More and more data are collected via sensors. Wireless networks can be implemented to facilitate the collection of sensors data and to reduce the cost of their acquisition. In this chapter, we present a general architecture combining Wireless Sensor Network (WSN) and Spatial Data Warehouse (SDW) technologies. This innovative solution is used to collect automatically sensor's information and to facilitate the analysis of these data. The WSN used in this application has been deployed by Irstea and tested during several years in vineyards in South of France. The novel contribution presented in this chapter is related to the use of a SDW to manage data produced by geo-referenced sensor nodes. SDW is one of the most appropriate modern technologies for analyzing large data sets at different temporal and spatial scales. This type of databases is a specific category of information system used to integrate, accumulate and analyze information from various sources. These data are usually organized according to a multidimensional schema to facilitate the calculation of indicators. In this chapter, we introduce the development of a SDW storing the data collected by this WSN. The implemented data warehouse can allow users to aggregate and explore interactively data produced by sensors. With this system, it is possible to visualize on a map the results of these aggregations.
Top1. Introduction
Viticulture tries to produce more and better in order to preserve the reputation of their wines. To achieve the desired quality levels, the vineyards are monitored, processed and irrigated. Farmers want to monitor and manage the soil moisture to provide the right water quantity needed to plants at the right time. To improve the quantity and especially the quality of the wines, suitable water supply is necessary. Unfortunately, with excessive water supply, some diseases (mildew, etc.) can appear.
The French national project called DISP’eau leaded by the ITK company and involving Irstea was proposed in order to develop decision support systems to help farmers in their decision to irrigate vineyards (Barbier, Cucchi, & Hill; ITK, 2014). In this project, an automatic collection of soil moisture measurements has been made in vineyards by means of a Wireless Sensor Network platform (WSN) deployed by Irstea. This WSN has been developed in order to periodically, automatically and remotely collect the data related to soil moisture (Jacquot, De Sousa, Chanet, & Pinet, 2011). As indicated in (Barbier, et al.), this type of data is used in agronomic models to assess the evolution of vineyards.
Wireless sensor networks (WSNs) are groups of spatially distributed autonomous wireless sensor nodes. WSNs can be used to monitor physical or environmental conditions, such as temperature, humidity, pressure, sound, motion or pollutants (Li, He, & Fu, 2008). These sensor nodes can collect data from different environments and pass their data through the network to transfer information to a main location. As shown in this chapter, the collected data can be used for precision farming. Thanks to the WSN technology, a large amount of data can be collected over time. However, it is often difficult to interpret these collected data in order to analyze and understand the monitored phenomena. Data warehouses (DW) can facilitate the analysis of this information – for instance, an example can be found in (Boulil, Bimonte, & Pinet, 2014).
The novel contribution presented in this chapter is the use of Spatial Data Warehouse (SDW) technology to manage data produced by geo-referenced sensor nodes. This chapter presents an innovative application combining WSN and SDW. More precisely, we present a SDW and a SDW-based application for the visualization and the exploration of the data collected by a WSN used in the DISP’eau project. The goal was to provide a tool to facilitate the analysis of sensor’s information produced by vineyards monitoring for precision farming. In the DISP’eau project, this type of data was used to provide vineyard irrigation recommendation.
DW is a large repository of data, aiming at supporting the decision-making process by enabling flexible and interactive analyses (Kimball, 2008). Online Analytical Processing (OLAP) systems allow decision makers to visualize and explore facts during querying sessions, whose results are displayed using interactive pivot tables and graphical displays. SDW is a DW extension for spatial information management (Malinowski & Zimanyi, 2008). Warehoused spatial data are analyzed by means of Spatial OLAP (SOLAP) systems, defined as “visual platforms built especially to support rapid and easy spatiotemporal analysis and exploration of data following a multidimensional approach comprised of aggregation levels available in cartographic displays as well as in tabular and diagram displays” (Bédard, Rivest, & Proulx, 2006).
The chapter is organized as follow. Section 2 introduces related works. Section 3 provides details on the deployed SOLAP architecture for WSN. Section 4 concludes the chapter and highlights some future works.