Kamil Nešetřil
email: kamil.nesetril@tul.cz

Jan Šembera

Business Intelligence and Geographic Information System for Hydrogeology

Keywords: hydrogeology, groundwater, environmental data management, decision support system (DSS), business intelligence (BI), spatial business intelligence

des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Environmental solutions often lack good analytics and reporting functionality -especially generic functionality that can be easily used for purposes that have not been foreseen at the design and development phase. Business intelligence (BI) and data warehousing is used mainly for integrating and analyzing corporate data. Groundwater data are very different from operational data of enterprises. This paper presents the Hydrogeological Information System HgIS that is based on business intelligence platform Pentaho. Adequacy of using data warehousing concepts and BI for groundwater data management, data analysis and for groundwater modelling is discussed.

Hydrogeological Information System HgIS

HgIS (Fig. 1) is an information system developed at the Technical University of Liberec (Czechia). Its purpose is to load data from the available data sources of any kind, to visualize and analyze data (to support formulation of alternative conceptual mod-els) and to implement simple models based on the data. Although it is focused on groundwater, it is also being used for broader range of environmental data. The data are loaded to the database by the Extract, Transform and Load (ETL) tool Pentaho Data Integration. Data transformations can be implemented without coding through an intuitive graphical user interface and run also in command-line interface, on the ETL server Carte or on Pentaho Business Analytics Server. We implemented the loading data from a chemical laboratory (xBase files), Czech Geological Survey (MS Access), eEarth project (XML files) [8], legacy formats (MS Word documents created by a Geobanka software), specific flat text files, general cross-table (MS Excel) and formats from some other data vendors.

The database structure is based on the data model of the software EnviroInsite (enviroinsite.com) and on national and international standards. It contains 36 tables with data on: the observation objects*, characterization of the geological layers, technical construction of wells, definition of the observed quantities*, action levels, definition of the vertical intervals*, measurements tied to the vertical intervals (e.g. chemical analyses or head measurements)*, measurements tied to specific depth (e.g. geophysical logging) and sampling conditions. Tables containing data noted with asterisk (*) are organized to the snowflake schema. We are using the PostgreSQL (postgresql.org) database management system. Interpretations and non-point data (arcs, polygons etc.) are stored in the PostgreSQL due to the spatial extension PostGIS (postgis.net).

Ordinary users have access only to the online application Pentaho Business Analytics (Fig. 2). It is a BI server that provides dashboards (interactive visualization) and reports (optimized to be printed or saved to the format that the users are familiar to -MS Excel, MS Word or PDF). Reports can be visually designed in the Pentaho Report Designer tool. Community Dashboard Framework (JavaScript) was used to implement dashboards and a map application. The data model of HgIS is based on Envi-roInsite software -therefore exporting data (by Pentaho Data Integration) for advanced hydrogeological visualization (in EnviroInsite) is straightforward. Envi-roInsite has access via ODBC to the data stored in proprietary data model implemented in MS Access or MS Excel. The reports and the exported files can be downloaded from Pentaho Business Analytics web application or sent to the users by e-mail according to a schedule or an event (e.g. user login, new data or new data exceeding an action level).

By some functionality HgIS belongs among "Environmental Data Management Systems" (EDMS). Some of these are EQuIS (earthsoft.com), SiteFX (earthfx.com), GW-Base (ribeka.com), WISKI (kisters.net), EnviroData (geotech.com), Oasismontaj (geosoft.com), HydroManager (waterloohydrogeologic.com) or ESdat (esdat.net). Those tools usually have an excellent graphical user interface and are able to import dozens of data exchange formats. But they are not flexible enough to create new data imports because they do not contain an easy to use highly adaptable extract, transform and load (ETL) module. Without an ETL module they cannot efficiently combine data operations (e.g. aggregation), analyses and simple visualization (reporting). Those shortcomings are overcome by HgIS that takes advantage of the highlevel universal BI tools.

The Hydrogeological Information System HgIS was previously presented at conferences as a tool for environmental data management [START_REF] Nešetřil | Groundwater data management system[END_REF] and for modelling [START_REF] Nešetřil | An information system for groundwater data and modelling[END_REF]. This paper focuses on application of the concepts, technologies and tools of data warehousing and business intelligence in HgIS. Adequacy and limitation of such approach are discussed.

Fig. 2. Report example -a general-purpose report of time development of arbitrary quantitiesgraph and table

2

Data-warehouse and BI Concepts for Groundwater Data

The architecture of the HgIS system corresponds to a spatial business intelligence solution (GeoBI) -a combination of business intelligence (Pentaho) and geographic information systems (PostGIS, the map application, EnviroInsite). BI technologies and tools have not previously been applied to groundwater data. However ESdat (esdat.net) integrates commercial reporting tools -SSRS, Telerik and Crystal Reports.

Telerik is used also in EQuIS (earthsoft.com). MineRP (minerp.com) is based on Microsoft BI tools, but MineRP focuses on mining industry not on groundwater. Boulil et al. [START_REF] Boulil | Multidimensional modeling and analysis of large and complex watercourse data: an OLAP-based solution[END_REF] analyzed quality of surface water (Online Analytical Processing) using Talend a PostgreSQL among others. Kingdon et al. [START_REF] Kingdon | A geodata warehouse: Using denormalisation techniques as a tool for delivering spatially enabled integrated geological information to geologists[END_REF] designed the data warehouse and the application PropBase from 10 operational databases of British Geological Survey. The concept of data warehouse is suitable for environmental data because they contain temporal and spatial dimensions. Groundwater observations and measurements are usually not changed after they are recorded: HgIS is therefore both environmental data management software (operational) and decision support system (Online Analytical Processing).

Different BI components were used in HgIS. Corporate BI solutions use also other components. Selection is discussed in the Table 1. Processing of environmental data differs from processing of business data. We perceive that our approach of processing environmental data differs from the standard data-warehouse and BI applications. The differences are discussed in the following chapters. • HgIS is designed for practitioners to facilitate data management, data analysis and modelling. It is not intended for a countrywide or international data infrastructure. The source data for HgIS are nowadays not in the SQL databases but (purchased and) exported data from such systems. Groundwater data are available in common formats (exchange formats, flat files and reports) and also in legacy (xBase) and poorly structured formats (spreadsheets or even MS Word -in our case fortunately the Word files were generated by a software). • HgIS benefits from using ETL tool by capturing diversity of source-data formats not by huge volume of data (big data) or real-time data (we deal mainly with broad data and long tail data [12]).

Data Warehouse

• HgIS uses a hybrid snowflake/star schema. ─ The spatial dimension is normalized -one observation object (e.g. well or borehole) has multiple monitored depth intervals (e.g. screens or sampled intervals). ─ The dimension of quantities is not normalized because quantities are of diverse nature and consistent sub-dimensions cannot be defined. A quantity is specified by a triad "constituent -media -fraction" (the same way as in the original Envi-roInsite data model). This triad can be both "iron -in water -filtered (dissolved form)" and "precipitation -monthly -maximum". ─ The temporal dimension is a degenerated dimension (a column in the fact table ).

Timestamp column can represent both actual measurement and e.g. a center of the time interval for aggregated values (e.g. monthly average). ─ The dimension of samples contains metadata for a set of measurements (specimen, borehole logging of one quantity in one hole), sampling conditions, sample treating, methods etc. ─ Geology data do not fit to the snowflake/star schema at all. • There are two major data warehousing concepts -by Inmon [START_REF] Inmon | Building the data warehouse[END_REF] (central data warehouse, top-down design) and Kimball [START_REF] Kimball | The data warehouse toolkit: the definitive guide to dimensional modeling[END_REF] (data marts). We found Inmon's paradigm more suitable for groundwater data because its structure (semantics) is known in advance and the volume of data is limited.

─ Inmon defines data warehouse as a subject-oriented, integrated, nonvolatile, and time-variant collection of data in support of management's decisions [START_REF] Inmon | Building the data warehouse[END_REF]. The data warehouse of HgIS matches this definition with slight divergences: o Integrated: HgIS contains the data structure for unknown data due to the flexible design. The flexibility causes inconsistency if used across projects/sites. o Nonvolatile: The database of HgIS contains also:

 Operational information that is not used for decision support (e.g. sample planning).  Some data structures in HgIS were developed to support planned functionality of an expert system. They contain the interpretation of the primary data (observations and measurements) imported by ETL. A hydrogeologist can explore time series and label some values as background or natural condition not influenced by human activity. Those data can be used to compute hydraulic head drawdown and quantify human influence. Primary data are loaded by ETL but further interpretation is to be inserted by the user via graphical user interface.  Auxiliary tables (e.g. for renaming) of HgIS are the part of ETL (not of the data warehouse). o Time-variant: Some descriptive attributes (e.g. well owner, water persistence or purpose) represent only the present state but the values can generally change in time. That information is used only for querying and reporting and not for tracking previous changes. However descriptive attributes can be defined as time-variant quantities -values are stored in the fact table then. ─ HgIS diverges from Inmon's concept in following aspects:

o HgIS stores time-series on all levels of granularity (both original and aggregated) in one star/snowflake schema -because we use only small volume of data. Data of all levels of granularity are necessary for some kind of analysis. o Observations and measurements are loaded directly to the snowflake/star schema. There is not separated data warehouse and dimensional database.

Advantages of the Used Concept

The approach applying data warehousing, BI tools (Pentaho platform) and geographic information systems (GIS) brings many advantages. HgIS is easily extensible and therefore sustainable (maintainable). It can be easily used and even further developed by business user / hydrogeologist. Moreover HgIS is:

• Data agnostic -The developed data model is a simple data structure that is still able to store all relevant data. It is able to store even unknown quantities (new quantities defined in the fact table). • Database agnostic -HgIS is not dependent on a specific database management system because transformation can be performed in Pentaho Data Integration. Pentaho Data Integration is communicating with the database by a universal interface (JDBC, JNDI or ODBC).

• Scalable -HgIS is suitable for both local and server deployment. Data transformation can be parallelized by ETL server Carte (the part of Pentaho Data Integration). • Interoperable -HgIS can be easily integrated with the data analysis tools and the data mining tools (using Pentaho Data Integration -steps: "Weka scoring", "ARFF output", "Tableau data extract" or "Execute R script"). Export routine to a specialized modeling software or implementation of special analysis can be easily developed (using Pentaho Data Integration, Report Designer and Pentaho Business Analytics).

Application and Impact

We have developed the following analyses and the models that are reusable because of their general purpose and connection to the database. Some analyses are utilizing Pentaho Data Integration. Results are stored directly in the database as separate quantities. One of them is computation of the hydrochemical type of water (based on major cations and anions) -e.g. Ca-Mg-HCO3. Some analyses and a model are utilizing Pentaho Data Integration and formulas in Pentaho Reporting (OpenFormula). The results are depicted in the reports:

• Identifying redox processes in ground water from chemical composition (dissolved O2, NO3, Mn 2+ , Fe 2+ , SO4 2-and sulfides) without measured Eh and pH [START_REF] Chapelle | Distinguishing ironreducing from sulfate-reducing conditions[END_REF]. • Multicriterial analysis assessing water quality trends in correspondence to eutrophication.

HgIS is used within 3 Czech national projects that deal with water quality evolution, predicting groundwater resources, water balance of mine pit lake, engineering geology and urban planning. HgIS is used as a part of a decision support system in a state enterprise.

Discussion

Since the previous versions of HgIS, presented in the conference papers [START_REF] Nešetřil | Groundwater data management system[END_REF][START_REF] Nešetřil | An information system for groundwater data and modelling[END_REF], following improvements were made: The architecture was refined and simplified. It is now more systematically based on the Pentaho platform. The first paper [START_REF] Nešetřil | Groundwater data management system[END_REF] was focused on data integration (ETL) while recent versions of HgIS take advantage of the whole BI stack. Recent versions do not include GeoKettle (spatially enabled fork of Pentaho Data Integration) because the latest version of GeoKettle is from the year 2013 and it is based on an outdated version of Pentaho Data Integration. Anyway GeoKettle does not provide precise conversion of spatial reference systems used in Czechia (WGS-84 and S-JTSK). The original map application was implemented in PHP. It was rewritten for the Pentaho platform using Community Dashboard Framework [START_REF] Alves | Community Dashboard Framework[END_REF].

The abstract business layer Pentaho Metadata is used as a data source for the reports, dashboards and the map application (instead of previous direct SQL access). Business user does not query physical data model but has access to e.g. denormalized business tables and columns with predefined format and language localization (similar to database views). Pentaho Metadata is based on the Common Warehouse Metamodel specification [START_REF]Common Warehouse Metamodel (CWM) Specification[END_REF][START_REF] Poole | If we share data, will anyone use them? Data sharing and reuse in the long tail of science and technology[END_REF].

The architecture of the system corresponds to a spatial business intelligence solution (GeoBI) -a combination of business intelligence (BI) and geographic information system (GIS). Therefore it can be used also for geographic analyses and management of big data sets. BI technologies and tools have not been applied for groundwater data before. Groundwater practitioners have worked with GIS software for decades but not with BI tools. Our effort is to introduce BI to the groundwater community. General concept of spatial BI for groundwater data is presented and can be applied using different BI stacks. HgIS is available commercially, upon request (contact the corresponding author).

Screenshots, documentation and background information is available at http://www.dataearth.cz.

Fig. 1 .

 1 Fig. 1. Architecture of HgIS (arrows represent data flow)

Table 1 .

 1 BI components

	Component	Purpose	Used in HgIS
	Operational system, oper-	Data source	Geofond, Geobanka, Lab-
	ational databases, OLTP		systém
	Data staging area	Temporal storage of	No (not much data)
		extracted data
	Operational data store	For data analyses	No (not much data)
	ETL	Data integration	Yes: Pentaho Data Integration
	Enterprise Application	Integration of opera-	No (HgIS is not utilizing op-
	Integration	tional enterprise sys-	erational enterprise systems)
		tems
	Data warehouse	Principal data storage Yes: PostgreSQL
	Data mart	Problem-oriented	No (Central data warehouse is
		data warehouse	sufficient)
	On-line Analytical Pro-	Data storage for	Data warehouse is sufficient
	cessing Cube	analyses	for HgIS

Acknowledgments. The contribution was prepared with support of the Technology Agency of the Czech Republic via the project Nr. TH02030069 (GERIT «Expert system for monitoring, risk assessment and decision support in the field of land use»).


Simon Burkard
email: s.burkard@htw-berlin.de

Frank Fuchs-Kittowski
email: frank.fuchs-kittowski@htw-berlin.de

Sebastian Himberger
email: s.himberger@htw-berlin.de

Fabian Fischer
email: fabian.fischer@htw-berlin.de

Stefan Pfennigschmidt
email: stefan.pfennigschmidt@fokus.fraunhofer.de

Mobile Location-Based Augmented Reality Framework

Keywords: Augmented Reality, Location-based AR, Geo-based AR, AR-SDK 1

ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Introduction

The mass distribution of powerful and easy-to-use mobile devices (smartphones, tablets, etc.) has led to the increased availability and use of location-based services. While location-specific information on mobile devices is often displayed on maps or in lists, an innovative user interface consists of information displayed as an augmented reality (AR) layer over the camera image of the mobile device.

The term "augmented reality" refers to the supplementation of the human visual perception of reality with digital, context-dependent information [START_REF] Azuma | A Survey of Augmented Reality[END_REF]. In mobile augmented reality (mAR), the image from the camera of mobile devices is used to extend the real, local environment of the user by displaying additional digital information in real time [START_REF] Höllerer | Exploring MARS: Developing In-door and Outdoor User Interfaces to a Mobile Augmented Reality System[END_REF]. The tracking method employed to acquire the position and viewing direction of the user (pose) can be used to differentiate between two forms of mAR: in the geo-based approach (also known as location-based AR, or GeoAR), the pose is determined using the built-in GPS sensors and inertial measurement unit (IMU) of the smartphone. In the image-based approach (vision-based AR), the pose as well as objects in the vicinity of the user are identified using optical tracking methods [START_REF] Fuchs-Kittowski | Mobile Erweiterte Realität[END_REF].

Mobile augmented reality has great economic potential. In spite of this, there has been little research and development in the area of location-based AR in recent years. The first available GeoAR software development kits (SDKs) such as Wikitude, Layar, and Metaio, have only a rudimentary range of functionality with few customisation options, or have disappeared completely from the market [START_REF] Rautenbach | Results of an evaluation of augmented reality mobile development frameworks for addresses in augmented reality[END_REF]. Instead, commercial companies as well as researchers have focussed on the development and improvement of vision-based AR (SLAM/3D tracking) in order to achieve the most exact positioning [START_REF] Amin | Comparative Study of Augmented Reality SDK's[END_REF], but mobile vision-based AR is still not ready for the mass market. However, precise positioning is not necessary for many users and areas of application. The disadvantages of location-based AR compared to vision-based AR are therefore acceptable in many cases. The current example of Pokémon Go demonstrates that AR technology that is not based on complex image-based recognition methods can be very successful. However, there are currently no established GeoAR SDKs on the market for the implementation of custom applications without the need for expert knowledge in the areas of AR and computer vision.

In this article, a GeoAR SDK will be presented that supports the custom development of a wide range of GeoAR applications and simplifies and accelerates the development process. The framework is aimed primarily at experienced app developers who wish to create location-based AR applications with their own concrete ideas of functionality and design, but who do not wish to have to acquire expert knowledge in computer vision and AR in order to do so.

The article is structured as follows: starting with a brief introduction to mAR, the functionality of location-based AR is discussed in more detail (Section 2). A distinction is made between location-based and image-based AR, and both technologies are evaluated with regard to robustness and accuracy. Following this, common applications of mobile GeoAR are presented and on their basis, general functional requirements necessary for a wide range of GeoAR applications are derived (Section 3). Comparing these requirements with existing mobile GeoAR SDKs (Section 4), it is found that current SDKs lack certain functionality, and there is a market potential for a novel framework. The concept of such a novel GeoAR framework is detailed in Section 5. Finally, two mAR applications that have been implemented with the framework are presented in Section 6.

Mobile location-based augmented reality

In this section, a brief introduction to mobile augmented reality is given, followed by a discussion of the functionality of location-based AR.

Augmented reality

The term "augmented reality" refers to the enrichment of the human perception of reality with additional, context-dependent, digital information in real-time [START_REF] Azuma | A Survey of Augmented Reality[END_REF]. The user of an AR application is presented with supplementary, virtual information within his/her field of view. This data has a fixed spatial relationship with objects in the real world. For example, computer-generated content can be superimposed on an image of the real world from the camera. This idea is already well established in many areas of everyday life, e.g., in TV broadcasts of soccer matches in which virtual off-side lines or virtual distance measurements are displayed over the real-life field. Augmented reality defines a reality-virtuality continuum. At either end stands complete reality or complete virtuality. In between the extremes lies the realm of mixed reality, which is characterised by different degrees of virtuality. In pure virtual environments, virtual reality (VR), the real surroundings are completely replaced by virtual ones and the user is totally immersed in the virtual world. On the other hand, the representation of additional information is at the forefront of AR; it is merely a supplement to the real world. Although the user must enter ("dive into") the virtual world and therefore interrupt contact with the real world, he/she continues to perceive the real surroundings. The real and virtual world, perceived simultaneously by the user, are unified.

Mobile augmented reality

In recent years, AR technology has become increasingly relevant in the context of mobile devices. In mobile augmented reality, mobile devices are used to merge the real and digital worlds in order to facilitate the perception of real and digital information on the local surroundings [START_REF] Höllerer | Exploring MARS: Developing In-door and Outdoor User Interfaces to a Mobile Augmented Reality System[END_REF]. The real world is viewed through the camera of a mobile device (e.g., a smartphone) and is supplemented in real time with location-specific computer-generated content. The digital, geocoded information is put in spatial context and the view of the world is enriched by its presence. This enables a new way of perceiving places by presenting information from the past, present, or future, e.g., the representation of a building that is no longer/not yet visible. It also allows a better understanding and analysis of digital data on-site, allowing for better decision-making.

For a long time, mAR was a form of fundamental research with only a few expensive specialised applications for the few experts in the field. Today, modern mobile devices are a suitable hardware platform for mAR due to their high performance abilities. In particular, the most important sensors for implementing mAR are already integrated into smartphones. In addition to the camera for recording images, these include the IMU for determining the orientation (rotation) of the device as well as a GPS receiver for roughly determining the position. Moreover, these powerful mobile devices are widespread, user-friendly, and affordable so that mass usage of mAR applications is possible for everyone (workers, citizens, etc.).

In the past several years, mAR applications have been developed for various purposes [START_REF] Adhani | A Survey of Mobile Augmented Reality Applications[END_REF][START_REF] Mehler-Bicher | Augmented Reality -Theorie und Praxis[END_REF], e.g., in Tourism [START_REF] Linaza | Evaluation of Mobile Augmented Reality Applications for Tourism[END_REF] (e.g, for displaying nearby hotels or tourist attractions), medicine [START_REF] Maier-Hein | Towards Mobile Augmented Reality for On-Patient Visualization of Medical Images[END_REF], education [START_REF] Bischoff | Dienste für Smartphones an Universitäten -ein plattformunabhängiges Augmented Reality Campus-Informationssystem für iPhone und Android-Smartphones[END_REF], culture/museums [START_REF] Haugstvedt | Mobile augmented reality for cultural heritage[END_REF], advertising/marketing [START_REF] Stampler | Augmented reality makes shopping more personal -New mobile application from IBM Research helps both consumers and retailers[END_REF][START_REF] Scott | Enabling smart retail settings via mobile augmented reality shopping apps[END_REF] (e.g., for visualising furniture in one's own home in 3D), and entertainment [START_REF] Joseph | Potential perils of peri-Pokémon perambulation: the dark reality of augmented reality?[END_REF] (e.g., Pokémon Go). An example of some of these applications is shown in Figure 2.

Due to the simple nature and widespread availability of mobile devices, the technical basis of mAR, and the numerous potential applications in many different fields, mAR has great economic potential [START_REF] Inoue | Mobile Augmented Reality Business Models[END_REF]. Market research companies predict strong growth in this area in the coming years. For example, Juniper Research estimates the market will be worth over US$6 billion in 2021 [START_REF] Barker | Augmented Reality -Developer & Vendor Strategies 2016-2021[END_REF].

Geo-based (location-based) and image-based AR

In the implementation of mAR applications, it is fundamentally possible to differentiate between two different technologies based on the method employed by the device to determine its own position in 3D space. Establishing the position of the mobile device is an essential requirement for the implementation of AR, as information corresponding to objects in the field of view can only be positioned correctly on the screen given a knowledge of the camera position and projection. The position and pointing of the camera in 3D space is described by six degrees of freedom: three degrees of freedom for the orientation (rotation) and three for the position (translation).

With location-based AR (GeoAR), the rotation is determined solely by the IMU of the device, i.e., by a combination of readings from the gyroscope, accelerometer, and magnetometer. The GPS signal is used to roughly fix the location. This AR technology is based on established, robust, simple technology that is not very CPU-intensive, but it is primarily suitable for outdoor applications and is problematic inside buildings where no GPS signal is available. Additionally, the IMU sensors tend to "drift" during the rotation determination and often the viewing direction cannot be calculated exactly due to local disturbances in the global magnetic field [START_REF] Blum | Smartphone Sensor Reliability for Augmented Reality Applications[END_REF].

Fig. 2. Typical applications of mobile augmented reality: display of nearby points of interest (location-based AR) and visualisation of virtual furniture (image-based AR).

In contrast, image-based AR calculates the position of the camera based solely on an analysis of the camera image using image processing techniques. By recognising prominent points in the video feed from the camera (markers or natural feature tracking), both the rotation and translation of the camera relative to its environment can be determined [START_REF] Marchand | Pose Estimation for Augmented Reality: A Hands-On Survey[END_REF]. Very complex and CPU-intensive algorithms are necessary to interpret and analyse the images. However, in ideal conditions, this method can facilitate very accurate and realistic AR overlays. A major challenge is to cope with external influences and produce acceptable results even with poor lighting conditions (e.g., in darkness), moving objects in the images, or featureless surroundings. Another disadvantage is that this technology does not scale in large environments, such as streets, and does not scale well to various distances to objects. This AR technology is more suited to applications relating to the direct environment of the device, e.g., displaying virtual information about objects in the immediate vicinity (see Figure 2).

The robustness of image-based technology can be further improved by creating 3D models of the environment (model-based AR). The user can then pinpoint him/herself within the virtual model at runtime and further extend the model (SLAM) [START_REF] Lahdenoja | Recent advances in monocular model-based tracking: a systematic literature review[END_REF]. However, other sensors that are not yet built into commercially available smartphones (e.g., infrared sensors for depth measurement or a second camera for stereo vision) are typically required to do this.

Table 1 summarises the above-mentioned key characteristics that determine the reliability and utility of mobile augmented reality approaches (and provides quantitative information from [START_REF] Bae | Fast and scalable structure-from-motion based localization for high-precision mobile augmented reality systems[END_REF]). These key characteristics are: 1) localisation, which determines the users' viewpoint in order to derives real world objects in the current scene and to display the relevant digital information in the correct position, 2) speed of determining the users' position, the relevant information and the visualisation of the information in the correct position, 3) robustness, like dependence on external infrastructure or battery consumption, and the ability to work with dynamically changing environments, 4) scalability, like scaling to larger areas, number of objects, and size of objects. Overall, however, it seems that image-based AR is not ready for the mass market due to its high error rate and lack of robustness. Image-based AR can -under ideal conditions -allow very accurate and realistic AR visualisations, but it is very error-prone. On the other hand, the disadvantages of location-based AR technologies (inaccurate positioning/orientation) are acceptable for many (outdoor) applications. These general advantages and disadvantages of the AR technologies are roughly sketched in Figure 3. In the classical mAR applications, nearby points of interest (POIs) are displayed as markers in the camera overlay. However, location-specific information may not always be just simple markers. Complex 3D models referenced with a geographic coordinate or objects defined by several connected coordinates can be displayed as polylines or polygons. Considering even more complex uses for mAR, four different types of applications can be defined (cf., [START_REF] Jeon | Considerations of Generic Framework for AR on the Web[END_REF], see Figure 4).

Metrics

Fig. 4. Types of applications of location-based AR

Area information. Specific information about the user's environment is displayed as additional AR information in the camera image. The information is displayed when relevant objects are within the field of view of the camera, e.g., tourist attractions, petrol stations, or rivers. Georeferenced data can be presented in very different formats on the image, for example, individual POIs can be displayed as icons (e.g., hotels or tourist attractions), polylines (e.g., rivers), polygons (e.g., flood risk maps), or georeferenced 3D objects (e.g., wind turbines). As an example, the GewässerBB app shows rivers as blue polylines for which hydrological information is available [START_REF] Fuchs-Kittowski | A content platform for smartphone-based mobile augmented reality[END_REF].

Object information. Specific information on a particular object in the immediate environment can visualised as additional AR information, for example, details of building facades, exhibits in an open-air museum, an excavation site, or a product. The content is displayed by superimposing the virtual information on the image of the object when the user points the device at a particular object. For example, certain details, on which further information is available, are highlighted in the AR view of the historical House of Olbrich [START_REF] Keil | The House of Olbrich -An Augmented Reality tour through architectural history[END_REF].

Navigation. AR navigation can be seen as another type of application. While the user proceeds towards a given destination, georeferenced waypoints (or arrows) as well as simple navigational information are shown in the camera image along a route. This provides an alternative form of displaying the information to the classic map-based navigation. An example of such navigation, which also provides the user with ecological information in order to promote sustainable living, is given in [START_REF] Yu | A mobile application for an ecological campus navigation system using augmented reality[END_REF].

Games. maR games present a game as an overlay on the camera view of the real world. Location-specific game elements are represented as AR objects. The playing field is thereby an extension of the real world. Known examples of mAR games are Pokémon Go or Ghost Hunter. In Ghost Hunter, the player hunts ghosts which come through the walls of the room. A ghost-hunter gun, with which the player can target and shoot the ghosts, is displayed in the camera image [START_REF] Armstrong | Ghost Hunter -An Augmented Reality Ghost Busting Game[END_REF].

A large number of different functionalities are required from a developer's perspective in order to individually create such applications. Based on an analysis of the typical (aforementioned) geoAR application types (object information, environmental context information, navigation, and gaming) as well our own existing geoAR applications in the context of • flood management (e.g. flood risk maps, current water levels and flood warnings, historical flood markers, and an educational flood trail) developed in several projects like MAGUN [START_REF] Fuchs-Kittowski | A content platform for smartphone-based mobile augmented reality[END_REF] and • urban disaster and safety management (e.g. incident reporting, navigation in disaster situation) developed in several projects like City.Risks , the following general functional requirements were derived for an SDK envisioned for the development of the widest possible range of applications:

• Presentation of spatial objects with one geographic reference (POI, 3D model) or several geographic references (polyline, polygon). Location-specific objects with a single coordinate (3D model, POI) or several coordinates (polyline, polygon) must be displayed in the correct position on the screen. • Dynamic creation of adaptable content. It must be possible to dynamically generate and flexibly scale the AR content. This means that it must be possible to create and delete objects at runtime. The objects themselves must be as customisable as possible so that the design and appearance of the AR content (size, colour, icon style, etc.) is determined by the user context.

• User interaction with AR objects. The developer should have control over certain user events. These events include, for example, instances when an object is clicked or when objects appear in or disappear from the camera focus. • Access to camera controls and picture. Access to the camera is desirable in order to control the camera or gain access to the camera image, for example, to process or store the current camera image.

SDKs for location-based AR

There are a number of AR SDKs available that are designed to facilitate and accelerate the implementation of AR applications [START_REF] Amin | Comapartive Study of Augmented Reality SDK's[END_REF]. In a market analysis, existing SDKs were systematically studied with regard to their functionality and possible applications in order to determine the extent to which the functional requirements identified above are fulfilled.

Fig. 5. Overview of available SDKs with GeoAR support

In a comprehensive literature analysis, relevant AR SDKs were identified and selected using search engines, link collections (e.g., [START_REF]SocialCompare: Augmented Reality SDK Comparison[END_REF][START_REF]Wikipedia: List of augmented reality software[END_REF]), and established scientific literature databases (e.g., ACM Digital Library, IEEE Xplore Digital Library, ScienceDirect, SpringerLink). The SDKs found in this search were subsequently examined and evaluated with respect to the functionality listed in the previous section as well as non-functional requirements (such as supported programming languages and platforms, available licences and licence cost, documentation, and current status).

Approximately 50 mAR SDKs were identified in this way. Of these, about 20 explicitly support location-based AR technology, facilitating mAR using GPS-based positioning and georeferenced content (GeoAR SDK). However, it is clear that most of the GeoAR SDKs are highly outdated or even officially discontinued or bought out (e.g. Metaio, Vuforia). This confirms the trend that many development studios and research companies are currently focusing purely on image-based tracking methods for AR solutions.

The few available, up-to-date GeoAR SDKs (Wikitude, DriodAR, PanicAR, and be-yondAR) only support some of the aforementioned functionality. The depiction of objects with a single geographic coordinate (POIs, 3D models) is supported as a classic use case by all SDKs. However, the visualisation of geographic objects that are defined by a collection of coordinates (e.g., a river as a polyline) cannot be directly implemented with the available SDKs.

Customisation of the appearance of AR objects is also not generally possible to the desired extent. Thus, the developer is often bound to the design specifications of the SDK. Furthermore, access to the two-dimensional screen coordinates of the rendered objects is usually restricted, meaning that objects cannot be dynamically expanded or superimposed with their own additional content. Similarly, access to the underlying camera image in high resolution is generally restricted, making it impossible to further process or save the image.

Overall, there are very few usable SDKs available for location-based AR applications. Existing SDKs are either out-of-date or only offer limited functionality with few options for customisation such that they do not support the development of a broad range of custom mAR applications.

Concept of a mobile location-based AR framework

The weaknesses of the existing GeoAR SDKs reveal the need for a new framework that offers more extensive functionality and possibilities for customisation. The concept of such a framework is presented in the following. The framework is intended to facilitate and speed up the development of as many of the aforementioned applications as possible while still allowing the developers as much freedom as possible to implement their individual ideas with regard to appearance and functionality. The framework is therefore designed as a low-level framework with the intention of internally performing the mathematical and core technological functions of location-based AR that would otherwise require expertise in the field of computer vision. It is suitable for app developers who have experience in writing mobile applications but who do not wish to have to acquire expertise in the fundamental mechanics of AR technology. Figure 6 shows the basic concept of the framework.

Fig. 6. Concept of a low-level location-based AR framework

A central part of the framework is an AR view (GeoARView) with its own lifecycle that displays the camera image as well as the overlayed, rendered AR objects. This core component consists of several layers which take care of internal functionalities like initialising and displaying the camera video stream (CameraLayer) or displaying AR objects (ARLayer) as well as user interface elements (e.g. radar and debug views). AR specific calculations and processes are encapsulated in separate classes, i.e. the conversion of three-dimensional geographic coordinates into corresponding two-dimensional screen coordinates on the basis of the current position and rotation of the device using the intrinsic camera projection matrix (e.g. ProjectionTranslation) or the rendering of an AR object (e.g. ARObjectRenderer). These classes are parts of the ARLayer.

Furthermore, an underlying model is created for the GeoAR objects to be displayed on the screen. This can either be represented by a single geographic reference point (POI) or by a list of connected reference points (polyline, polygon). Individual geographic points are initialised with their geographic coordinates, i.e., longitude, latitude, and height above sea level (optional). The appearance and design of these points on the screen (icon style, colour, size, transparency, etc.) can be changed at any time. Their appearance can also be adjusted according to context, for example, changing the transparency of a point depending on its distance from the user. Access to the distance between the user and the respective objects, as well as the 2D screen coordinates of the rendered objects, is always possible. The user of the framework has full control to the styling by implementing their own custom renderer.

A simple event model can be used to react to user interaction. Listeners exist to react when, for example, the device is rotated and new objects become visible or objects disappear out of the field of view. Additionally, all objects can be found within a definable region of the screen. This allows a reaction to click events from the user, for example. The listeners can also access the current feed from the camera in full resolution. Figure 7 shows the class diagram of the framework.

Implementation and usage

A prototype of the framework was implemented for the Android platform and was used in two applications of the EU project, City.Risks. The overall idea of this project is to develop IT solutions to prevent and mitigate security risks in urban areas. With the aid of smartphones in smart cities, citizens actively contribute to combatting crime and increasing the sense of security. Two applications for different use cases with integrated AR were developed using the location-based AR framework described in this article (see Figure 8). One application uses mAR to show information about ongoing crime incidents in the city, explore crime-related data in this area, and to actively report issues. Another application allows the user to navigate out of a dangerous area to a safe destination using AR methods.

Summary

With the new generation of ubiquitous, powerful mobile devices (smartphones, etc.), a technical infrastructure is available that allows the development of a wide variety of mobile AR applications -especially in the field of location-based AR -as well as widespread use of these mAR applications by anyone. In this article, the concept of a low-level development framework that facilitates the creation of a broad range of custom location-based AR applications has been presented. Geo-based AR technology has disappeared from sight in the last years as many companies have focused more on the optimisation of image-based AR technologies. Despite inaccuracies in the position determination, location-based AR is useful for many applications. However, developing one's own GeoAR application is often not possible without special expertise. The few existing SDKs can only be used under certain conditions as important functionality is missing or there are very few customisation options.

In contrast, the framework presented here allows the implementation of custom ideas with regard to appearance and functionality, thus enabling the creation of a wide range of different types of AR applications. It has been designed as a low-level framework for experienced app developers with no expertise in the field of computer vision. For this reason, it performs the mathematical and core technological functions of locationbased AR.

Two applications from the EU project City.Risks were prototyped on Android using the framework. Later in the project, the viability of the framework will be more closely investigated. In particular, the degree of inaccuracy in the determination of the position and orientation of the device that can be tolerated will be explored. The extent to which this inaccuracy can be compensated by the use of additional methods (e.g., use of GPS bearing or feature tracking to improve the estimate of the rotation) will also be studied.

Fig. 1 .

 1 Fig.1. Reality-virtuality continuum[START_REF] Milgram | Augmented reality -A class of displays on the reality-virtuality continuum[END_REF]

Fig. 3 .

 3 Fig. 3. Comparison of robustness and accuracy of mAR technologies

Fig. 7 .

 7 Fig. 7. Class diagram of the framework for location-based AR for Android

Fig. 8 .

 8 Fig. 8. City.Risks mAR applications: Nearby incidents (left), Safety-aware navigation (right)

Location-based Image-based Model-based

	Localization accuracy	1.5-35 m	0.5-2 mm	0.5-20 mm
	Localisation area	Large (GPS area) 3 m (markers)	10 m (objects)
	Localisation speed	100-200 msec	20-140 msec	5-240 sec
	External infrastructure	GPS satellite	Optical markers External sensors, model
	Resistant to drifts	No	Yes	Yes
	CPU/battery consumption Low	High	Very high
	Scale to large scenes	Yes (outdoor)	No (room)	No (building)

Table 1 .

 1 Comparison of mobile augmented reality approaches


Stefan Jensen
email: stefan.jensen@eea.europa.eu

Trends in policy relevant European environmental information systems

Keywords:

The paper presents the evolution of European environmental reporting and how it has transformed information systems. It connects systemic changes in policy assessments whilst acknowledging that information systems themselves have evolved both from a knowledge and a technology perspective. It starts out by setting the policy context where a review of the current legislation related to environmental monitoring and reporting goes hand in hand with initiatives to promote open and distributed data access. The knowledge management model of EEA has been developed over almost two decades and is the background against which an evolution related to the way environmental data is been reported and generated and indicators are been developed has to be seen. This evolution is triggered by a growing need to support systemic thinking and integrative projects involving a growing set of stakeholders. To support these new demands our ways to manage environmental data needs to change. We receive more volumes of often less homogeneous data in more frequent intervals. We need to combine data from very different sources -environmental data based on legislation; data from research and big earth observation programs; data from citizens and industry. This data is structured or unstructured. While we continue investing in streamlining the data management aspects of reporting, we have to step-wise engage in new approaches like big data analytics. With these newly emerging data flows we also need to revise our information technology infrastructure by introducing more modularity and new tools.

Background

The first pieces of environmental legislation in the European Union (EU) entered into force more than four decades ago. Since then EU Member States have been regularly collecting and reporting comparable data on a wide range of environmental issues ranging from hourly pollutant concentrations in cities to seasonal measurements of bathing water quality. They also keep track of greenhouse gas emissions, energy con-sumption, pollutants released from industrial facilities, the size and location of protected areas, etc. These data streams are essential for monitoring progress and ensuring an effective implementation of environmental legislation.

In 2016, the relationship between legislation and environmental monitoring underwent a review. This has also been referred to as a "fitness check" on environmental monitoring and reporting. i This fitness check triggered a refocusing on key environmental data and created a legal basis for the coming years to better incorporate new trends and areas for environmental data collection. Meanwhile, the Shared Environmental information system (SEIS) ii concept, itself a decade old, has been reinforced through ideas such as "active dissemination" and data harvesting. The data harvesting principle is already part of the SEIS principles (manage data as close as possible to the source; avoid duplication). The active dissemination concept is fairly new and has currently only been assessed by a set of pilot studies. The idea reflects the growth of environmental data and information which is too large to be reported in its entirety. The data should therefore be made available at the local or national level. Interested users in the EU Member States and the European Commission (as the owner of the data flows) would harvest national services on demand. This would occur in particular when the state of the specific environmental theme is not developing according to target. National data reporting would then be limited to a sub-set of data only. The EU's INSPIRE directive iii follows a similar principle building on a common spatial data infrastructure (SDI) to which data can be flexibly connected or, when legislation demands, a thematic subset can be reported.

The 2016 EU Communication on data, information and knowledge management iv supports the aforementioned trend. It encourages increased information sharing and collaboration between EU institutions such as through the establishment of knowledge and competence centres. It also calls for capacity to handle "big data" and machinereadable open formats as well as stronger use of particular data and metadata standards.

The paper presents the evolution of European environmental reporting and how it has been used. It connects systemic changes in policy assessments whilst acknowledging that information systems themselves have evolved both from knowledge and technology perspectives.

EEA's knowledge management model

The European Environment Agency (EEA) has, since its establishment in 1993, provided a regular analysis of the state of environment using various methodologies. This analysis has been published in various forms such as reports, factsheets etc. The methodology underpinning this has always been based on the MDIAK concept (monitoring, data, indicators, assessment and knowledge) combined with the DPSIR (driving forces, pressures, state, impact and response) model.

While the five areas in the MDIAK model have not changed, the way they have been used has evolved over time. The technology used in collecting, reporting and analysing environmental data has developed remarkably in this period. We can now collect, store and process larger amounts of data. We can also interlink different data streams to draw increasingly more accurate analyses on what is happening and why it is happening.

Monitoring is coming from more sources and data of different kinds is emerging in high volumes. Our indicator and modelling activities are getting more substantial based on efforts to increase data and indicator quality. Assessments become more cross-cutting and systemic in order to target a user community which constantly grows in numbers and diversity. A more in-depth look into these five areas (see figure 1) will be provided in the next two chapters.

The EEA recognises that common EU rules ensure data compatibility and facilitate data and information exchanges across Europe. Yet to be able to fully understand environmental trends, it is essential to cooperate not only within the EU but also at the wider European and international level. The founding EEA legislation v established the Environmental Information and Observation Network (Eionet) as the instrument to link with the EEA Member Countries after 1993 and together with it, and a growing set of European and international partners, the EEA is in a unique position to process environmental information and foresee future knowledge needs.

Evolution of environmental monitoring, data and indicators

To underpin EEA analyses and reports with evidence, we needed to establish a solid data and information base. Since the start of EEA's mandate, this was carried out in agreement with the management board and with the support of Eionet. The EEA subsequently established data flows based on voluntary arrangements with Member Countries and increasingly serviced data reporting on behalf of DG Environment. Those data flows were established under the environmental aquis -the set of legislation related to environment available at the European level. Data flows have been growing substantially in number and volume over the past years as displayed in figure 2. This led to the need to modernize our data handling system Reportnet which was introduced in 2002.

Figure 2: Growth in reporting environmental data

Environmental indicators are another part of the way the EEA gathers and uses data thereby contributing to its "knowledge system". Indicators serve many purposes such as allowing data to be combined through the use of different models to represent complex relationships. Indicators are also widely regarded as communication tools to simplify environmental messages. The EEA has consolidated its indicators during the past year into a focused number of high quality core set indicators (figure 3). Data collaboration has also taken place with the European Statistical System (ESS). To highlight their policy relevance, EEA indicators have been submitted as part of the European contribution to the Sustainable Development Goals' (SDGs) indicators. This international policy process is a key driver for agreeing on environmental and other sustainable development related content for the years to come. A further area of growing importance is the increased availability of remotely sensed data. Copernicus, the EU's earth observation programme, has been particularly responsible for driving growth through data from satellite observations which can be combined with data reported by monitoring equipment on land and at sea. European environmental assessments are finally taking more advantage of research data as the open availability of this data is increasing. EU funded projects are increasingly obliged to make resulting data publically available.The open data movement in general and infrastructure initiatives like INSPIRE and the Copernicus program are further instruments through which free data availability is promoted.

In addition to particular data ownership and management issues, discussed later in this paper, large amounts of data present another challenge: how to distil relevant policy relevant knowledge out of large data flows. What do we need to know to improve the environment further? Such reflections may well lead to identifying new areas to monitor, others to discontinue or monitor less frequently. It might also require building new connections between data sets. Moreover, the knowledge needs of an urban planner may be very different from those of a European policy maker. How can environmental knowledge contribute to the management of complex systems like cities?

Systemic assessment and an extended knowledge approach

Despite some gaps, current knowledge on the environment is impressive and our understanding of specific issues has expanded over time. However, this enhanced understanding has also highlighted the need to look at the "bigger picture"-the need for more systemic analyses, looking at entire systems, such as on mobility and food. For example monitoring air pollutant concentrations can only take us to a certain point. The EEA cannot analyse and tackle air pollution without looking at other areas such as transport, the dieselisation of the vehicle fleet, agriculture, urban sprawl, and consumption patterns. The environment is complex and our knowledge base needs to factor in this complexity. It has become clear that we will increasingly need more systemic and cross-cutting knowledge in the future. In close collaboration with partners, and in support of the European Commission's 7 th Environmental Action Program (7 th EAP) vi , the EEA is contributing to Europe's environmental knowledge base with systems-based thematic assessments. The EEA does not only focus on past trends and the current state of play, but also on emerging issues and future knowledge needs. Further details on this approach and related work can be found in the most recent EEA Multiannual work programme (MAWP) vii . Systemic analysis constitutes the core of the EEA's latest European environmentstate and outlook report released in 2015. One section within this report has tried to assess the influence of global trends on Europe's environment and vice versa. Thematic EEA reports also address the issue at hand within a wider systemic analysis. This very much pushes the need to adapt our data management to the new knowledge management approach. With input sources multiplying and data volumes growing we will have to adapt our information systems. This relates to many aspects: the governance structure around them -so who guides which process, the content they represent and the IT system which is used to process the data and disseminate the results.

While we used to work with a governance building on environmental datacentres (EDCs) we move to a wider governance steered by a European environmental knowledge community (EKC). A knowledge approach is widening scope and analytical opportunities. The demand for policy relevant knowledge is seen as coming from the interaction between many institutional players and those relationships develop subsequently. Due to the diversity of them, data variety is growing. Activities move from project type to more integrated program type. Data management, "big data" and the impact on the information system Several aspects have already been highlighted which are driving the future development of both data management and the information system as such. To acknowledge this, the EEA established a data and information management framework in 2015 viii . Data management is far more complex as the input sources are much more diverse. Figure 5 shows the main areas from which data has been collected. Challenges lie, in particular, in the different quality standards, assurance and control which needs be addressed where data are jointly been processed into European data sets. The EEA runs a common workspace for processing the national reporting data which then needs to be complemented by data sets from other input streams. This is especially the case when producing environmental indicators with sources which need to be integrated. It is here where we start meeting the issues which are addressed as part of the big data discussion. Out of the five big data characteristics (volume, variety, velocity, variability, veracity) the increase in volume is obvious in the area of national reporting data and the huge data stream generated by Copernicus. Variety is growing particularly when looking at research data and the new data emerging from citizen science (e.g. on species distribution) or data from the telecommunications industry. Here, spatial patterns derived from mobile phone data can be related to human movements and used to produce new population density or transportation flow related data sets. Velocity, the concept which addresses the speed in which data is generated and processed, is gaining a bigger share in the monitoring data. Here the near real time air quality monitoring is becoming more operational. Data variability grows the more the questions put forward become systemic, and finally data veracity -the differences in data quality -continues to place limits on producing integrated datasets of good overall quality. Data publication, independent from its explicit usage in assessments, is one area which is seeing growth within the EEA. The demand to download datasets in flexible ways is increasing as are the need to create regular and interactive visualisations. The provision of map viewers, where user statistics have been monitored since 2007, has been growing steadily since 2009 and a larger variety of web services have been established and are increasing in popularity in part because they are integrated by stakeholders into their websites and information systems.

The typology of assessment products has also been changing. The number and volume of reports have been decreasing. Some elements of those reports are provided through the EEA website whilst others are more targeted, and offered in a briefing format alongside smaller information products. In general such products are not offered in hard copy format however users can often customize them to their needs and print them as PDF directly from the website.

Information technology and tool related challenges

As has been discussed, new data processing steps require new workflows and more integrated tools. Figure 6 explains the key components and tools used for the management of the reporting data. It incorporates the update of Reportnet and shows the tools used for data processing in the common workspace. On the dissemination side, a variety of tools has been used -depending on the type of data and the target audience. For a number of years, the EEA has been using cloud technology to host services and distribute data. We can be considered as a ground-breaker amongst the EU institutions. The use of cloud services has allowed EEA to better address the challenges posed by big data. Demands on our services tend to peak towards the end of a particular quarter when major aquis reporting is due or alongside the publication of popular reports such as the one on European bathing water due in June. Access to web map services can be exceptionally high.

There is one area which is not specific to the environment yet creates the need to take serious and partially costly measures. Information and communication technology (ICT) security needs to be tackled more intensely. This calls to enhance several functions in the information system: back-up solutions for data need to improve; security needs to be enhanced, that is why data access is been categorized into different security levels. Based on this, aauthentication levels for several of our services should be enhanced beyond the standard "public" and "restricted" designations, alongside other security measures.

Figure 1 :

 1 Figure 1: EEA's knowledge management based on the MDIAK model

Figure 3 :

 3 Figure 3: EEA's core set of indicators

Figure 4 :

 4 Figure 4: Evolution of the EU institutional structure towards a knowledge approach

Figure 5 :

 5 Figure 5: Highlighted components for management of EEA multi-source data flows

Figure 6 :

 6 Figure 6: Tools used in the EEA management of reporting data

i

  European Commission. "Fitness Check of reporting and monitoring of EU environment policy". Commission Staff Working Document SWD(2017)230 and Report on "Actions to Streamline Environmental Reporting" COM(2017)312 ii European Commission. "Towards a Shared Environmental Information System (SEIS)". Eur-lex. [Online] 1 February 2008. http://eur-lex.europa.eu/legalcontent/EN/TXT/PDF/?uri=CELEX:52008DC0046&from=EN. COM(2008) 46 final

iii European

Parliament, Council of the European Union.

  Directive 2007/2/EC establishing an Infrastructure for Spatial Information in the European Community (INSPIRE) and related Implementing Rules. Eur-lex. [Online] 14 March 2007. http://inspire.ec.europa.eu/ iv European Commission. Data, Information and knowledge management at the European Commission. Communication to the Commission. C(2016)6626 [Online] https://ec.europa.eu/transparency/regdoc/rep/3/2016/EN/C-2016-6626-F1-EN-MAIN.PDF

v European

Parliament, Council of the European Union.

  Regulation 401/2009 on the European Environment Agency and the European Environment Information and Observation Network. Eur-lex. [Online] 23 04 2009. http://eur-lex.europa.eu/legalcontent/EN/ALL/?uri=CELEX:32009R0401 vi European Commission. Environment Action Programme to 2020 'Living well, within the limits of our planet'. Europa. [Online] 20 11 2013. http://eurlex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32013D1386 vii European Environment Agency. Multiannual Work Programme 2014 -2018: Expanding the knowledge base for policy implementation and long-term transitions. [Online] 21 January 2014. http://www.eea.europa.eu/publications/multiannual-workprogramme-2014-2018. ISBN 978-92-9213-418-1 viii European Environment Agency. EEA Data and Information management framework. 18 December 2015. unpublished EEA internal


Miroslav Kadlec
email: miroslav.kadlec@mail.muni.cz

Barbora Buhnova
email: buhnova@mail.muni.cz

Tomas Pitner

Planning and Scheduling for Optimizing Communication in Smart Grids

Keywords: Smart Grids, Communication, Scheduling, Optimization

Smart grid is a concept defining future electricity distribution network with the purpose of improving its reliability, efficiency and reducing its ecological impact. To achieve that, massive volumes of data need to be sensed and transmitted between the elements of the grid. A robust and efficient communication infrastructure is thus as essential part of smart grid. Some of the data transmissions are not time-critical, providing an opportunity to improve the communication network performance. In this paper, we present a new approach to optimize smart grid communications through time-based scheduling. Additionally, we provide a review of communication technologies in smart grids and published approaches to avoid congestions and transmission failures.

Introduction

The traditional electricity distribution infrastructures were developed during the first half of the 20 th century and nowadays, they are undergoing complex transformation to fully satisfy the needs of modern society. The electricity consumption has doubled in the past 20 years and it is supposed to keep rising at least the same speed. Since the majority of electricity generation relies on coal, oil and gas resources, the growth in power usage brings more and more significant environmental problem. Additionally, more aspects of modern life are dependent on the electricity supply. Though, incorporating renewable resources based generators (e. g. photovoltaic power plants and wind turbines) as well as making the whole infrastructure more monitorable and controllable become an important strategic act to preserve power grid sustainability.

The need to employ more power generators based on renewable sources brings several challenges to be solved. First, both wind and solar power plants provide rather unstable electricity production varying with current weather that is difficult to be predicted, but may be estimated from short term weather forecast. Nowadays, end electricity users are also encouraged to install their own small power sources in order to save money lowering the electricity consumption and potentially send electricity back to the network in cases of overproduction. Since storing energy or transmitting it over long distances causes significant losses, the task of keeping the power grid capable of maintaining these changes is nontrivial.

The concept of smart grid represents a complex approach to improve current power distribution infrastructure in order to provide application such as demand response, use of distributed energy resources (DER) or real-time state monitoring [START_REF] Saputro | A survey of routing protocols for smart grid communications[END_REF]. The overall goals are to lower energy losses and to make the electricity network more reliable, efficient and environmental-friendly. In order to achieve the previously mentioned applications, smart metering and remotely controllable devices, communication infrastructure and computational systems for data collection and decision making are supposed to be added to the existing power distribution network.

To make the grid state analysis and decision making as relevant as possible, a large amount of up-to date complete data will be needed. Typically, the data will be generated in metering/monitoring points all over the network and then collected in central offices and, conversely, control commands will be sent from central offices to elements of the grid. Hence, a bidirectional, robust and reliable communication network is a critical part of smart grid. Due to its complexity, expenses associated with deploying a communication infrastructure of this scale are supposed to be high and strongly dependent on concrete technology used.

The communication requests are expected to have variable data sizes and requirements on latency and reliability of delivery. Since some of the data transfers are not time critical, the communication may be planned using proper scheduling techniques, request importance evaluation, real time adaptation and predictions of network traffic load and message sizes in order to balance traffic load and to prevent and manage communication network overloads. Thus, a given communication infrastructure will be capable to maintain bigger number of communication requests and to provide most relevant data possible at the time, so a better trade-off between quality and price of the communication infrastructure may be earned. To schedule the messages efficiently is a new optimization problem with many aspects specific to smart grid environment.

In this paper, we propose new approach to optimize smart grid communications through time-based scheduling. As the first step, an analysis of smart grid communication was conducted, based on existing literature as well as our practical experience from the smart grid design and implementation projects that we actively contribute to within the context of the Czech Republic. Then, the optimization problem is identified along with its inputs as well as techniques to find the solution are outlined.

The rest of the paper is organized as follows. In the Section 2, messages exchanged between smart grid elements are summarized with emphasis to their communication requirements. In the Section 3, communication technologies usable for smart grids with their specifics and challenges are listed. In the Section 4, the opportunity to use a scheduling algorithm in order to optimize traffic load is described and design of a potential solution is outlined and the specifics of given optimization problems are described.

Communication in Smart Grids

As long as smart grids are being developed as enhancement of current electricity distribution networks rather than brand-new infrastructures, the concrete implementations vary through different countries according to the existing power grids specifics. On the other hand, the overall structure, high-level applications and other general characteristics remain the same. In order to provide a standardized model of smart grid basic structure, SGAM (Smart Grid Architecture Model) framework has been presented in [START_REF]Smart Grid Coordination Group: Smart Grid Reference Architecture[END_REF]. Although the model omits technical and implementation details, it provides a good insight into the organizational structure and defines several levels of abstraction the smart grids may be looked on, Fig 1 . Along the "interoperability" axis, several layers are listed, where each one is supported by layers located below. For example, the function layer describes functions and services provided by the smart grid in order to fulfill business requirements and policies. Similarly, the information layer summarizes all the data needed by the functions.

According to the SGAM framework, communication infrastructure serves as the support element for all the upper layers. Indeed, having up to date data transmitted by communication infrastructure is necessary for the data-analysis and decision-making systems incorporated in smart grids [1][3]. Thus, deploying a robust communication infrastructure capable of satisfying smart grid requirements is one of major tasks in smart grid development [START_REF] Madueno | How Many Smart Meters can be Deployed in a GSM cell[END_REF] [START_REF] Kouhdaragh | Smart meters density effects on the number of collectors in a Smart Grid[END_REF]. According to [START_REF] Yu | Cognitive radio based hierarchical communications infrastructure for smart grid[END_REF] [START_REF] Gungor | Advance Metering Infrastructure and DLMS/COSEM Standards for Smart Grid[END_REF], the communication network itself may be divided into three domains according to the coverage range and involved devices:

• Home area network (HAN), that realizes communication between the smart meter and other household appliances, for example local power generators, controllable consuming devices, electric cars and other devices equipped by communication interface. • Neighborhood area network (NAN) interconnects smart meters and data concetrators, that collect metering data and send them to central systems.

Complementarily, control commands coming from central systems are passed to smart meters.

• Wide area network (WAN) cover data exchange between data concentrators and the central systems, where the collected data is analyzed and potentially reacted to.

A graphical representation of described architecture is in Fig. 2, showing HAN, NAN and WAN with typical communicating elements. Such a multi-layered model is expected to be used since the smart grid communication network is needed to cover entire region with the intention to connect a large set of nodes [START_REF] Bojkovic | Smart grid communications architecture: a survey and challenges[END_REF].

Communication Requirements

Several papers have been published aiming to describe communication requirements of smart grids. The general objective of making the power distribution network more efficient, reliable and inteligent is expected to be achieved through implementing certain smart grid applications. The following requirements on the smart grid are the most commonly presented in existing studies [3][9][10] [START_REF] Brown | Impact of smart grid on distribution system design[END_REF]:

• Automatic metering reading (AMR) refers to a technique that uses a communication infrastructure to automatically collect metering data, events and alarms from the smart meters. Typical AMR communication scenarios are meter readings (both on-demand and previously scheduled readings), meter events and alarms (e. g. health events, unauthorized meter access), grid events detected by meters (temporary power outages) and others. AMI itself may be viewed as the most basic and simple application that serves as a platform supporting other applications [START_REF] Gao | A survey of communication/networking in Smart Grids[END_REF][9]. • Wide area situational awareness will monitor and manage all the components of the electric power system. Through predicting and modifying behavior of smart grid elements potential problems in the infrastructure may be detected or avoided [START_REF] Gao | A survey of communication/networking in Smart Grids[END_REF]. • Demand response and consumer efficiency aims at balancing the overall electricity consumption by encouraging customers to cut their usage during peak times. In order to achieve this behavior, dynamic electricity pricing will enable the consumers to lower their expenses by postponing some consumption, that is not time critical (e. g. electric water heater or air conditioning) to off-peak times [START_REF] Gao | A survey of communication/networking in Smart Grids[END_REF][9]. • Distributed energy resources uses load prediction and balance techniques in order to optimize integration of big number of small electricity generators based on renewable sources [START_REF] Brown | Impact of smart grid on distribution system design[END_REF]. • Grid-to-vehicle, vehicle-to-grid (G2V, V2G) tends to use integrated accumulators of electric and hybrid vehicles as temporary electricity sources in order to overcome short-term consumption peak times [START_REF] Khan | A comprehensive review of the application characteristics and traffic requirements of a smart grid communications network[END_REF].

Although, the overall smart grid system is lacking widely accepted standards, for particular smart grid applications, standards and protocols have been published. In [START_REF] Gungor | Smart grid technologies: communication technologies and standards[END_REF], major smart grid standards are listed and described, e. g. ANSI C12.1, ANSI C12.19 (Advanced metering infrastructure), OpenADR (Dynamic pricing) and SAE J2836 (Electric vehicles communication).

With the purpose of defining technical requirements on smart grid communication network, research has been conducted to analyze general business communication requirements. According to [START_REF] Wang | A survey on the communication architectures in smart grid[END_REF][15], reliability, latency, security and interoperability are the key characteristics of communication network in the context of smart grids requirements.

Concrete messages exchanged within smart grids are supposed to have various requirements depending on related smart grid application. In [START_REF] Luan | Smart grid communication network capacity planning for power utilities[END_REF], utility operation practices, planned implementations and experience from other research groups have been analyzed in order to enumerate particular communication events with its estimated packet sizes, frequency, and delay objective. In the presented results, there are considerable differences between separate events requirements -while interval data reading is stated to have 480 Bytes data size, it is not considered to be delay time critical; on the other hand, meter remote disconnect requests size is only 20 Bytes, but the delay objective is 2 seconds.

Even more exhaustive summary has been published in [START_REF] Kuzlu | Communication network requirements for major smart grid applications in HAN, NAN and WAN[END_REF], presenting typical data size, sampling frequency, maximum latency and desired reliability for various NAN and WAN applications. Twelve groups of smart grid operations have been investigated, for example:

• Advanced metering infrastructure applications including on-demand, scheduled and bulk metering data reads • Pricing applications, e. g. TOU (time-of-use) commands, real-time pricing and critical peak pricing • Electric service prepayment • Demand response communication According to the results presented, there is a big variability of both data size and latency requirements of separate operations.

Communication Technologies

Various communication technologies have been considered for smart grids including both wire-line and wireless solutions with respect to their abilities to maintain the requirements listed in the previous section and additionally to other aspects like coverage range and deployment costs, comparing their suitability for smart grids needs.

Wireline technologies represent one option to build up communication network separate from the power distribution infrastructure itself. Construction of such a network would require extra investments; on the other hand, it is capable of providing higher data transmission rates and not shared with other application [START_REF] Wang | Compressing electrical power grids[END_REF]. Some national-scale wireline networks have already been deployed for other purposes, e. g. cable television or telecommunications. Various transmission media may be possibly used.

Fiber-optic networks provide both the highest data rate (for SONET/SDH architecture, the maximum theoretical rate is 10 Gbps, for VDM technology up to 40 Gbps) and large coverage range (up to 100 km) [START_REF] Kuzlu | Communication network requirements for major smart grid applications in HAN, NAN and WAN[END_REF].

DSL (Digital Subscriber Line) technology relies ordinary telephone lines. ADSL (Asynchronous DSL) is capable of max theoretical data rate 1-8 Mbps downstream with coverage up to 5 km. High-speed versions of DSL (HDSL, VDSL) provide higher data rates, but smaller coverage range [START_REF] Habib | Channelized voice over digital subscriber line[END_REF]. Although, DSL architecture is already deployed for many premises that would lower the initial installation expenses, it may not be suitable for smart grid applications due to possible down time problems and low reliability especially for more distant customers [START_REF] Kuzlu | Communication network requirements for major smart grid applications in HAN, NAN and WAN[END_REF].

Coaxial cable transmission network uses cable television infrastructure. For highspeed data transfer over existing hybrid fiber-coaxial network, DOCSIS (Data Over Cable Service Interface Specification) may be used. This technology provides 172 Mbps maximum data rate and up to 28 km coverage range [START_REF] Kuzlu | Communication network requirements for major smart grid applications in HAN, NAN and WAN[END_REF].

Ethernet is a technology often used to build local networks in houses and workplaces providing data rates between 10 Mbps and 10 Gbps [START_REF] Wang | A survey on the communication architectures in smart grid[END_REF]. Unlike DSL and Coaxial solutions, there is no Ethernet based large-scale network currently deployed.

Powerline Communication (PLC) may be viewed as a special case of wireline technology, but has several unique characteristics. PLC is realized through power distribution network. To transmit both the power and data, electric signal modulation is used. As long as the theoretical data rate is about hundreds of kbps and the transmission channel is rather noisy, PLC is not as effective as e. g. DSL [START_REF] Kuzlu | Communication network requirements for major smart grid applications in HAN, NAN and WAN[END_REF]. However, it is not shared with other applications, that generate external traffic and though, cause inpredictable and unbalanced load.

Wireless communication technologies are also often inspected for the use in smart grids, mainly due to the fact, no wires need to be installed. Concrete wireless technologies may represent appropriate solutions for NANs or WANs.

Wireless Local Area Network (WLAN) is high speed communication technology often used for wireless Internet access commonly known as WiFi. It provides reliable secure communication with data rate up to 2-600 Mbps (depending on standard), but its maximum range is lower than 100 m and the cost and power consumption are higher making WLAN less suitable for massive smart grid use [START_REF] Kuzlu | Communication network requirements for major smart grid applications in HAN, NAN and WAN[END_REF].

ZigBee operates on unlicensed bands (shared with WLAN) and its maximum theoretical data rate is 250 kbps. Due to its low speed, short range coverage (up to 100 m), ZigBee may be suitable for in-home applications [START_REF] Wang | A survey on the communication architectures in smart grid[END_REF] [START_REF] Kuzlu | Communication network requirements for major smart grid applications in HAN, NAN and WAN[END_REF].

Cellular networks, e. g. GSM, GPRS, LTE, have the potential to enable wide area wireless smart metering deployment. The technology is based on a large number of transcievers dividing the covered area into communication cells. Communication devices connect to them do send and receive messages. In [START_REF] Usman | Evolution of communication technologies for smart grid application[END_REF], GSM is considered a possible solution for smart grid communication since it is already deployed in most of inhabited areas. On the other hand, performance reduction during peak traffic and the problems connected to maintaining QoS over noisy channel.

Along with comparing technical parameters, Cost Benefit Analysis is an important procedure to assess the suitability of a particular technology for smart grid solutions. In [START_REF] Giordano | Guidelines for cost benefit analysis of smart metering deployment[END_REF], a comprehensive assessment framework of smart grid projects is proposed with purpose to provide guidance and advice for conducting cost-benefit analysis. According to the presented research, for monetizing possible benefits related to the smart metering roll-out, following aspects are critical (not exhaustive):

• Hardware cost including investments in communication infrastructure installation • Data transfer costs (in GPRS, Radio Communications, ...) • Communication success rate • Number of smart metering installed Hence, achieving a good trade-off between communication network costs and its robustness and capability is an important step in order to make the smart grid deployment effective and beneficial. Both PLC and GPRS are mentioned as technologies potentially usable for smart grid. Additionally, a comparison is presented, regarding PLC as more suitable for "concentrated" deployments (bigger number of clients concentrated in a limited area e. g. an entire city) and more initial-costs intensive, while GPRS is stated to work well in "scattered" deployments (e. g. only clients with higher consumption in each network).

Optimizing Smart Grid Communication

In recent years, several papers have been published with the purpose to identify opportunities to optimize smart grid communication, describing possible approaches and solutions to the presented optimization problem.

Communication infrastructure design

Proper technology choice and topology design is the first step to build a sustainable communication infrastructure. In [START_REF] Luan | Smart grid communication network capacity planning for power utilities[END_REF], an approach is presented to estimate communication profiles for selected smart grid application. Then, the capacity utilization ratio is applied with respect to the traffic described while considering two scenarios (Blue Sky Day and Storm Day Scenario). As an output of this analysis, both the regional and WAN capacity requirements are derived. In [START_REF] Zhou | Scalable distributed communication architectures to support advanced metering infrastructure in smart grid[END_REF], the scalability of three communication architectures for advanced metering infrastructure (including i. e. AMR) is inspected. Accumulated bandwidth-distance product (ABDP) is defined as a performance metric. For each architecture, an optimization problem is defined in order to obtain the solution for minimizing the total costs considering both the ABDP and the cost of meter data management system. Additionally, the scalability of the total expenses for the centralized and distributed infrastructure is analyzed and compared, while the distributed architecture is said to have significantly lower total cost [START_REF] Zhou | Scalable distributed communication architectures to support advanced metering infrastructure in smart grid[END_REF].

Congestion avoidance

The problem of potential congestion caused by huge number of data samples has already been addressed in literature. TCP based networks may possibly be overloaded due to excessive packet retransmission in case of communication peak times. To overcome this problem, so-called transport aggregator operating on communication transport level is presented in [START_REF] Khalifa | Transport protocol for smart grid infrastructure[END_REF]. The aggregator node is added between the data sources and the data collection server in order to split the TCP connections between the mentioned elements. Its task is to collect data from sources (e. g. smart meters) and to forward to the central server while adjusting the transmission rate and achieving congestion control. Another investigated method is to identify the most salient data to be communicated [START_REF] Wang | Compressing electrical power grids[END_REF]. Through Singular Value Decomposition (SVD) analysis, the authors have determined, that typical grid admittance matrices have only small number of strong components. SVD sparsity proved to be able to find out, what parts of the system are strongly coupled [START_REF] Wang | Compressing electrical power grids[END_REF]. In [START_REF] Allalouf | Data-qualityaware volume reduction in smart grid network[END_REF], an intelligent traffic volume reduction of the communication flows is presented. The authors note that beside of "essential" data that must reach the target, there is a "non-essential" part. An assumption is made, that due to limited bandwidth, the communication infrastructure is not capable to transmit all the data successfully. Quality-aware reduction of the non-essential data component is used to utilize the overall revenue of communication flows processed.

In [START_REF] Huang | Priority-based traffic scheduling and utility optimization for cognitive radio communication infrastructurebased smart grid[END_REF], a systematic priority-based approach is described to model and optimize communication in sensor networks over cognitive radio (CR) infrastructure. The proposed algorithm performed well in simulations, however is tightly connected to the concrete technology used.

To the best of authors knowledge, time-based scheduling of data transmissions in order to optimize communications in smart grids has not been investigated exhaustively. We have addressed this problem in cooperation with the major electricity distribution company in the Czech Republic. An AMR (Automated Meter Reading) pilot project is being executed within particular regions providing an opportunity to explore ways to optimize communications. In the current setting, all the data collection are executed at fixed times that were assigned considering neither the network specifics nor the current state if the infrastructure. As long as particular communication requests have various requirements in scope of delivery time and message size, proper timebased scheduling may be used to prevent the network from congestion and potential message interferences. In the first part of the research described in this paper, we focus on WAN communication scheduling, thus the messages in following text are the ones transmitted between a central management system and Distribution Transformer Stations (DTS).

Time-based communication scheduling problem

We have analyzed the current AMR environment identifying the following groups of characteristics important for implementing an appropriate communication scheduler.

First, we have inspected the specifics of cellular network that is going to be used in the scope of the Czech Republic (and likely also in other European countries) for WAN communication. The cellular network consists of base transceiver station (BTS) distributed within the covered area. The following aspects have been considered crucial for the scheduling process:

• Sending big amount of data through a single BTS may lead to undesired interferences and potential overload of given transceiver. • It is uncertain, which BTS a concrete element communicates through.

o Nevertheless, the probability that two DTS will communicate through the same BTS might be partially estimated according to their geographic location. • The BTS capacity varies (due to external traffic, weather conditions).

Estimation and detection of critical periods would help to avoid scheduling requests in such intervals.

Single DTS is typically unable to serve more simultaneous communication requests.

Additionally, we have determined, which attributes each communication request should be assigned with in order to describe its characteristics and requirements:

• target device, • desired interval, when the given request needs to be processed, determined by start time and deadline. • priority preference describing, when the request should be preferably processed (e. g. control commands should be scheduled as soon as possible; on the other hand, in some cases of metering data reading, it may be beneficial to schedule them later, providing data concentrator with extra time to collect more data), • estimated data load and duration of the request.

Since neither the data describing communication network nor the requests characteristics (data load, processing time) are often available, collecting message sizes, failure rate and processing times will be an important part of the scheduling algorithm in order to build a sufficient knowledge base. Gathered information will be then used to enhance the accuracy of predicted values essential for proper scheduling.

We assume that not all the communication requests will be known during schedule creation. Though, the scheduling needs to be done on both a strategic and operational level. While the strategic-level scheduling algorithm of previously known requests (e. g. periodic meter readings) is not necessarily time critical (and may be able to analyze all the inputs mentioned), light-weight techniques need to be implemented to dynamically readjust scheduled messages in case of sudden latency-critical messages generation (e. g. ad-hoc meter readings, smart meter commands).

Finally, since the meter data readings represents a significant part of all data transmitted, the overall traffic may be reduced through identifying less critical data (e. g. periodic metering data of low-consumption places) and potentially omitting them in case of overload.

Future work

Proposed analysis was conducted as part of a pilot project defined by Czech energy distribution company aiming to deploy AMI in particular areas. In the scope of the Czech Republic, the PLC is most probably to be used for NAN communication, while the WAN will mostly use cellular network (GPRS and LTE).

Future effort will aim at implementing systems for data collection, analysis and prediction in order to obtain the essential inputs described in the previous section and then, to design and implement a prototype of the proposed communication scheduler itself.

Conclusion

Smart grid is a complex approach to make currently existing power grids more robust, efficient and sustainable. Beside other applications, smart grids aim at balancing electricity consumption, incorporate renewable energy resources, monitor the whole system and avoid or recover from faults. To achieve that, a communication infrastructure is used to interconnect smart metering devices deployed in the area with central systems for data analysis, prediction and decision making.

To maintain huge amounts of data, a sufficient communication network needs to be deployed. Along with optimization techniques listed in sections 3.1, 3.2, time based messages scheduling is believed to be able to decrease the risks of network overloading and congestion. Hence, a better trade-off between capability and price of the communication infrastructure can be earned. In this paper, an approach to optimize smart grid communication through communication requests scheduling is presented along with identification of essential input data of the proposed scheduler. As a part of the work presented in this paper, an extensive review of the state of the art of technologies and existing literature resources have been performed, which became part of Sections 2 and 3, to underline the reasoning that follows these sections.

Fig. 1 .

 1 Fig. 1. Smart Grid Architecture model [2].

Fig. 2 .

 2 Fig. 2. Multilayer structure of the smart grid communication network [8].