# Thinger.io: An Open Source Platform for Deploying Data Fusion Applications in IoT Environments
## 1 Introduction  
Currently, data and information fusion is a very broad field of research in many domains. Modern fusion systems are oriented to the integration of multisensor data, information available in databases, experience and expert knowledge, contextual information, mission status, etc., in order to provide real-time estimates with a high value added about the situations of interest. The current trends in modern Data Fusion (DF) applications are related with the capacity to integrate all types of information or knowledge: observational data, knowledge models (a priori or inductively learned), and contextual information. Each type of information has a different nature and potential support to the output of the fusion process:  
* **Observational Data:** Observational data are the original data about the dynamic scenario, as collected from heterogeneous sensors (each one with some observational capability). These data are related to observable entities in the real world that are considered of interest. * **Contextual Information:** The \"context\" and information about Contextual Information [1] is defined as \"the set of circumstances surrounding a task that are potentially of relevance to its completion.\" This information should be considered in the fusion process for this relevance. When the fusion system has to estimate or infer tasks, this process implies the development of a set of best-possible estimation. These estimations should take into account this lateral knowledge [2]. Context could be seen as the framework, i.e., not the specific entity, event, or behavior of main interest but context is the information which is relevant to the formation of a best estimate of these items. * **Learned Knowledge:** There are situations where a priori knowledge for DF process development cannot be stablished. In this case, one possibility is to try and extract the knowledge through online machine learning processes operating on observational data and context information. These are procedural and algorithmic methods for discovering relationships among targets and behaviors of entities of interest [3]. There is a trade-off involved in trying to develop fully-automated algorithmic DF processes for complex problems where the insertion of human intelligence at some point in the process may be a much more judicious choice. DF applications can benefit from new Internet of Things (IoT) technologies with the ability to acquire and process a huge amount of heterogeneous sensor data [4]. The possibilities offered by IoT systems are numerous, and can be applied to several fields, from Smart Homes [5], Smart Cities [6], Connected Car [7], Industry 4.0 [8], Smart Farming [9], etc. In general, the IoT can be applied to any process or environment which requires monitoring or remote actuation, generally over the Internet. It can be used for monitoring fuel consumption, control cultivation processes, supervise storage conditions, control pollution, irrigation systems, etc. There are also many recent developments related to monitoring chronic patients or elderly people (associated to the HealtCare term), agriculture (Smart Farming), power generation systems, etc. Despite recent advances in the IoT infrastructure, capturing, storing and processing a large amount of data from heterogeneous sensors remains, to this day, a challenge. The exponential increase in the number and variety of sensors hinders interoperability for the development of data fusion applications. Modern data fusion applications require the integration of three technologies: Cloud, Big Data and IoT technologies. IoT and Big Data technologies are closely related. The arrival of this large number of connected devices causes the generation of many data that must be analyzed through Big Data technology. Smart objects offer millions of data due to the increase in the number of devices connected to each other. All these data can be managed by massive data analysis techniques. IoT technology, thanks to its connectivity, provides infinite possibilities for Big Data.