RESEARCH Open Access

An architecture for integrated intelligence inurban management using cloud computingZaheer Khan1*, David Ludlow1, Richard McClatchey1 and Ashiq Anjum2

Abstract

With the emergence of new methodologies and technologies it has now become possible to manage largeamounts of environmental sensing data and apply new integrated computing models to acquire informationintelligence. This paper advocates the application of cloud technologies to support the information,communication and decision making needs of a wide variety of stakeholders in the complex business of themanagement of urban and regional development. The complexity is evident in the socio-economic andenvironmental interactions and impacts embodied in the concept of the urban-ecosystem. This highlights theneed for more effective integrated environmental management systems. A key to understanding the nature ofintegrated environmental management systems is the identification of the need for horizontal integration ofinformation across sectoral inter-agency boundaries at the local level, and the need for vertical coordinationbetween levels of governance. This paper offers a user-oriented approach to the specification of requirements forthe effective management of urban areas and the potential contributions that can be supported by cloudcomputing. The commonality of the influence of the drivers of change at the urban level offers the opportunity forthe cloud computing community to develop generic solutions that can serve the needs of hundreds of citiesthroughout Europe and indeed globally. In this respect, different cloud based architecture scenarios are presentedwhich utilise capabilities compliant to various standards in generating information and intelligence for urbangovernance.

Keywords: Information intelligence, Environmental monitoring, Data harmonisation, Service integration, Cloud andcomputing standards

IntroductionIntegrated environmental intelligence can be described asthe capability of a system to access, process, visualise andshare data (spatial and non-spatial), metadata and modelsfrom various domains relating to land-use/cover, biodi-versity, atmosphere as well as socio-economic aspects ofthe city for various purposes. Examples include the iden-tification of environmental change and causality, futureenvironmental trend analysis, socio-economic develop-ment, policy development and collaborative decision-making. Even if, there is a profusion and explosion in thegrowth of environmental data, this data mostly is frag-mented and un-harmonised. Furthermore, such dataexist in proprietary and open systems, less compliant to

standards and sometimes requires extensive computingcapacity, constraining its use across different platforms.This suggests that integrated environmental monitoringrequires compliance to standards, data harmonisationand service interoperability together with extensive on-demand processing and storage capacities in order toanswer scientific and policy related questions.In the above context, this research is an attempt to

bridge the gaps between fragmented cross-thematic envir-onmental information, with a particular focus on theurban environments. In this regard, it aims to develop acloud-based framework that facilitates data accessibilityand storage across platforms, and provides necessary on-demand computational resources for the required proces-sing, simulation and visualisation tasks. This frameworkalso aims to provide mechanisms for integrated informa-tion intelligence necessary to resolve the highly complexcross thematic multiple impact challenges associated with

* Correspondence: Zaheer2.Khan@uwe.ac.uk1Faculty of Environment and Technology, University of the West of England,Bristol, UKFull list of author information is available at the end of the article

Khan et al. Journal of Cloud Computing: Advances, Systems and Applications 2012, 1:1http://www.cloud-casa.com/content/1/1/1

© 2012 Khan et al; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons AttributionLicense (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium,provided the original work is properly cited.

climate change, as well as measures to ameliorate theseimpacts associated with climate change mitigation andadaptation. More specifically, from a user perspective, theaims include:- informing complex policy and science related ques-

tions which cannot be addressed directly from non-inte-grated information sources;- studying different environmental phenomena by

investigating the causality relationship between environ-mental variables;- enabling access to cross-thematic environmental

information for climate change mitigation and/oradaptation;- facilitating various stakeholders in generating envir-

onmental indicators to support policy interventions,assessment of the state of the urban environment, deci-sion-making and policy implementation;- empowering citizens in participatory environmental

data collection and collaborative decision making pro-cesses and- designing and utilising state of the art user-centred

information technology tools and techniques in relationto all of the above.From a technological perspective, the above aims help

in identifying the required system capabilities which pri-marily necessitate acquisition and harmonisation of

cross-thematic spatial and non-spatial data (using stan-dards) in order to provide uniform access to interoper-able information for various purposes. Further, itindicates that in order to deliver integrated intelligencederived from an increasing volume of environmentaldata, significant storage capacity is needed to preservethe environmental data and additional computationalpower is necessary to process this data. Furthermore, itsuggests that specialised processing and visualisationservices are needed to analyse the data in order to sup-port enhanced decision-making. For instance, when theoutput from processed data e.g. information maps, scalefrom a local (i.e. City) to national or regional levels forcomparative environmental indicator analyses, theamounts of data and the number of processing require-ments typically increase. In this case, the cloud comput-ing paradigm offers an excellent means of fulfilling theabove requirements. It can provide an on-demand fra-mework to make systematic the information flow fromdata collection to analysis, monitoring and assessmentbased on standard guidelines such as ISO 19100 Series,the Open Geospatial Consortium (OGC) [1] standardsand the INfrastructure for SPatial InfoRmation in Eur-ope (INSPIRE) [2] specifications. Figure 1 depicts bothuser and technology perspectives to achieve integratedinformation intelligence for environmental monitoring.

Figure 1 User-oriented and Technological-based perspective.

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The challenge here is to introduce appropriate mapping,harmonisation, integration and utilisation of suitabletools and technologies in a cloud environment in orderto fulfil stakeholder requirements.One of the basic elements for Integrated Environmen-

tal Monitoring (IEM) is the processing of ‘data’, which inthe case of urban environments is mostly geo-tagged (e.g.in space and time) and combined with socio-economicand demographic elements. In this respect the OGC pro-vides a sound foundation of standards in the distributedgeo-processing domain. Recently OGC highlighted thesignificance of cloud computing for the geospatial com-puting world by indicating a number of application areaswhere cloud computing is well suited, including the mod-elling of complex environmental applications, the fusionof existing preserved data and new sensor data for deci-sion-making, enterprises, scientific and governmentalstorage and compute-intensive applications [3]. Further-more, it is highlighted in [3] that exchanging geospatialdata between systems (clouds or user applications) is notsufficient rather it is also necessary to understand themeaning of data. For example the existence of thousandsof spatial reference systems; various representations forspatial data including raster, vector, points and others;semantic heterogeneity; proprietary interfaces and vendorspecific encodings also add to the complexities. All thisrequires integrated solutions for information intelligencefor environmental monitoring.The remainder of this paper is structured as follows. In

next section background and related work are presented.Then a user perspective is presented briefly in order toderive technological capabilities in clouds. After this wepresent a proposed architecture for IEM followed by dif-ferent implementation scenarios using cloud computing.Then we briefly discuss integrated intelligence and deci-sion-making approaches followed by an assessment ofthe proposed architecture by identifying its benefits,potential research and technological challenges and anoverall reflection on the expected research outcomes ispresented. Finally, we conclude and identify futureresearch possibilities.

Background and related workThe development of data harmonisation tools, decision-support systems, visualisation and simulation tools forspecific environmental thematic domains is challenging.But the development and implementation of an inte-grated intelligence framework that can accommodatecross-thematic data harmonisation for various applica-tion domains using a variety of tools, components andservices is even more challenging.In the above context, the Global Earth Observation

System of Systems (GEOSS) [4] and Ground EuropeanNetwork for Earth Science Interoperations-Digital Earth

Communities (GENESI-DEC) [5] are examples of on-going initiatives for integrating domain specific environ-mental data from various sources. More specifically, therecent development of new methodologies for informa-tion acquisition (for example, the Shared EnvironmentalInformation System (SEIS) [6], Global Monitoring forEnvironment and Security (GMES) [7], and informationprovided by member states to European EnvironmentAgency (EEA) arising from various Directives includingas Air quality, Noise and Water) and the increase ofcomputing capacities (e.g. sensor nets, smart phones,Web 2.0, grids and clouds) opens new fields for moni-toring and assessment with high information flow andthe capacity to manipulate it.From a technical perspective, managing increasing data

volumes, the varying needs of scalable computingresources, harmonisation and interoperability processes,tools and services, visualisation and compliance to stan-dards are the major drivers in developing an integratedsystem. In this regard, the cloud computing paradigm is asuitable cyber-infrastructure to fulfil most of the aboverequirements due to its characteristics such as elasticity,on-demand processing, a pay-as-you-use cost model,lower IT management costs etc. [8]. For example, recentlyit has been indicated that £21bn - the annual UK govern-ment IT budget - could be reduced by £3.2bn out of acentral budget of £16bn by putting government applica-tions and services in the cloud such as the proposed G-cloud [9]. The scope of cloud computing may vary from aprivate (e.g. for a single organisation use) to public (e.g.access provided beyond an organisation’s use) or a combi-nation of both (also called hybrid clouds). The scale of ser-vice provision also varies from Infrastructure as a Service(IaaS) to Platform as a Service (PaaS) to Software as a Ser-vice (SaaS). The level of flexibility, service developmentand management in using customised solutions variesfrom IaaS (i.e. more flexible, more development work andhigh management) to SaaS. There are many commercialvendors already providing public cloud services: for exam-ple, Microsoft (Azure), Amazon (EC2/S3), Google (AppEngine) and Rackspace etc. Similarly, some well-knownresearch based initiatives are also under development suchas Eucalyptus, OpenNebula, Nimbus, and OpenStack. Thecloud user community also demands standards and prefer-ably open interfaces and encoding to avoid lock-in and tofacilitate easy migration from one provider to another. Inthis regard, the Open Cloud Computing Interface (OCCI)[10] is an OGF working group that aims to develop intero-perability, portability and integration standards to servemany cloud computing models such as IaaS, PaaS andSaaS. OCCI specifications provide a RESTful protocol andAPI for many kinds of management tasks. OCCI is workin progress in many cloud projects such as Eucalyptus,Openstack, SLA@SOI, RESERVOIR, jclouds etc.

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There exist cloud (and in particular SaaS) enabledtools for different environmental applications such asland, transport and urban planning, e.g. ESS [11]. Simi-larly, the UK Natural Environmental Research Councilhas funded a research project called Environmental Vir-tual Observatory pilot (EVOp) [12] to use clouds toachieve similar objectives for the soil and waterdomains. However, further steps are needed to realisecross-thematic integrated intelligence for environmentalmanagement, using standard harmonisation processes inorder to facilitate information requirements at all levelsof governance i.e. local, national, and EU.

An example: user perspective for an urban environmentUrban management aims to respond effectively to the keypolitical concerns of European cities today including thesocio-economic and environmental impacts of climatechange, deteriorating public health and biodiversity lossetc. All of these urban impacts are associated with a dys-functional urban model in which urban transportation sys-tems, in the context of urban sprawl, generate excessgreenhouse gas (GHG) emissions promoting climatechange. Furthermore, motorised transportation as theprime generator of GHG emissions also degrades theenvironment in respect of noise and air quality, impactingadversely on human health. These political concerns aris-ing from the direct socio-economic and environmentalimpacts of the drivers of change are fundamentally relatedat the urban level in the land-use-transport-environmentnexus, in which the degree of compactness of the citydetermines the potential for public transportation solu-tions which in general offer multiple socio-economic andenvironmental benefits.Given the interrelated, interconnected and complex

nature of drivers and impacts in the urban environment,policy integration and integrated urban governance areviewed as the “sine qua non” of effective responses.Urban governance is thereby promoted via integratedapproaches to territorial impact assessment, policy for-mulation and policy implementation.Despite these aspirations for the development of inte-

grated approaches, the reality remains that urban intelli-gence at the local level is generally poorly developed inthe critical areas of territorial impact assessment andurban planning. Agencies addressing the management ofurban regions have a critical need to integrate large setsof data across the sectoral domains of land-use, trans-port, health, etc. in order to respond to the politicaldemands for sustainable cities, but this is rarely achievedeffectively. Furthermore, the complexities surroundingdata integration to secure reliable intelligence increasefurther when the typical framework conditions for inter-agency collaboration on territorial planning issuesrequires intercity or intra-regional comparisons and

assessments. Failure to secure policy integration can beattributed to a variety of factors including notably orga-nisational and procedural barriers associated with thesectoral responsibilities for land-use, transport andenvironment, primarily in a horizontal perspective at thelocal level, and in a vertical perspective between govern-ment agencies responsible for policy development fromlocal, to regional, national and EU levels.These failures further reinforce the demands of the pol-

icy-making community for the development of integratedurban management frameworks, reflected in the demandfor definition and development of new assessment meth-odologies for urban and regional development. Forinstance, the European Environment Agency’s (EEA)Integrated Urban Monitoring for Europe (IUME) initia-tive [13] connects the political drives for climate changemitigation and adaptation, healthy and economicallyviable urban communities, that characterise the Europeanideals of good governance and sustainable development.Further initiatives are focused around the policy model(see Figure 2 below), which identifies specific datarequirements of planning agencies according to a genericagency driven policy cycle for the management of cities.Furthermore, the definition of these integrated manage-ment frameworks must also respond to the evolution ofthe governance model and new demands, external to thegovernment process, arising from the requirement foreffective stakeholder and citizen engagement. Suchdemands necessitate new mechanisms for building capa-city and supporting the ability of a community to createsustainable futures, as advocated, for example, by Yigit-canlar T [14], by introducing citizen science, participa-tory governance and collaborative decision-making.The user-defined generic city management policy cycle

(see Figure 2 below) structures policy related informationrequirements within a framework that proceeds from issueidentification and description, to the definition of responseand engagement with the political authority, to implemen-tation and monitoring of the effectiveness of the actionsproposed. Information required to support planning activ-ities within such a management framework permits theidentification of precisely the nature of the informationthat is required by which agency, for which purpose, andat which stage in the policy cycle. These user-defined inte-gration principles permit the specification of policydefined requirements which in turn form the foundationsfor the development of ICT related solutions, specified inthe framework of the data and process model as describedbelow. Figure 2 demonstrates the basis for an appropriateuser and policy defined model of information needsaccording to a model of the horizontal integration of dataflowing between sectoral responsibilities at the local leveland vertical coordination of data flowing between agenciesof governance from the local to EU levels.

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Mapping the user-oriented perspective to technologicalperspectiveUsing the above user perspective we now attempt topresent a common process to be used for environmentalmanagement with the objective of identifying technicalcapabilities required for the implementation of an IEMSystem (IEMS).A generic process for environmental management using ICTIn the following we attempt to present a general monitor-ing process with the objective of mapping process activ-ities to specific methods, technologies and tools forintegrated information intelligence. The main steps of theprocess are:Step 1 Data Acquisition: several approaches can be

considered for this step including automated discoveryand retrieval from distributed repositories, remote sensing,participatory sensing and citizens’ science, etc. For exam-ple, EEA requires member-states to produce domain spe-cific (i.e. air quality, noise, water) harmonised catalogues, ameta-database including data sources, data sets and otherinformation such as reports and published meta informa-tion via EEA guideline compliant national web sites. EEAalso use a Clearing House Mechanism (CHM) to getaccess to data and exchange of information between var-ious stakeholders. Local governments are also active inlocal environmental monitoring initiatives e.g. SI@M inVitoria-Gasteiz [15] and use different sensors (air quality,noise), LIght Detection And Ranging (LIDAR) and othermethods such as population surveys etc. for datacollection.Step 2 Data Assimilation and Preliminary Analysis:

this step is mainly required to ensure that captured datameets standard or common application schema structures,

and may require the harmonisation and transformation ofthe data according to required target system specifications.This step also determines whether there is need for addi-tional data for required processing services.Step 3 Data Integration and Processing: after perform-

ing discovery and integration of the required datasets, thisstep includes processing for various environmental modelsand visualisation of results for decision-making, etc.Step 4 Data Export: this step is needed for exchange

of information for further processing, storage and use indifferent tools and applications such as different visuali-sation needs by different stakeholders.Required technical capabilities for an IEM systemThe most effective environment for accomplishing inte-grated information intelligence for urban management canbe a service oriented environment based on common corestandardised data formats, services and applications. Suchan environment enables the collection, storage, processing,visualisation and dissemination of information betweenvarious stakeholders. An important element in a Publish-Find-Bind model (Service producer/Registry/Service con-sumer) is to achieve interoperability between services bysupporting various services, data harmonisation and trans-formation, and encoding standards in order to facilitateinformation flow between different services. In general,the above generic process indicates that the following cap-abilities should be considered as technical requirementsfor information intelligence in an integrated environmen-tal monitoring system:1) Data Acquisition: the ability to collect data from var-

ious sources including databases, flat files, web services,sensors networks and web portals (e.g. OGC’s Sensor WebEnablement and Sensor Observation Service and Web

Figure 2 Integrated Environmental Monitoring: Needs and Challenges.

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Feature Service (WFS), Web Map Service - WMS), partici-patory sensing and citizens’ observations (e.g. using smartphones). This would further require:

a) Metadata: the ability to get additional informationabout the data such as ownership, usage restrictions,cost models, etc. and compliance to metadata stan-dards such as Dublin Core or ISO 19115. Further-more the use of common vocabularies or ontologiesto mask semantic heterogeneity can be beneficial.b) Discovery and Metadata Catalogues: the abilityto discover relevant (meta-)data from varioussources that match specific criteria (e.g. OGC WebCoverage Processing Service (WCS) with filterencoding). For instance, the OGC discovery servicestandard (OGC Catalogue Service for Web - CSW)provides standard interfaces that can be used on cat-alogues and includes geospatial extensions. Also, theUniversal Description Discovery and Integration(UDDI) framework or OASIS’s Electronic businessRegistry Information Model (ebRIM) models can beused for registry mechanism (publish/find/bindpattern).c) Data quality: the ability to assess the quality ofretrieved data, e.g. missing data, resolution support,etc.d) Retrieval: the ability to retrieve the required datafrom a specified source using various methods suchas the OGC download service standard, File TransferProtocol (FTP), Hyper Text Transfer Protocol(HTTP), eXtensible Markup Language-Remote Pro-cedure Call (XML-RPC), Simple Object Access Pro-tocol (SOAP) services, etc.e) Storage: the ability to store transition and pro-cessed data.

2) Schema mapping and transformations: the abilityto perform schema mapping to reference datasets, toharmonise spatial schema based on ISO 19100 series(and INSPIRE specifications for Europe), for instance, tocoordinate transformation using different coordinatereference systems.3) Service interoperability: the adoption of standards

such as W3C’s web standards, OASIS’s RM-ODP [16]and OGC’s view (WMS), download (WFS), discovery(CSW) services.4) Data fusion, processing and synthesis: the ability

to integrate data and apply computational processingsteps e.g. using OGC Web Processing Service (WPS)standard, in order to generate desired results and buildsynthesis around gaps in data coverage (e.g. OGC’s sen-sor, feature and decision fusion models can be utilised).5) Workflow management: the ability to design, com-

pose and execute workflows, e.g. in a distributed

environment using the ActiveBPEL Engine, Kepler,Taverna, LONI. Standard compliant services enabledevelopers to formulate service chains (orchestration)for a specific business process models (or workflow)enactment (e.g. BPMSOA Framework [17]). For exam-ple, an OGC White paper [3] suggests that OGC stan-dards compliant services at SaaS level enable servicechaining in a workflow.6) Provenance: the ability to preserve and track infor-

mation about sources of data and processes completedin the past, e.g. CRISTAL [18].7) Visualisation: the ability to generate data and pro-

cess outputs in a user-friendly i.e. understandable froma user perspective by using various Graphical UserInterface (GUI) tools and techniques such as 2D/3Dmodels (e.g. OGC Web 3D Service - W3DS proposal),simulations and maps for various platforms such as weband smartphones using OGC Web Map Servicestandard.8) Decision-making: the ability to enable users to take

decisions based on the recommended ‘best-fit’ outputfrom various scenarios using artificial intelligence (predi-cate, description, fuzzy logic), expert systems tools andtechniques such as OGC standards (OWS Context stan-dard, KML standard and Decision Fusion Model etc.),DROOLS and JESS rule-based engines.9) Social-networking: the ability to enable users to

interact with each other and share experiences (e.g.Web 2.0, forums).10) Information Sharing and Feedback mechanisms:

the ability to share information (e.g. OGC Contextdocuments, Web services, etc.) between users and toenable them to provide feedback/comments on results,annotate data and processing outputs (e.g. Web 2.0,Wikis, Issue Tracking).11) Security and reliability: the ability to implement

authentication, authorisation, encryption, decryption,auditing and backup mechanisms to enable use of dataand services by legitimate users and avoid loss of data.Secure service standards like ISO/IEC 10181-4:1997 -Security Framework for Open Systems can be utilised.12) Scalability and Extensibility: the ability to add

new users, new data sources and new application-speci-fic models without compromising Quality of Service(QoS) requirements such as query response time(INSPIRE requirement) etc.13) Alerts and Events: the ability to facilitate publish-

subscribe patterns for event notifications using e.g. WS-Notification.

Proposed architecture for IEM systemFigure 3 depicts a proposed architecture from a techno-logical perspective. It consists primarily of five horizon-tal and two vertical layers. The output from the first

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two bottom layers is generic which can be tailored tospecific application needs in the three layers.• The platform integration layer depicts a cyber-infra-

structure based on a hybrid cloud environment thatensures cross-platform accessibility of environmentaldata.• The data acquisition and analysis layer is used to

access environmental data from various sources includingremote database repositories, sensor nets, and citizens’observations in the cloud environment. This layer alsoensures the quality of data acquired and identifies theneed for necessary data harmonisation and data cleansing.• The thematic layer classifies the acquired data into

application specific thematic categories and performs dataharmonisation and updates the data/service catalogues forfurther use of the data.• The service composition layer is required to design

workflows, identify data sources, and link necessary pro-cessing components to enact the workflows. Furthermore,necessary analytical analysis of the workflow outputs canbe performed in this layer. This layer also ensures that theprovenance of data and specific processes is maintainedthat can be utilised for analysis by different expert systemsin the application layer.• The application service layer uses the outcomes from

the service composition layer in application domain speci-fic tools such as simulations and visual maps to performan analytical analysis for decision-making. Further, thislayer enables stakeholders to use existing tools anddevelop new application domain specific components andservices (at SaaS level).• The management and integration layer is used to

automate the flow of data and information between thehorizontal layers. It ensures that processed outputs fromone layer to another are syntactically correct. It also aims

to handle change management that occurs at differentlayers and to reduce the extent to which the layeredarchitecture requires management overhead.• The security layer ensures the necessary authentica-

tion, authorization and auditing for the use of data andservices by legitimate users.One of the requirements in achieving the aims of this

research is to understand the incompatibilities betweenvarious standards and processes. For example, interoper-ability between OGC and standard W3C web service stan-dards needs additional consideration for wider coverage ofspatial and non-spatial application aspects. For example,Lee et al. [19] highlight the need for standards and in par-ticular collaborative efforts between OGC and OGF inorder to identify possible areas for initial integration effortsbetween geospatial and grid computing domains and as aresult some projects use standards from both domainssuch as GDI-Grid [20], use of grid for geospatial data [21],European Space Agency’s G-PoD [22], etc.The service oriented architecture (SOA, publish-find-

bind) style has been adopted by general web serviceswhich use WSDL and SOAP messages for interactionbetween web services. Such an approach is platform inde-pendent, suitable for composing service oriented work-flows and easy to implement. However, it may not besuitable for substantial data transfer. Similarly, OGC OWS(OGC Web Services) are based on a service orientedapproach using XML/SOAP and HTTP mechanisms andprovide a readymade solution for cloud computing to per-form geo-processing activities. However, most implemen-tations of OGC OWS are HTTP based which requiresadditional wrappers/transformations for cross-platformservice compatibility and utilisation.In the above context, the OGC WS-Phase 5 interoper-

ability programme [23] developed and demonstrated

Figure 3 Proposed Architecture of IEMS.

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SOAP/WSDL binding patterns for WMS, WCS-T, WFS-T and WPS. Also, a conflation workflow process and aBPEL script was designed and implemented to demon-strate service chaining and workflow, web processingservices, and service interoperability using a variety ofOGC service standards. Furthermore, substantial effortshave been identified in the literature concerning thecompatibility aspects of web/grid and OGC serviceswhich are promising for IEM. For example, Hobona etal. [24] investigated the integration of grid services andgeospatial web services into workflows (using BPEL andSCUFL) for geoscientific processing. They indicated thatboth OGSA and ISO 19119:2005 (Geographic informa-tion - services) follow a SOA based publish-find-bindpattern however mechanisms differ and can restrictinteroperability between the two approaches. Thesesame authors [24] proposed SOAP-enabled interfacesfor OGC XML schemas as wrappers that on the onehand facilitate interoperability between SOAP clientsand geo-services and enable relatively easy workflowcomposition but, on the other hand, introduce perfor-mance overheads. Similarly, in [25], researchers investi-gated a WS-I + based approach for geospatial grid withthe objective of utilising continuous data streaming (e.g.data from sensors). They also demonstrated the use ofWSDL/SOAP based interfaces for OGC web services(with HTTP approach) and introduced mechanisms tohandle performance issues with large datasets. Aydin, et.al. [26] attempted to develop OGC compliant grid ser-vices and introduced WSDL based wrappers to trans-form input/output from OGC (e.g. GML document) togrid services and vice versa. Also, authors of [26]emphasised the need for a decentralised dynamic discov-ery architecture (like UDDI) to define services, inter-faces, operations and protocol bindings for the discoveryof WFSs using WS-Discovery specifications. In [27],researchers investigated the possibility of utilising anUDDI based metadata oriented service informationmodel for geospatial services publishing and discovery.All the above literature indicates that the interoperabil-ity between general web service standards and OGCstandards exists to some extent and can be used in acloud environment to provide services for differenttypes of clients.With respect to cloud computing and OGC standards,

Blower JD [28] demonstrated the deployment of OGCWMS using Google App Engine. Brananski et al. [29] pre-sented geospatial services in a cloud environment usingAmazon EC2. Schaffer et al. [30] investigated the use ofSDI components as value-added services into AmazonWeb Services (AWS - EC2) cloud and mapped a publish-find-bind pattern from SDI to Cloud computing. Theydemonstrated the integration of OGC WPS at the SaaSlayer and performed scalability tests for Wildfire use case

with the objective of investigating interoperability of geos-patial web services and cloud computing. Similarly, Foer-ster, et. al [31] demonstrated the effectiveness of a hybridcloud (Open Nebula - private cloud and EC2 - publiccloud) together with OGC a compliant 52° north basedWeb Processing Service. Baranski et al. [32] demonstratedthat INSPIRE-related challenging QoS requirements arenot easy to achieve without a scalable high performanceIT infrastructure, especially during peak times (many cli-ents accessing services concurrently). They achieved pro-mising results by using the hybrid cloud (Eucalyptus -private cloud and Amazon EC2 - public cloud) architec-ture with OGC compliant 52° north based Web ProcessingService.All the above literature demonstrates that significant

efforts are taking place to utilise cloud computing for thegeospatial domain whereas Foerster, et al. [31] and Bar-anski et al. [32] extended the use of a hybrid cloud i.e. amixture of a private cloud for efficient utilisation of exist-ing IT infrastructure and security reasons and a publiccloud for QoS i.e. high performance, reliability and avail-ability at peak service demands. The above also revealsthat the use of OGC services in a cloud environment doesnot change the way OGC services are utilised withoutcloud computing. Rather use of cloud computing bringQoS i.e. high performance, scalability and low-up front ITinfrastructure costs.

Implementation scenariosThis section discusses different possibilities for imple-menting the IEM using cloud computing. Figures 4, 5 and6 depict how IEM can be implemented using differentcloud architectures. In Figure 4, it mainly represents cus-tomer and service provider perspectives. The GatewayProxy is a client side stub to access services in a cloudenvironment and use according to application needs. TheRegistry component is used to register services providedby a specific cloud platform to enable inter-cloud servicediscovery and provision. The Common Access Gatewayrefers to a controlled entry point to a cloud environment.It also performs several functions to ensure QoS deliveryof information and services to a client. It consists of aService Request/Response Handler ensuring that serviceagnostic interface i.e. necessary transformation betweenOGC WS (e.g. HTTP based) and W3C (WSDL andSOAP) based service requests can be handled. TheResource Pool consists of a pool of IPs and VMs for privateclouds (such as OpenNebula or Eucalyptus based VMs)and public clouds (e.g. AMI for Amazon clouds). TheApplication Handler deals with specific thematic applica-tions and their harmonisation aspects. The WorkflowHandler manages pipelining of different services. The Fed-erated Registry is an instance of a UDDI or ebRIM likestructure that keeps track of active cloud platform

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resources such as specific thematic services. The Control-ler schedules and manages the lifecycle of VirtualMachines (VMs) on a cloud platform and establishes com-munication between VMs with clients through the Gate-way Proxy. The Management and Security ensures theprovenance of application execution with authenticatedand authorised access to services in a cloud platform. Eachworker node is running KVM as a hypervisor and eachVM instance will consist of open source HTTP server/Apache Axis, JVM run-time etc. that enables deploymentof OGC Web Services and standard web services on VMs.Additional open source software can be used based onspecific application demand.Figure 5 depicts a multiple cloud scenario where two

or more organisations can exchange data and servicesusing a private and/or public cloud infrastructure. TheInter-cloud communication component ensures compati-ble communication between two private cloud architec-tures. The Registry component can realise specificservice/data discovery and bindings from multi-cloudplatforms. Such a scenario emphasises the need forinter-cloud compatibility such as the OCCI [10] demon-strated in different cloud platforms [33].Figure 6 depicts a hybrid cloud architecture where

resources can be deployed in both private and publicclouds but have compatible infrastructures with a singleCommon Access Gateway for transparent access to com-putational and storage resources. As an example, in Fig-ure 6, OpenNebula is used as a private cloud andAmazon EC2 and S3 are used as public cloud infrastruc-tures. In the private cloud, the Front End node is an

instance of the Controller component that can also usediscovery service(s) such as OGC CSW. At peakdemand time, additional resources can be acquired frompublic clouds like Amazon EC2, S3. This hybrid cloudinfrastructure facilitates both efficient utilisation ofexisting organisation IT resources in a secure environ-ment and outsources additional computation/storagerequirement to a public cloud infrastructure. Amazonspecific VMs (i.e. AMIs) with specific software platformsmust be available in the Resource Pool in order todeploy dynamically when required. However, such inter-action also requires interoperability between cloud infra-structures. In the above example both Open Nebula andEucalyptus are compatible with Amazon AWS.

Information intelligence and decision making modelYigitcanlar T [14] emphasised the need for a virtualdecision making environment using web-based Geogra-phical Information System (GIS) integrated with othertechnologies such as multimedia, virtual reality or visua-lisation. The same author [14] also argued that such anenvironment can facilitate wider public participation indecision-making processes and development of a sus-tainable and smart urban environment. In this paper wepropose suitable technologies and compliance to variousstandards in order to achieve similar objectives. Themain aim here is to use data from various sources, toapply processing models, to visualise results, to build aknowledge-base and to use intelligence for decision-making and exchange outcomes. Using a knowledgepyramid Figure 7 depicts the main value-added elements

Figure 4 Private Cloud and Public Cloud Architecture Scenario.

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for decision-making models. The key to transform data(structured and unstructured) into information andintelligence is to use WPS with specific knowledge dis-covery algorithms. Such information can be used as aninput to a decision making process in order to consideralternative scenarios and discover the best-fit outcomes.Typical steps in the decision making process include i)information collection; ii) analysis and decision alterna-tives; iii) impact assessment; iv) decision-making andcommunication; and v) process refinement.OGC uses three fusion categories which collectively

contribute towards decision-making processes. These cate-gories are: i) Sensor fusion, which mainly deals with imagechange detection and feature extraction e.g. same locationdifferent time, shape identification; ii) Feature/Objectfusion which aims for conflation of attributes in

geometries, e.g. entity aggregation (tabular data and GISmap) and relational analysis for change detection; and iii)decision fusion that uses data e.g. geo-data such maps anddemographics with the aim to identify meanings and pur-poses in a specific context [34]. OGC’s decision fusionmodel enables a user to acquire processed informationbased on different types of required data (structured e.g.relational, object oriented data models using OGC CityGML, KML etc. and/or unstructured e.g. wikis, blogs,YouTube videos using GeoRSS, GeoJSON etc.) from var-ious sources (databases, sensors, social-networking,WWW, etc.) using specific interoperable IT tools and ser-vices. Furthermore, using OGC standards different fusionprocess models can be adopted such as the Joint Directorsof Laboratories (JDL) Fusion model, the Observe OrientDecide Act (OODA) loop and Multi-INT [34].

Figure 5 Private Multi-Cloud and Public Multi-Cloud Architecture Scenarios.

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OGC’s vision for decision fusion fits nicely with theacquisition of information intelligence for urban manage-ment in a cloud environment. Pravia MA et al. [35] pro-vide a comprehensive multi-INT model for an urbansituation divided into soft and hard sources. A serviceoriented environment with necessary capabilities as men-tioned above (mapping user to technological perspectivesection) are in line with OGC’s service platform for deci-sion fusion [34]. A cloud environment can provide a deci-sion-making platform at the PaaS layer that can compilenon-spatial, spatial and temporal data from varioussources in an urban environment (e.g. hard sources: datacoordinates, signal frequency and location, video, imagery,buildings, land-use, event occurrence, socio-economic,demographic etc. and soft sources: social-networks onweb, etc.) for a specific context. Furthermore, such a deci-sion-making platform can be utilised for domain specificinformation intelligence at the SaaS layer.

Assessment and reflection on the IEMS architectureThe IEMS layered architecture has the following benefits,but also reveals several core challenges which require

further investigation. In addition, we critically reflect onthe extent to which this IEMS architecture is suitable forthe objectives stated in the introduction section.Benefits of the layered IEMS architectureThe layered design of the architecture adopts a ‘separa-tion of concerns’ as a core design principle in order toavoid reinventing the wheel and to capitalise on theexperience and knowledge from existing projects, toolsand services. The layered architecture also brings exten-sibility and flexibility since new components can beadded at various layers. Likewise outputs from differentlayers can be tailored to the specification of varioustools and utilised in different applications. Furthermore,a service oriented model in a cloud environment pro-vides flexibility in using both standard web services forwider adoptability and workflow composition as well asOGC standards to manage substantial amounts of spa-tial data and geoprocessing services.For example, the architectural design of the IEMS can

adopt the ORCHESTRA Architecture Reference Model(OA-RM) [36] based approach in a cloud environmentas it can provide a useful specification for data and ser-vice interoperability in a distributed environment usingboth ISO, OGC and web service standards. Similarly, forspatial data harmonisation there exist several tools andservices which can be reused for the above purpose, e.g.,the HUMBOLDT project [37] provides various INSPIRE

Figure 6 Hybrid Cloud Scenario - with an Example.

Figure 7 Integrated information for decision-making.

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and OGC compliant state-of-the-art harmonisation toolsand services. However, such tools require investigationto be utilised in a cloud environment.The use of cloud computing provides high performance

and scalability, transparent access to on-demand resourcesand low up front investment costs for integrated informa-tion intelligence for urban management that can be usedfor decision-making and other purposes.Technological challengesThe implementation of the IEMS architecture is notstraightforward as it poses the following challenges:1) The layered architecture provides flexibility in mana-

ging functionalities at individual layers. However, mana-ging the complexity of various layers and integratingoutputs from one layer to other in a (semi-)automatedprocess chain is not straightforward and requires rigorousmanagement of tasks at various layers. This complexityincreases further when multi-cloud architectures areimplemented (Figure 5) and hence a management andintegration service must be introduced to handle thiscomplexity.2) To provide a virtual environment for various stake-

holders e.g. citizens, research, scientific and policy makingcommunities, enabling them to select multiple variablesacross various environmental attributes (e.g. urban, trans-port, biodiversity and health) and to extract correspondingharmonised information from a cloud infrastructurewhich could be used for various purposes such as model-ling, simulations, environmental indicators and decisionmaking support for policy development. In this regard,access to environmental data from different distributedrepositories is required. However, these distributed datasources may have different technological infrastructuresincluding proprietary systems, desktop based systems, webservices, grids, clouds, and non-standardised data models,which makes this task more challenging and requires rig-orous analysis for data accessibility, harmonisation andtransformation and compliance to policy requirements.3) To support the uptake of cross-thematic harmonisa-

tion of selected environmental thematic areas such asland-use, transport and health according to existing stan-dards such as the INSPIRE directive. The implementationof a thematic layer (Figure 3) in a cloud infrastructurebased on INSPIRE components including implementationrules, metadata, data specification, services, monitoringand reporting will be challenging due to the recent specifi-cation of INSPIRE annexes.4) To enable various stakeholders to visually design

workflows (e.g. using BPMN and BPEL), to compose andbind services for execution, to identify data sources and toexecute these workflows in a (multi-)cloud environment.In addition, this requires the development of provenancemechanisms to keep track of data and service sources andintelligent processing performed for future references.

5) To develop case studies around citizen science i.e. toenable citizens to participate in environmental monitoringin order to raise their awareness and responsivenesstowards the management of the urban environment usingtools such as smart phone applications (for participatorysensing) and web 2.0 (for collaborative decision-making).This would also require the acquisition of data using stan-dard interfaces such as OGC’s sensor web enablement e.g.sensor observation service (SOS) and OGC’s WFS andvisualisation using OGC’s WMS in a cloud environment.6) As indicated above, data may originate from two dif-

ferent cloud infrastructures. However, each cloud infra-structure is unique and mostly incompatible with eachother i.e. the underlying cloud architecture, data modelsand access mechanisms, and services vary from onecloud infrastructure to another. Such an incompatibilityintroduces a gap in the acquisition of integrated intelli-gence across the platforms not only for applications fromthe same domain but also for cross-disciplinary applica-tions such as environment, health and economic develop-ment. Similar to OGC, one of the aims of the open cloudconsortium (OCC) [38] is to support the development ofstandards for cloud computing and frameworks for inter-operability between clouds. This will enable cross plat-form interoperability, consistent security mechanismsand content sharing. Using such interoperability initia-tives, the inter-cloud cross-thematic urban managementdimension can bring novelty and a unique perspective incloud computing research and, at the same time, willbenefit both scientific, policy making and business com-munities in exploiting the full potential of clouds.7) As indicated in other studies, the hybrid clouds can

be used for OGC standards and in particular WPS.Further investigation is needed to test the behaviour ofOGC and standard web services integration in a hybridcloud environment.Reflection and critical analysisThe real significance of integrated information intelligenceis to be able to answer science and policy questions whichcannot be answered directly from fragmented informationsources. For example, in relation to urban and regionalgovernance, integrated intelligence offers the opportunityto genuinely support evidence based practice and to pro-vide urban planners with the tools and methodologies thatwill enable them to effectively manage the complexity ofthe city. This will enable different stakeholders (environ-mentalists, urban planners, policy makers and citizens),who cannot deal with the complexity of increasing data,harmonisation, service interoperability and processingneeds, to gain benefit from this work.The proposed IEMS architecture and implementation

scenarios attempt to provide the necessary generic capabil-ities in order to fulfil the requirements for the develop-ment of an integrated intelligence system for monitoring

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of the urban environment. Despite the fact that there areseveral tools which can provide specific technical capabil-ity, integrating and using these tools in a cloud environ-ment in compliance to different standards is notstraightforward and leads to several technological chal-lenges as discussed in this section.The benefit of a cloud environment is twofold. Firstly

the use of OGC and standard web services for data acqui-sition, processing and sharing at the PaaS level providesthe opportunity for various stakeholders to develop con-text based domain specific applications at the SaaS level.For example, these services at the PaaS level in a cloudenvironment encapsulate the complexity of data acquisi-tion, cross-thematic harmonised transformations, compu-ter intensive processing, multi-dimensional modelling andvisualisation and collaborative decision support mechan-isms for various stakeholders. Secondly the extensibilityand scalability characteristics of cloud platforms willaccommodate continuously increasing data volumes andcaching for visualisation and user groups who can beinvolved for citizen science-based participatory environ-mental monitoring.One of the critical aspects that the above research

attempts to address is building the capacity and ability ofa community in creating sustainable futures, and to beinvolved in community driven environmental decision-making processes. For example, citizens can have accessto informed environmental intelligence affecting citizens’daily lives and regional economic prosperity which isdirectly or indirectly affected by environmental wellbeing. Similarly, urban planners can obtain improvedpredictions of impacts of urban planning policy by incor-porating multiple variables such as land-use, socio-eco-nomic, transportation and public health. Furthermore,the processed output can be presented in various formsthat could be used to indicate the effectiveness of envir-onmental changes at various levels (e.g., local, nationaland European) with benefits and consequences to stake-holders, policy and decision-makers and the general pub-lic. However, rigorous development efforts are requiredfrom technological peers to develop solutions at the SaaSlevel using the identified technical capabilities and a pro-posed IEM architecture to achieve the above aims.

ConclusionsIn this paper, we have argued that the integration of frag-mented environmental information can result in betterenvironmental monitoring in order to mitigate variousenvironmental challenges such as issues related to cli-mate change. Furthermore, we argued that cloud com-puting can play a major role in achieving environmentalinformation integration by providing on-demand proces-sing and storage capabilities. Our urban managementbased example assists in identifying a generic set of

technical capabilities for information intelligence andproposes a layered architecture for IEMS using differentcloud based implementation scenarios. However, basedon our preliminary assessment we argue that it is not asimple matter to develop integrated intelligence to its fullpotential as a result of certain technological challenges.These challenges require rigorous investigation in futureresearch in realising the true potential of the proposedcloud based architecture for integrated information intel-ligence and decision making in environmental domain.

Author details1Faculty of Environment and Technology, University of the West of England,Bristol, UK 2School of Computing and Mathematics, University of Derby,Derby, UK

Authors’ contributionsZK contributed to the ideas of applying cloud computing for integratedinformation intelligence for urban management. He participated inidentifying user information needs; carried out mapping of user needs totechnical capabilities; proposed cloud implementation scenarios using weband OGC standards; and critically assessed benefits and challenges forintegrated monitoring and decision making models using cloudenvironment. DL contributed to elaborate user’s perspective in an urbanenvironment and linked it with policy context in order to determine therequired technical capabilities in the integrated urban management. RMcontributed in defining the proposed architecture of the IEM based on therequired technical capabilities and participated in the assessment of IEMS.AA participated in mapping the user needs into technical processes, toolsand identified capabilities required for the integrated informationintelligence in cloud environment. He also contributed to the assessment ofthe IEMS. All authors read and approved the final manuscript.

Authors’ informationDr. Zaheer Khan is a Postdoctoral Research Fellow in the Faculty ofEnvironment and Technology of UWE, Bristol, UK. He has over 7 years ofteaching and research experience in system analysis and design, databases,distributed systems, etc. He has been actively working in large-scale EU andnon-EU collaborative computing research projects: FP6 Health-e-Child, FP6HUMBOLDT, FP7 LifeWatch and SAGE. Currently he is working on the FP7UrbanAPI project dealing with ICT applications for urban governance. Hismain research interests are in distributed computing in particular cloudcomputing, software engineering, business process management, datainteroperability and management, software agents and geospatialinformation systems.Mr. David Ludlow is an urban planner with extensive practice experiencegained in UK land-use planning agencies, and at UWE as project managerfor more than 30 EU funded projects focused on integrated urbanmanagement information systems, and sustainable urban development. Heis a member of the EU Expert Group on the Urban Environment (EuropeanSustainable Cities Project), and the Expert Group for the EU ThematicStrategy on the Urban Environment (6th Environment Action Programme).Currently he is UWE project manager for EU FP7 projects urbanAPI andurbanNexus, the ESPON SEGI project, and urban expert for the EuropeanTopic Centre Spatial Information Analysis (European Environment Agency).Professor Richard McClatchey currently leads the Centre for ComplexCooperative Systems at UWE and is active in collaborative computingprojects at CERN, and with many international partners in other EC projects:Health-e-Child, SHARE, neuGRID and N4U. He has been research active forthe past 30 years and has led many projects funded by industry and by theEC in the areas of large-scale distributed data and process management anddatabase modelling and in systems design and integration.Dr. Ashiq Anjum is a senior lecturer in the School of Computing andMathematics at the University of Derby. His areas of expertise includeDistributed, Parallel and Concurrent Systems (mainly Cloud and GridComputing). He has worked on a number of scientific and industrial projects(notable contributions include FP7 funded TRASNFORM project, FP7 funded

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neuGrid project, FP6 funded Health-e-Child project, NSF/DOE funded CAIGEEproject and US Aid funded GAE project) in collaboration with leadingindustrial and academic partners in Europe and USA. He has 12 years ofexperience in academic and industrial research and has around 60publications to his credit.

Competing interestsThe authors declare that they have no competing interests.

Received: 30 November 2011 Accepted: 5 April 2012Published: 5 April 2012

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doi:10.1186/2192-113X-1-1Cite this article as: Khan et al.: An architecture for integratedintelligence in urban management using cloud computing. Journal ofCloud Computing: Advances, Systems and Applications 2012 1:1.

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