- Sensors Journal
- March 2019
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- In the last two decades, data and information fusion has experienced significantdevelopment due mainly to advances in sensor technology. The sensors provide a continuousflow of data about the environment in which they are deployed, which is received and processed tobuild a dynamic estimation of the situation. With current technology, it is relatively simple to deploya set of sensors in a specific geographic area, in order to have highly sensorized spaces. However, tobe able to fusion and process the information coming from the data sources of a highly sensorizedspace, it is necessary to solve certain problems inherent to this type of technology. The challengeis analogous to what we can find in the field of the Internet of Things (IoT). IoT technology ischaracterized by providing the infrastructure capacity to capture, store, and process a huge amountof heterogeneous sensor data (in most cases, from different manufacturers), in the same way that itoccurs in data fusion applications. This work is not simple, mainly due to the fact that there is nostandardization of the technologies involved (especially within the communication protocols usedby the connectable sensors). The solutions that we can find today are proprietary solutions thatimply an important dependence and a high cost. The aim of this paper is to present a new opensource platform with capabilities for the collection, management and analysis of a huge amount ofheterogeneous sensor data. In addition, this platform allows the use of hardware-agnostic in a highlyscalable and cost-effective manner. This platform is called Thinger.io. One of the main characteristicsof Thinger.io is the ability to model sensorized environments through a high level language thatallows a simple and easy implementation of data fusion applications, as we will show in this paper.
- iot middleware; scalabillity; data fusion applications