2014
Trilles-Oliver, Sergio; Belmonte-Fernández, Óscar; Díaz-Sánchez, Laura; Huerta-Guijarro, Joaquín
Mobile Access to Sensor Networks by Using GIS Standards and RESTful Services Journal Article
In: IEEE Sensors Journal, vol. 14, no. 12, pp. 4143–4153, 2014, ISSN: 1530-437X, (IF: 1.762, Q2).
Abstract | Links | BibTeX | Tags: Air quality sensors, Geographic Information Systems (GIS), interoperability, meteorological sensors, RESTFul, sensor observation services, Standards
@article{TrillesOliver2014,
title = {Mobile Access to Sensor Networks by Using GIS Standards and RESTful Services},
author = { Sergio Trilles-Oliver and Óscar Belmonte-Fernández and Laura Díaz-Sánchez and Joaquín Huerta-Guijarro},
url = {http://hdl.handle.net/10234/145376},
doi = {10.1109/JSEN.2014.2339931},
issn = {1530-437X},
year = {2014},
date = {2014-01-01},
journal = {IEEE Sensors Journal},
volume = {14},
number = {12},
pages = {4143--4153},
abstract = {There is an increasing deployment of sensor networks that measure the physical state of the environment. These networks provide large volumes of data in many different formats, resolutions, and scales. There are different types and character of data: from meteorological conditions to air quality and the concentrations of pollutants due to human activity, such as transportation or other industry-related actions. Current sensor networks publish this data in different formats. This implies not offering standard access to different data sources. These varieties of formats lead to an interoperability problem. Also, access to these sources is not fast and agile; it prevents access through mobile devices. The work in this paper aims at increasing the interoperability and improving accessibility of data provided by sensor networks. In this way, these data can be employed by different devices and with diverse context requirements, such as specific location and time. To address this problem, geographic information system services, such as the sensor observation service, in conjunction with representational state transfer architecture are used. A standard-based solution that increases interoperability is presented. It also allows a better integration of data that has already been published in different semistructured formats in order to be used by various platforms (web or mobile). Furthermore, this system adds value to original sensor data in order to assist in the decision making process. Finally, to illustrate how to use these services, a mobile application that shows the hot-spots following air quality index has been developed},
note = {IF: 1.762, Q2},
keywords = {Air quality sensors, Geographic Information Systems (GIS), interoperability, meteorological sensors, RESTFul, sensor observation services, Standards},
pubstate = {published},
tppubtype = {article}
}
There is an increasing deployment of sensor networks that measure the physical state of the environment. These networks provide large volumes of data in many different formats, resolutions, and scales. There are different types and character of data: from meteorological conditions to air quality and the concentrations of pollutants due to human activity, such as transportation or other industry-related actions. Current sensor networks publish this data in different formats. This implies not offering standard access to different data sources. These varieties of formats lead to an interoperability problem. Also, access to these sources is not fast and agile; it prevents access through mobile devices. The work in this paper aims at increasing the interoperability and improving accessibility of data provided by sensor networks. In this way, these data can be employed by different devices and with diverse context requirements, such as specific location and time. To address this problem, geographic information system services, such as the sensor observation service, in conjunction with representational state transfer architecture are used. A standard-based solution that increases interoperability is presented. It also allows a better integration of data that has already been published in different semistructured formats in order to be used by various platforms (web or mobile). Furthermore, this system adds value to original sensor data in order to assist in the decision making process. Finally, to illustrate how to use these services, a mobile application that shows the hot-spots following air quality index has been developed