University of the Witwatersrand, Johannesburg, in fulfilment of the requirements for the degree of Master of Science in EngineeringThe MC2/64 system is a configureable multi-user transputer- based system which was designed using a modular approach. The MC2/64 consists of MC2 Clusters which are connected using a modified Clos network. The MC2 Clusters were designed and realised as completely configurable modules using and extending an algorithm based on Eulerian cycles through a requested graph. This dissertation discusses the configuration algorithm and the extensions made to the algorithm for the MC2 Clusters. The total MC2/64 system is not completely configurable as a MC2 Cluster releases only a limited number of links for inter-cluster connections. This dissertation analyses the configurability of MC2/64, but also presents algorithms which enhance the usability of the system from the user's point of view. The design and the implementation of the network control software are also submitted as topics in this dissertation. The network control software must allow multiple users to use the system, but without them influencing each other's transputer domains. This dissertation therefore seeks to give an overview of network control problems and the solutions implemented in current MC2/64 systems. The results of the research done for this dissertation will hopefully aid in the design of future MC2 systems which will provide South Africa with much needed, low cost, high performance computing power.Andrew Chakane 201

The way from Lean Product Development (LPD) to Smart Product Development (SPD)

Abstract Lean Product Development (LPD) is the application of lean principles to product development, aiming to develop new or improved products that are successful in the market. LPD deals with the complete process from gathering and generating ideas, through assessing potential success, to developing concepts, evaluating them to create a best concept, detailing the product, testing/developing it and handing over to manufacture. With the beginning of the fourth Industrial Revolution (Industrial 4.0) and the rising efforts to realize a smart factory environment, also product development has to perform a substantial transformation. This paper firstly describes the concept of Lean Product Development as well as new requirements for an intelligent and Smart Product Development (SPD) through the introduction of modern Industry 4.0 related technologies. Based on Axiomatic Design methodology, a set of guidelines for the design of Lean Product Development Processes is presented. These guidelines are linked with concepts from Industry 4.0 in Engineering, showing how a lean and smart product development process can be achieved by the use of advanced and modern technologies and instruments

Evolutionary space platform concept study. Volume 1: Executive summary

The Evolutionary Space Platform Concept Study encompassed a 10 month effort to define, evaluate and compare approaches and concepts for evolving unmanned and manned capability platforms beyond the current Space Platform concepts to an evolutionary goal of establishing a permanent manned presence in space. Areas addressed included: special emphasis trade studies on the current unmanned concept, assessment of manned platform concepts, and utility analysis of a manned platform for defense related missions

Earth Observatory Satellite system definition study. Report no. 3: Design/cost tradeoff studies. Appendix D: EOS configuration design data. Part 2: Data management system configuration

The Earth Observatory Satellite (EOS) data management system (DMS) is discussed. The DMS is composed of several subsystems or system elements which have basic purposes and are connected together so that the DMS can support the EOS program by providing the following: (1) payload data acquisition and recording, (2) data processing and product generation, (3) spacecraft and processing management and control, and (4) data user services. The configuration and purposes of the primary or high-data rate system and the secondary or local user system are explained. Diagrams of the systems are provided to support the systems analysis

Space shuttle low cost/risk avionics study

All work breakdown structure elements containing any avionics related effort were examined for pricing the life cycle costs. The analytical, testing, and integration efforts are included for the basic onboard avionics and electrical power systems. The design and procurement of special test equipment and maintenance and repair equipment are considered. Program management associated with these efforts is described. Flight test spares and labor and materials associated with the operations and maintenance of the avionics systems throughout the horizontal flight test are examined. It was determined that cost savings can be achieved by using existing hardware, maximizing orbiter-booster commonality, specifying new equipments to MIL quality standards, basing redundancy on cost effective analysis, minimizing software complexity and reducing cross strapping and computer-managed functions, utilizing compilers and floating point computers, and evolving the design as dictated by the horizontal flight test schedules

FY 1974 scientific and technical reports, articles, papers, and presentations

Formal NASA technical reports and papers published in technical journals, and presentations by MSFC personnel during FY 1974 are presented. Papers from MSFC contractors are also included