Location Intelligence for Augmented Smart Cities Integrating Sensor

Web and Spatial Data Infrastructure (SmaCiSENS)

Devanjan Bhattacharya and Marco Painho

Nova Information Management School, Universidade Nova de Lisboa, Lisbon, Portugal

Keywords: Spatial Data Infrastructure, Sensor Web, Geographical Information System, Smart Cities, Knowledge Based

System, Expert System, Spatial Technologies.

Abstract: 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.

1 INTRODUCTION

Everything happens somewhere and some-whence.

Spatial and temporal aspects of data lead to critical

insights into the information contained in it.

Nowadays it is increasingly imperative to capture

data at all spatial scales, local to global, and extract

useful information from it ubiquitously and regularly.

For the information to have an impact, it should be

extracted in real-time or as near to real-time as

possible. It is where location plays a vital part by

rendering meaningful ways of understanding

relationships between datasets quickly.

The benefits of associating all developmental

works with spatial attributes is universally

acknowledged now, hence the next big leap will be to

research a platform that manages real-time sensor

data and provides critical spatial analytics. Both the

aspects are size intensive and time consuming so an

automated, compartmentalized yet integrated

solution is the optimal way forward. The research

creates background for describing a spatially aware

sensor web SmaCiSens, a system for smart city

enhancements and interdisciplinary processes for

sustainable development. It proposes a

multidimensional distributed spatial platform using

open source geo-datasets which involves web-

geoinformatics for creating schema and interface for

mapping under Open Geospatial Consortium (OGC)

standards by interlinking models and datasets. Spatial

systems are needed for real time analysis and

information on events and developments through

Open Source Geographical Information System (OS-

GIS) platform.

Location is involved with everything, hence a

spatial system is vital for better urban information

management and spatial data infrastructure(SDI)

creation. This also benefits when huge databases are

created and consulted regularly for region planning at

different scales through satellite images and maps of

locations. There is need for spatially referenced data

creation, analysis, and management. The paper

describes existing state-of-the-art towards

development of a system with sensor-web(SW)

access utilizing geomatics for sustainable societies.

There has been a need to develop automated integral

spatial systems to sense and categorize events and

issue information that reaches users directly. At

present, no web-enabled spatial sensor information

system exists which can disseminate messages after

282

Bhattacharya, D. and Painho, M.

Location Intelligence for Augmented Smart Cities Integrating Sensor Web and Spatial Data Infrastructure (SmaCiSENS).

DOI: 10.5220/0006786102820289

In Proceedings of the 4th International Conference on Geographical Information Systems Theory, Applications and Management (GISTAM 2018), pages 282-289

ISBN: 978-989-758-294-3

events evaluation in real time. The research work

formalizes a notion of an integrated, independent,

generalized, and automated geo-event analyzing

system making use of geo-spatial data under popular

usage platform. Integrating SW with SDI enables to

extend SDIs with sensor web enablement (SWE),

converging geospatial and built infrastructure, and

implement test cases with sensor data and SDI.

The previous works in this domain either

focused on SDI and described an application, or on

sensor web describing its application, mainly

suggesting the development of such ideas but have

not created an integrated system generalized enough

for multi-case use. Most such theories have put

forward specific instances of usability and hence the

research has stopped at that. The current work

describes the research gaps that could be overcome by

the development of a generalized system

SmaCiSENS and the broader research benefits arising

out of the endeavor. The aim is to create a generalized

location enabled platform for use-cases analysis in

smart cities environments ranging from automated

natural hazard monitor webGIS with internet-SMS

warning, climate monitoring, to urban design,

intelligent transportation systems, disaster

management SDI.

2 BACKGROUND

Systems have been proposed to handle the seemingly

infinite and continuous data being generated but have

fallen short of universal adoption due to several

reasons, apart from lacking domain knowledge hence

not being decisive, being offline or disconnected, case

specific, temporally disjoint, and unintegrated.

Further, to be fully accepted as a system the solution

should provide decision-making information in least

supervised manner. It should have some degree of

autonomous functionality. With the advent of digital

initiatives and information technology (IT)

implementations like internet of things (IoT) and

sensors, administrations today are faced with a

challenge of large volumes of unstructured data

coming from multiple disparate sources which are

difficult to make sense of. Traditional systems report

problems but are not capable of “showing them”,

which adds to the troubles of the administration in a

situation where they are flooded with high volume

data constantly. In many cases by the time

administration has figured out the exact location

along with an action plan, the ground situation would

have already changed.

With the advent of sensors for monitoring,

data collection is ubiquitous are at unprecedented

levels. The enormous volumes of data being collected

constantly, create mounting challenges for optimal

data processing and information retrieval (Janowicz

et al., 2010). Furthermore, the spatial aspect of data is

being largely underutilized in processing, although

the importance of spatial decision-making is now

widely accepted (Nativi and Bigagli, 2009). It has

been proven that spatial analysis of data gives more

meaning to the information extraction and hence

enables easier assimilation of large volumes of data

(Reed and Reichardt, 2008). Presently the systems

that implement such processes are limited in effect by

not utilizing all the data due to their standalone

nature, offline or disconnected design, lack of spatial

capabilities, unintegrated approach, and temporally

disjoint.

For example, in spatial data processing the

major hurdles are that the different research groups

globally are processing their data in silos, most of the

time repeating same processes at each location,

creating similar metadata each time, duplicating data,

thereby falling behind the rushing stream of more

incoming data (Taylor and Parsons, 2015). The

solution could be addressed through integrating: data

source, spatial data platform, data understanding,

knowledge base, inferencing and visualization into a

single, well-connected online real-time system. Such

a spatial expert system(ES) with knowledge

bases(KB) will not only serve the critical research of

spatializing developmental works but do so to any

research relying on real-time data capture and

analysis with spatial domain of data being the unique

enabler. Several important sources over the years

have heavily stressed the need for developing a

system capable of encapsulating the entire essence of

geospatial studies in one platform which can be open,

shareable, knowledgeable, and contributable globally

(Maguire and Longley, 2004).

Spatial data are dramatically increasing in

volume and complexity, just as the users of these data

in the scientific community and the public are rapidly

increasing in number (Laurini, 2017). A new

paradigm of more open, user-friendly data access is

needed to ensure that society can reduce vulnerability

to spatial data variability and change, while at the

same time exploiting opportunities that will occur

(Bröring et al., 2011). The burgeoning types and

volume of spatial data alone constitute a major

challenge to the spatial research community. As a

result, spatial scientists must not only share data

among themselves, but they must also meet a growing

obligation to facilitate access to data for those outside

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283

their community and, in doing so, respond to this

broader user community to ensure that the data are as

useful as possible (Bishop, 2015). Although research

scientists have been the main users of these data, an

increasing number of resource managers need and are

seeking access to spatial data to inform their

decisions, just as a growing range of policy-makers

rely on spatial data to develop spatial change

strategies. With this gravity comes the responsibility

to curate spatial data and share it more freely,

usefully, and readily than ever before (Chen et al.,

2015).

Almost every decision that an individual or

organization makes has some geospatial component.

Almost any piece of information stored in a database

has a location attribute. Sensor web(SW) and spatial

data infrastructures(SDI) show great promise for

building and maintaining a sustainable and changing

society (Giuliani et al., 2017), which often needs to

acquire spatial data through sensors and extract

information using data analysis from SDI for better

decision-making. The full potential of SW and IoT

can only be reached with spatial intelligence

integrated to them (Mayer and Zipf, 2009). The

geospatial industry is also trying to ride the big wave

and exploit the market (Li et al., 2015). Moreover the

concept of geo-data democratization is gaining

momentum and is being described as the next big

disruption (Pantazis et al., 2011). But the long-term

solution has eluded everybody so far and only case

specific systems are being designed. Hence the

motivation in SmaCiSENS is to develop a system

using the core spatial technologies of SDI integrated

with SW and IoT through standard architectures and

utilising expert domain KBs.

To study a system designed on SDI, spatial

KBs and SW, one has to survey each of them

separately since hardly an integrated system exists

(Bhattacharya and Painho, 2017). Considerable

research and development has been carried out in SDI

in past years (Bhattacharya et al., 2017). For example,

Global Earth Observation System of Systems

(GEOSS, 2017), enviroGRID, 2017, and

COPERNICUS, 2017, provide high quality spatial

data to users in user-friendly geo-visualization

platforms. The Infrastructure for Spatial Information

in Europe, INSPIRE, 2017, adopts service

technologies for building its spatial information

infrastructure. Other systems are high-level

middleware services and domain-specific services for

problem-solving and scientific discovery in

infrastructures. For example, the Group on Earth

Observation (GEO) Model Web initiative proposes to

provide environmental models as services and

integrating distributed models in infrastructures. Data

provenance is added into SDI to capture and share the

derivation history of geospatial data products, which

is important in evaluating the quality of data products.

Others propose the ontology approach for geospatial

resource discovery in SDI. The Open Geospatial

Consortium (OGC) is leading and coordinating the

efforts of international organizations and enterprises

to develop interoperable geospatial services. A series

of standard-based interface specifications are already

available, including Web map services (WMS), Web

feature service (WFS), Web coverage services

(WCS), catalogue services for the Web (CSW), and

Web processing service (WPS).

Research on the spatial capabilities

enhancement of sensor web, in a limited manner, are

reported in Liang and Huang, 2013. There are some

ongoing activities that are defining architectures and

best practices for integrating sensor networks and

observed data into existing and new SDI applications

eg. Sensor Systems Anywhere, (SANY, 2009), as a

Framework Programme 6 (FP6) integrated project

focuses on interoperability of in-situ sensors and

sensor networks. An instance of utilizing sensor web

with spatial knowledge was reported in Liang and

Huang, 2013 where a system was developed to

provide a graphical user interface to sensor data and

provisions for spatial reference to that data. The

research in Liang et al., 2005 reported a primitive

system of sensor and web mapping was discussed that

worked on web mapping interfaces. SmaCiSENS

goes much ahead of such efforts by combining

various research threads into one system having

semantic KB robustness for a variety of data formats

and sources, generalized interfaces for connectivity to

spatial functions, a comprehensive and extensive SDI

framework, and real-time data capture and

processing.

SmaCiSENS can have a real edge over the

currently under development projects of similar kind

in terms of impact on urban development, hazard

management and mitigation, digital mapping,

location based services, core services and much more.

Moreover, the ever-increasing repository of data are

held in silos and are not shareable. The apprehension

of losing data integrity also hinders the process of

sharing. Typically, the fault is put on lack of easy

tools to do that. Hence, we are looking at a two-faced

problem where the solution would have to tackle not

only data from multiple sources but also provide data

management platform. Such a system would

invariably need a spatial data infrastructure (SDI) as

its backbone framework. And it should be able to

harness the sensor web. Essentially the system would

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call for an integration of Sensor Web(SW) and SDI.

Both SDI and SW are web-enabled so by the logic of

extension it should be possible to connect SDI and

sensor web. With the inclusion of domain knowledge

base (KB) about the data and problems the system

becomes ready to infer solutions. And the solution not

only helps with administration, the situation holds

true with many other areas of immediate attention and

action, like climate modeling, geohazard

management, global changes that are taking place, all

these and more, have huge spatial implications that

could be understood quickly with location

intelligence through a geo-enabled expert

system(GES). The best solution is implementation of

a spatial knowledge based expert system (Laurini,

2017).

Geo-visual platforms discussed in Mayer and

Zipf, 2009. integrated sensors with SDI for creating

3D city view. But the work only applied to a few

specific sensor datasets. The generalized integration

of sensor web and SDI has been reported by Bröring

et al., 2011 and the papers elaborate on the underlying

logics. A geosensor web concept has been discussed

in Maguire and Longley, 2004 with SDI in

background but the discussion is theoretical and the

development aspects from this theory have been put

together by (Bhattacharya and Painho 2016) setting

the tone for SmaCiSENS. Further, the semantic

requirements of a spatial system have been suggested

in Janowicz et al., 2010.

A detailed deployment of enhanced SDIs is

presented in (Chen et al., 2015) where the need for

scalability of ontologies to spatial datasets is proven.

In another related publication, the interlinking of

open geodata sets has been described (Taylor and

Parsons, 2015) and the functional and usable

enhancements are proven. A proof of concept funded

by NASA has described a geospatial sensor web

architecture (United Nations, 2010) but it has not

been fully developed yet, pointing to the importance

of the vision which has been picked up by this

proposal addressing SmaCiSENS. Distributed

architectures for socio-economic spatial studies have

been proposed by (Nativi and Bigagli, 2009) but a

developed system has not been realized yet.

Several schemas for cyberinfrastructure have

been reported (Liang and Huang, 2013; 2005) but the

development is still to be done. According to Laurini,

2017 the main reason such a system as SmaCiSENS

has not been achieved yet is the difficulty to merge

spatial geometry and topology inherently with

traditional analysis and create an effective spatial

inference engine. But through this research work the

solution is demonstrated in creating algorithms and

ontologies utilizing geometry, topology, and location

arithmetic and shown that SmaCiSENS can be the

system to address all the challenges.

The research questions that have been discussed

through the present work are: i) how to build open

source spatial ontologies for spatial phenomenon

using causative factors ii) how to connect ontologies

to intelligent inferencing logics iii) how to build

specialized knowledge bases for a generalized spatial

KBES iv) how to apply the system to automate

procedures viz. urban and natural v) how to integrate

sensor web(SW), other data sources and spatial data

infrastructure(SDI) with open source technologies.

SmaCiSENS aims to address these challenges

through developing a framework that houses data,

metadata, understanding of the data, knowledge to be

applied on the data, and output from the data.

SmaCiSENS would enable a distributed spatial

framework that targets to deliver spatial decisions to

start with but would administer spatial functionalities

to a variety of social needs.

3 METHODOLOGY

The methodology of development of SmaCiSENS is

modular in structure (Fig. 1). The overall architecture

depends on the creation of knowledge bases for

natural hazards (or any event) to deduce the specifics

of the occurrence. The methodology is that the input

module of the system implements extraction, based

on legend matching, of information about causative

factors from thematic maps, satellite images, and GIS

layers. Understanding module addresses expert

knowledge rules (qualitative approach) in expert

module, which conducts pixel-based reclassification

of input (compatible to KB), results in evaluation of

intensity/effect of hazard(any situation) on ratings of

causative factors (deterministic method) out from

Output module and communication to user is

achieved through Communication module which also

gives feedback to improve inputs and geospatial

information improvement, since GIS module has bi-

directional data flow. The interactive graphical user

interface (GUI) allows for data visualization,

manipulation and sharing.

Open-Source Geographical Information

System (OS - GIS) and distributed architecture based

platform such as GeoNode allows 3-dimensional

(3D-GIS) development. To develop on an open

source platform is extremely vital when huge

databases are to be created and consulted regularly for

region planning at different scales particularly

satellite images and maps of locations (Bhattacharya

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

285

and Painho 2016). There is a big need for spatially

referenced data creation, analysis and management.

Some of the salient points of SmaCiSENS are that

GeoNode is an open source platform facilitating the

creation, sharing, and collaborative use of geospatial

data (Figure 2). The project aims to surpass existing

spatial data infrastructure solutions by integrating

robust social and cartographic tools; at its core, the

GeoNode is based on open source components

GeoServer, GeoNetwork, Django, and GeoExt that

provide a platform for sophisticated web browser

spatial visualization and analysis.

Figure 1: Schema SmaCiSENS modular architecture & data

flow.

The elements of Sensor Web Enablement (SWE)

services could be understood as:

• a service for retrieving sensor 'observation' data and

meta-information, the so-called 'Sensor Observation

Service' (SOS); • a service for sensor planning and

executing tasks, called the 'Sensor Planning Service'

(SPS); • a service that allows users to subscribe to

specific alert types, known as the 'Sensor Alert

Service' (SAS); • a service that facilitates

asynchronous message interchange between users

and services, and between two OGC-SWE services,

called the 'Web Notification Service' (WNS). And

that of web mapping (SDI) services: Web Map

Service (WMS); Web Feature Service (WFS); Web

Coverage Service (WCS); Web Map Context

(WMC); Catalogue; Metadata. It is necessary to have

a one-to-one correspondence between the elements of

Sensor Web and SDI. A possible environment for

establishing such interfaces could be GeoServer and

GeoNode which provide an OGC compatible data

store that can speak WMS, WFS, WCS and others in

common formats like GML, GeoJSON, KML and

GeoTiff. It can be connected to different spatial

backends including PostGIS, Oracle Spatial, ArcSDE

and others.

Figure 2: The Geonode architecture used in Figure 1.

The Catalog, GeoNetwork, provides a standard

catalogue and search interface based on OGC

standards. It is used via the CSW interface to create

and update records when they are accessed in

GeoNode. It talks to the other components via HTTP

and JSON as well as standard OGC services

(Bhattacharya and Painho 2017, 2016). The

integration of sensor web and SDI in open source

domain could be achieved possibly by setting up one

to one correspondence between their services through

functions calling and methods calling.

The main research is on the inclusion of spatial

components of geometry and topology with

conventional analysis. Through the inclusion of SDI,

spatial KBs, and spatial inference engine the goal of

a spatial expert system can be achieved. The

framework would work on open technologies such as

GeoNode, GeoServer, Apache Kafka/Storm/Hadoop,

Apache CloudStack, Open Street Map, PostgreSQL,

PostGIS, Java, QGIS, TeraData, Presto, SPARQL,

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Python, CKAN, RDF, OWL and MongoDB to

manage the top level, middle level and lower level

architecture (Fig. 3). This architecture makes a

distributed spatial system possible where the location

knowledge is advanced by utilising databases and

ontologies(Fig. 4).

Figure 3: Capturing sensor web for SmaCiSENS.

The topological and geometric matching of big open

data has been achieved in a moderately non-trivial

case as reported in (Bhattacharya and Painho 2017).

Additionally the ontological representation of natural

phenomenon for expert systems has been developed

for specific cases. The object-oriented ontological

framework has been planned to be hybrid of object-

based and rule-based as reported by Pantazis, 2011.

The feasibility analysis behind SmaCiSENS involves

significant works on the spatial data analysis

algorithms (Bhattacharya and Painho 2017) which

benefit from the semantic logic being robust and

research through SmaCiSENS has to proceed in

similar directions. Also related works on user

generated spatial data and crowd sourced VGI prove

the need for integrated platform for spatial analysis

(Laurini, 2017). The developmental research work

under the proposal draws methodologies from the

following previous works and delivers functionalities

such as: integrating in a single platform multiple

spatial data sources with analytics capabilities

(Bhattacharya and Painho 2016); maintaining a

spatial knowledge base with inferencing capabilities

(Bhattacharya and Painho 2017); integrating sensor

web with spatial data infrastructure (Liang and

Huang, 2013); providing a platform for visualizing

and testing different models (Liang et al., 2005);

distributed processing of spatial big data.

Figure 4: Sample ontology for Things (IoT).

4 DISCUSSIONS

SmaCiSENS will deliver the current state-of-the-art

with respect to sensor web data utilization and spatial

data infrastructure (SDI) applications for

developmental activities in smart cities. Further it is

seen that singly as a technology sensor web as well as

SDI each is stagnating on their own. The need at

present is to explore possibilities of merging the two

so that each can feed on the strengths of each other

and synergize their respective fields. For the future

smart cities this could be a huge advantage as we look

towards harnessing the potential of ubiquitous sensor

data through intelligent apps. The apps would get a

ready framework in SmaCiSENS for data sources as

well as spatial processing platform with cloud,

virtualization and data federation capabilities. It is

evident that within a short time, the vast majority of

all geospatial content will be from sensor networks

and systems, with the volume of available sensed

content orders of magnitude larger than at present.

SmaCiSENS poses a very big challenge but it is high-

gain as well. The provision of a fail-safe process is

also there, just in case, where we develop

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

287

SmaCiSENS on the specific applications dataset

only.

Optimal decision making relies not only on the

ability to fuse and apply core geospatial information,

but also the ability to discover, access and apply real

time information from sensors and sensor networks.

A gamut of information about the environment - land,

air, water, weather, climate and natural and man-

made risks can be harnessed by seamless and rapid

access to sensors. In addition, sensors are critical

components of building, transportation, utility and

industry infrastructure.

The ability to harness and render this

information in a location context is a major challenge.

Until recently though, there were no facilitating

standards to make it easier to discover, access and

integrate this information. Therefore, a consistent set

of encoding and interface standards are mandatory for

adapting and integrating sensor networks into an SDI

application. Both, SDI (web mapping) standards and

sensor web enablement standards from OGC, have to

meet at a common ground and connect together. The

integration of sensor web and SDI in open source

domain could be achieved possibly by setting up one

to one correspondence between their services through

functions calling and methods calling.

SmaCiSENS can deliver an integrated sensor

web and SDI which can solve a lot of challenges that

stand-alone, disconnected, case-specific, and

customized systems lack. The next level of capability

for both SDI and sensor web would be to evolve into

a new realm of a location enabled and semantically

enriched Geospatial Web or Geosemantic Web but

additionally with spatial analytics capabilities. The

integration can be done through merging the common

OGC interfaces of SDI and Sensor Web. Through

SmaCiSENS, Sensor Web and SDI are going to keep

expanding in the next decade. Sensors are going to be

so ubiquitous that similar to the world wide web the

addition of vast number of sensors will keep

happening like new data sources of present internet.

The concept of SmaCiSENS has to keep evolving to

help overall development.

5 CONCLUSIONS

SmaCiSENS addresses smarter living conditions for

citizens and better management of resources for

administrators and industries by spatially enabling the

new data sources coming up. Currently the vast

capacity of spatially referencing newer sources of

data and information is not being done. SDI concept

has hit a roadblock towards further development.

One very smart and efficient way forward is to

integrate SDI with new data sources sensors that are

coming up and creating a sensor web of spatially

oriented information. The results of data processing

and infrastructure building will be of interest to

current and future stakeholders so that has to be

formulated according to the university rules.

Expected users will be scientists developing systems

like EU INSPIRE directives, OGC, ISO, public users

and other agencies.

SmaCiSENS can have very attractive market

and research and academic scope in spatial

developments. Applicable to virtually all activities of

commerce and governance. Sensor Web and SDI are

going to keep expanding in the next decade. It is

envisioned that sensors are going to be so ubiquitous

that like the WWW the addition of vast number of

sensors will keep happening like new data sources of

present internet. The concept of SmaCiSENS has to

keep evolving step by step in the years to come to help

overall development of a nation. The future scope is

immense for a system such as SmaCiSENS what with

various applications such as defense application of

analyzing different sensor data; health applications of

medical sensor data; and last but not the least, spatial

change studies globally.

ACKNOWLEDGEMENTS

D. Bhattacharya has been funded by the European

Commission through the GEO-C project H2020-

MSCA-ITN-2014, Grant Agreement number 642332,

http://www.geo-c.eu/.

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