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A huge volume of data are being generated from multiple sources, including smart cities, the IoT devices, scientific modeling, or different big data simulations; but also from users' daily activities. These daily new data are added to historical repositories, providing the huge and complex universe of the digital data. Recently, the Fog-to-Cloud (F2C) data management architecture is envisioned to handle all big data complexities, from IoT devices (the closest layer to the users) to cloud technologies (the farthest layer to the IoT devices), as well as different data phases from creation to usage from fog to cloud scenario. Moreover, the F2C data management architecture can have several benefits from the combined advantages of fog (distributed) and cloud (centralized) technologies including reducing network traffic, reducing latencies drastically while improving security. In this paper, we have several novel contributions. First, we described the previous studies of the Zero Emission Buildings (ZEB) in the context of the data flow and movement architecture. Second, we have proposed Zero Emission Neighbourhoods (ZEN) data management architecture for smart city scenarios based on a distributed hierarchical F2C data management. Indeed, we used the 6Vs big data challenges (Volume, Variety, Velocity, Variability, Veracity, and Value) for evaluating the data management architectures (including ZEB and ZEN). The result of the evaluation shows that our proposed ZEN data management architecture can address 6Vs challenges and is able to manage the data lifecycle from its production up to its usage.

Smart city solutions make a high-level technological innovation in a city to expand their citizens' quality of life by several different technological management strategies (such as resource management and data management) among end-users, city planners, and technological devices (e.g., sensors, IoT devices, etc.). Currently, data management architectures have been offered by some researchers to organize obtained data in smart cities, including Centralized Data Management (CDM) and Distributed-to-Centralized Data Management (D2C-DM). In addition, the D2C-DM architecture can provide several advantages from the combined advantages of distributed (e.g., Fog) and centralized (e.g., Cloud) technologies, such as reducing network traffic and their latencies, upgrading the security levels and so on. In this paper, we propose several novel contributions. First, we design a D2C-DM with Fog, cloudlet, and Cloud technologies to organize huge amounts of the data production in smart city scenarios, from physical devices (including IoT and sensors devices) to non-physical devices (including third-party applications, and other databases). Second, we tailor our proposed D2C-DM architecture with the Zero Emission Neighborhoods (ZEN) scenario to manage its different data types, including context, research, and Key Performance Indicator (KPI) data. Finally, the advantages of this D2C-DM architecture are discussed.

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Abstract-There is a vast amount of data being generated every day in the world, coming from a variety of sources, with different formats, quality levels, etc. This new data, together with the archived historical data, constitute the seed for future knowledge discovery and value generation in several fields of eScience. Discovering value from data is a complex computing process where data is the key resource, not only during its processing, but also during its entire life cycle. However, there is still a huge concern about how to organize and manage this data in all fields, and at all scales, for efficient usage and exploitation during all data life cycles. Although several specific Data LifeCycle (DLC) models have been recently defined for particular scenarios, we argue that there is no global and comprehensive DLC framework to be widely used in different fields. For this reason, in this paper we present and describe a comprehensive scenario agnostic Data LifeCycle (COSA-DLC) model successfully addressing all challenges included in the 6Vs, namely Value, Volume, Variety, Velocity, Variability and Veracity, not tailored to any specific environment, but easy to be adapted to fit the requirements of any particular field. We conclude that a comprehensive scenario agnostic DLC model provides several advantages, such as facilitating global data organization and integration, easing the adaptation to any kind of scenario, guaranteeing good quality data levels, and helping save design time and efforts for the research and industrial communities.

A huge amount of data is constantly being produced in the world. Data coming from the IoT, from scientific simulations, or from any other field of the eScience, are accumulated over historical data sets and set up the seed for future Big Data processing, with the final goal to generate added value and discover knowledge. In such computing processes, data are the main resource; however, organizing and managing data during their entire life cycle becomes a complex research topic. As part of this, Data LifeCycle (DLC) models have been proposed to efficiently organize large and complex data sets, from creation to consumption, in any field, and any scale, for an effective data usage and big data exploitation.Several DLC frameworks can be found in the literature, each one defined for specific environments and scenarios. However, we realized that there is no global and comprehensive DLC model to be easily adapted to different scientific areas. For this reason, in this paper we describe the Comprehensive Scenario Agnostic Data LifeCycle (COSA-DLC) model, a DLC model which: i) is proved to be comprehensive as it addresses the 6Vs challenges (namely Value, Volume, Variety, Velocity, Variability and Veracity; and ii), it can be easily adapted to any particular scenario and, therefore, fit the requirements of a specific scientific field. In this paper we also include two use cases to illustrate the ease of the adaptation in different scenarios. We conclude that the comprehensive scenario agnostic DLC model provides several advantages, such as facilitating global data management, organization and integration, easing the adaptation to any kind of scenario, guaranteeing good data quality levels and, therefore, saving design time and efforts for the scientific and industrial communities. CCS Concepts• Information systems➝Data management systems • Computer systems organization➝Architectures➝Other architectures➝ Data flow architectures • Software and its engineering➝ Software system structures➝ Data flow architectures

Abstract-Smart Cities are the most challenging and promising technological solutions for absorbing the increasing pressure of population growth, while simultaneously enforcing a sustainable economic progress as well as a higher quality of life. Several technologies are involved in a potential Smart City deployment, although data are the fuel to achieve the demanded and mandatory smartness. Data can be obtained from multiple sources, in large quantities, and with a variety of formats, therefore, an appropriate management is critical for their effective usage. Data life cycle models constitute an effective trend towards developing an integral and efficient data management framework, from data creation to data consumption and removal. In this paper we present the Smart City Comprehensive Data LifeCycle (SCC-DLC) model, a data management architecture generated from a comprehensive scenario agnostic model, tailored for the particular scenario of Smart Cities. We define the management of each data life phase, and describe its implementation on a Smart City with Fog-toCloud (F2C) resources management, an architecture that combines the advantages of both cloud and fog strategies.

Abstract-Smart cities are the current technological solutions to handle the challenges and complexity of the growing urban density. Traditionally, smart city resources are managed with a cloud based solution, where sensors and devices are connected to provide a centralized and rich set of open data. The advantages of cloud based frameworks are their ubiquity, as well as an (almost) unlimited resources capacity. However, accessing data from the cloud implies large network traffic, high latencies usually not appropriate for realtime or critical solutions, as well as higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. It proposes the use of devices at the edge to provide closer computing facilities and, therefore, reducing network traffic, reducing latencies drastically while improving security. In this work, we present a new framework for data management in the context of a smart city through a global fog to cloud resources management architecture. We show this model has the advantages of both, fog and cloud technologies, as it allows reduced latencies for critical applications while being able to use the high computing capabilities of cloud technology. As a first experiment, we estimate the network traffic in this model during data collection and compare it with a traditional real system.