Integration of hydrological observations into a Spatial Data Infrastructure under a Sensor Web environment

Augmented Smart Cities Integrating Sensor Web and Spatial Data Infrastructure (SmaCiSENS)

10.20944/preprints201711.0172.v1 ◽

2017 ◽

Author(s):

Devanjan Bhattacharya ◽

Marco Painho

Keyword(s):

Spatial Data ◽

Climate Modeling ◽

Smart Cities ◽

Spatial Data Infrastructure ◽

Sensor Data ◽

Global Changes ◽

Multiple Sources ◽

Data Infrastructure ◽

Management Platform ◽

Sensor Web

Spatio-temporal aspects of data lead to critical information. Sensors capture data at all scales continually so it is imperative that useful information be extracted ubiquitously and regularly. Location plays a vital part by helping understand relations between datasets. It is crucial to link developmental works with spatial attributes and current challenge is to create an open platform that manages real-time sensor data and provides critical spatial analytics atop expert domain knowledge provided in the system. That is a two-faced problem where the solution tackles not only data from multiple sources but also runs data management platform, a spatial data infrastructure(SDI) as backbone framework able to harness sensor web(SW). The paper proposes development of such a globally shared open spatial expert system(ES), SmaCiSENS, a first of a kind geo-enabled knowledge based(KB) ES for multiple fields, smarter cities to climate modeling. SmaCiSENS is integration of SW and SDI with domain KB on data and problems, ready to infer solutions. The paper describes an architecture for semantic enablement for SW, SDI; connect interfaces, functions of SDI and SW, and sensor data application program interfaces (APIs) to better manage climate modeling, geohazard, global changes, and other vital areas of attention and action.

Download Full-text

SMART CITIES INTELLIGENCE SYSTEM (SMACiSYS) INTEGRATING SENSOR WEB WITH SPATIAL DATA INFRASTRUCTURES (SENSDI)

ISPRS Annals of Photogrammetry Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-annals-iv-4-w3-21-2017 ◽

2017 ◽

Vol IV-4/W3 ◽

pp. 21-28 ◽

Cited By ~ 2

Author(s):

D. Bhattacharya ◽

M. Painho

Keyword(s):

Real Time ◽

Spatial Data ◽

Smart Cities ◽

Spatial Data Infrastructure ◽

Sensor Data ◽

Data Infrastructure ◽

Intelligence System ◽

Sensor Web ◽

Data Infrastructures ◽

Spatial Data Infrastructures

The paper endeavours to enhance the Sensor Web with crucial geospatial analysis capabilities through integration with Spatial Data Infrastructure. The objective is development of automated smart cities intelligence system (SMACiSYS) with sensor-web access (SENSDI) utilizing geomatics for sustainable societies. There has been a need to develop automated integrated system to categorize events and issue information that reaches users directly. At present, no web-enabled information system exists which can disseminate messages after events evaluation in real time. Research work formalizes a notion of an integrated, independent, generalized, and automated geo-event analysing system making use of geo-spatial data under popular usage platform. Integrating Sensor Web With Spatial Data Infrastructures (SENSDI) aims to extend SDIs with sensor web enablement, converging geospatial and built infrastructure, and implement test cases with sensor data and SDI. The other benefit, conversely, is the expansion of spatial data infrastructure to utilize sensor web, dynamically and in real time for smart applications that smarter cities demand nowadays. Hence, SENSDI augments existing smart cities platforms utilizing sensor web and spatial information achieved by coupling pairs of otherwise disjoint interfaces and APIs formulated by Open Geospatial Consortium (OGC) keeping entire platform open access and open source. SENSDI is based on Geonode, QGIS and Java, that bind most of the functionalities of Internet, sensor web and nowadays Internet of Things superseding Internet of Sensors as well. In a nutshell, the project delivers a generalized real-time accessible and analysable platform for sensing the environment and mapping the captured information for optimal decision-making and societal benefit.

Download Full-text