Nowadays, Smart Cities are positioned as one of the most challenging and important research topics, highlighting major changes in people's lifestyle. Technologies such as smart energy, smart transportation or smart health are being designed to improve citizen's quality of life. Smart Cities leverage the deployment of a network of devices - sensors and mobile devices-, all connected through different means and/or technologies, according to their network availability and capacities, setting a novel framework feeding end-users with an innovative set of smart services. Aligned to this objective, a typical Smart City architecture is organized into layers, including a sensing layer (generates data), a network layer (moves the data), a middleware layer (manages all collected data and makes it ready for usage) and an application layer (provides the smart services benefiting from this data). In this paper a real Smart City is analyzed, corresponding to the city of Barcelona, with special emphasis on the layers responsible for collecting the data generated by the deployed sensors. The amount of daily sensors data transmitted through the network has been estimated and a rough projection has been made assuming an exhaustive deployment that fully covers all city. Finally, we discuss some solutions to both reduce the data transmission and improve the data management.

A vast amount of data is constantly being produced in the world from several sources, including the IoT at smart cities. Such complex universe of digital data is being set up through these daily accumulated fresh data over other historical repositories. This data can then be used in different forms and then be reused in further processes, hence, drawing the life cycle of data. Conventionally, the smart city resources management are based on cloud models where sensors data are collected to prepare a centralized and rich set of open data. Cloud-based frameworks provide several benefits, consisting of their ubiquity and (almost) unlimited resources capacity. But accessing data from the cloud results in large network traffics, high latencies and are not recommended for real-time solutions. In order to solve this, fog computing has been defined as a promising technology. It uses devices at the edge to perform closer computation and, therefore, reduces network traffic, decreases latencies, while enhancing security levels. In this paper we present the Data Preservation block, as part of our hierarchical fog-to-cloud F2C data management architecture. 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. Furthermore, we have estimated the amount of collected data to be stored in the storage media, from fog to cloud layers, at the Barcelona smart city, and shown the tremendous advantages of such data organization.

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. 2. 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.Peer ReviewedPostprint (author's final draft

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.

Traditional smart city resources management rely on cloud based solutions to provide a centralized and rich set of open data. The advantages of cloud based frameworks are their ubiquity, (almost) unlimited resources capacity, cost efficiency, as well as elasticity. However, accessing data from the cloud implies large network traffic, high data latencies, and higher security risks. Alternatively, fog computing emerges as a promising technology to absorb these inconveniences. The use of devices at the edge provides closer computing facilities, reduces network traffic and latencies, and improves security. We have defined a new framework for data management in the context of smart city through a global fog to cloud management architecture; in this paper we present the data acquisition block. As a first experiment we estimate the network traffic during data collection, and compare it with a traditional real system. We also show the effectiveness of some basic data aggregation techniques in the model, such as redundant data elimination and data compression.

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-to-Cloud (F2C) resources management, an architecture that combines the advantages of both cloud and fog strategies.