Towards a Platform for Urban Data Management, Integration and

Processing

Elarbi Badidi

and Muthucumaru Maheswaran

College of Information Technology, United Arab Emirates University, Al-ain, United Arab Emirates

School of Computer Science, McGill University, Montreal, Canada

Keywords: Smart City, Iot, Urban Data Streams, Semantic Interoperability, Edge Computing, Urban Data Analytics.

Abstract: Smart city infrastructure includes deployment of a variety of sensors and provision of open data platforms

and applications that can help improve the quality of life of the citizens. The large volumes of data

generated by sensors and various Internet of Things (IoT) devices need to be harnessed to help smart city

applications make informed decisions on the fly. Also, efficient management of smart city components

relies on the ability to federate their data, locally process urban data streams, and utilize big data analytics to

harness their governance. Data interoperability and integration is one of the most challenging problems

facing smart cities today. Successful data integration is one of the keys to improved services and

governance. This paper describes the architectural design of a framework that aims to deal with the

integration of data across the various systems of the city, urban data analytics, and creation of value-added

services. The framework relies on recent technologies for data processing including IoT, edge computing,

cloud computing, data analytics, and semantic integration.

1 INTRODUCTION

Over the last few decades, cities are experiencing

tremendous pressure due to migration waves and

urban growth. Their infrastructures need to cope

with growing demand for the supply of energy,

water, transportation, and healthcare services. City

stakeholders are using digital technologies to reduce

costs, improve the quality of services delivered to

citizens, balance budgets, and enhance the efficiency

of various city systems. However, the lack of

integration of data generated by the diverse city

components and systems results in making city

utilities and services operate sub-optimally, limiting

the creation of value-added services, increasing

transport costs, etc. Recent digital technologies offer

new opportunities to mitigate these impacts and

transform cities into smart cities through smart and

innovative planning, management, and operation.

Managing a smart city holistically and

harnessing its governance are becoming essential to

federate its data, locally process data streams

generated by various IoT devices and sensors, and

utilize big data analytics (Khan, 2015) (Ojo, 2015).

An integrated data perspective can benefit smart

cities using big data collection, integration,

processing, and sharing through cloud-based

services. Nevertheless, such data integration and

utilization necessitate suitable software technologies

to collect, store, analyze and visualize enormous

amounts of data from the city ecosystem.

Data interoperability and integration are two of

the most challenging issues facing smart cities today

(Trilles, 2016) (Gyrard, 2016). Indeed, to enable the

efficient governance of smart cities and to create

value-added services that enhance the lives of

citizens, smart city stakeholders have to interpret

many types of information from a variety of sources

including water consumption, road traffic, energy

consumption, healthcare services, and many others.

Unfortunately, they are not currently able to

efficiently harness that information because of the

massive amounts of generated data, data

heterogeneity across the city systems, and the lack of

a common data model and ontology. Successful data

integration is one of the keys to improved services

and governance (An, 2016) (Luciano, 2014). It will

allow for analyses of economic activity, resource

consumption, mobility patterns, and public health,

which will guide the city development.

This work describes the architectural design of a

framework able to deal with the integration of data

Badidi, E. and Maheswaran, M.

Towards a Platform for Urban Data Management, Integration and Processing.

DOI: 10.5220/0006789602990306

In Proceedings of the 3rd International Conference on Internet of Things, Big Data and Security (IoTBDS 2018), pages 299-306

ISBN: 978-989-758-296-7

299

across the various systems of the city, urban data

analytics, and creation of value-added services. The

framework relies on recent technologies for data

processing including IoT, edge computing, cloud

computing, data analytics, and semantic integration.

The framework aims at allowing smart city

stakeholders to connect, manage, process and

analyze data from thousands of IoT devices and

sensors at the edge of their networks. It will mainly

allow to:

• collect data from thousands of IoT devices,

• normalize the integration of IoT devices within

the smart city,

• perform real-time big data analytics on IoT

streams and events, and

• extend smart city applications and processes

with IoT data seamlessly.

The remainder of this paper is organized as

follows. Section 2 provides background information

on urban data streams and describes the challenges

of urban data streams processing. Section 3 provides

an overview of some of the techniques used for data

integration. Section 4 describes the conceptual

architecture of our proposed framework for urban

data integration. Section 5 discusses challenges and

concerns of urban data integration. Finally, Section 6

concludes the paper.

2 URBAN DATA STREAMS

In a smart city context, systems are equipped to

work with real-time data from sensors, electric and

water meters, or other devices used to assure the

functions of the city. Sensors usually convey

information about real-world phenomena, generally

ranging from direct measurements such as

temperature or pressure to user observations like

water leaking. Sensors include not only hardware

sensors but also people. The concept of people as

sensors refers to users providing direct input via

social networks or dedicated end-user interfaces

(Doran, 2013).

Urban data streams come from a variety of IoT

devices and sensors that monitor and report on:

• Weather conditions as they relate to traffic

jams and accidents so that alerts and warning

systems are activated.

• Parking space availability so that drivers avoid

the lengthy searches for open spaces.

• The structural integrity of bridges, historical

monuments, and buildings when it comes to

the impact that weather conditions and

vibrations have on the structure’s safety.

• Trash levels in waste containers to optimize

trash collection routes.

• Night activity and traffic so that adaptive smart

lighting lights streets, sidewalks, and roads in

an energy efficient manner.

Over the last few years, the European Union has

been encouraging its member states to develop smart

cities and allocated 365 million euros for this

initiative. Amsterdam, Barcelona, and many other

cities are leading the smart city development effort.

Copenhagen, which aims to be the world’s best

city for cyclists, has started monitoring the city's

bike traffic in real time by deploying sensors

throughout several parts of the city. These sensors

provide valuable data helping improve bike routes in

the city as at least 50% of the city's residents

commute to their workplaces or educational

institutions by bike every day (Wired.com, 2015).

London started installing smart parking sensors

that would allow drivers using a map to view a real-

time map of parking spaces and to quickly locate

parking spaces and remove the need for lengthy

searches for an open spot. Londoners hope that this

system would alleviate urban traffic congestion and

cut down on carbon emissions. Other cities around

the world are also trying out deploying smart

parking systems in an attempt to improve the

everyday life of their citizens (Computing, 2014).

Furthermore, many cities are using cutting-edge

IoT solutions to implement intelligent adaptive street

lighting systems. These systems can help cities

create safer urban environments and at the same

time save energy and protect the environment. They

light up when human activity is detected and dim

down to reduce costs when streets are empty. For

example, San Diego city has recently started a $30

million Smart City IoT platform project in what

represents the world’s massive Smart City IoT

platform deployment. The platform will add nearly

3,200 intelligent IoT nodes to the current street

lighting infrastructure to collect real-time sensor

data across the city (Diginomica.com, 2017). The

collected data can be used to optimize municipal

systems, increase safety, guide fire and police to

accident or emergency scenes as well as develop

smart apps that, can, for instance, direct drivers to

available parking spaces.

Several efforts investigated the realization of

smart cities through the IoT, often considered as the

principal technological enabler. Jin et al. (Jin, 2014)

introduced IoT for smart cities from three different

perspectives: network-centric IoT, cloud-centric IoT,

and data-centric IoT. The data-centric IoT

perspective includes data collection, data processing,

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data storage, and data visualization. Zanella et al.

(Zanella, 2014) provided a survey of the enabling

technologies, protocols, and architecture for an

urban IoT, i.e., a communication infrastructure that

aims to provide simple, unified, and cost-effective

access to a variety of public services.

One of the challenging issues of current urban

deployments is the non-interoperability of the

diverse and heterogeneous devices and technologies

used in the city (Trilles, 2016) (Gyrard, 2016). These

devices generate different types of data conveyed to

a control center for storage and processing. Zanella

et al. described the Web-service approach for IoT

service architecture and explained its benefits for

implementing interoperable services. International

standardization bodies such as IETF, ETSI, and

W3C, among others, are also promoting this

approach.

3 DATA INTEGRATION

TECHNIQUES

Efficient utilization of data from disparate sources

requires understanding the database schema of each

data source and devising a translation mechanism to

permit data exchange. The literature on data

integration identifies six main techniques: data

consolidation, data federation, data propagation,

utilization of the Extensible Markup Language

(XML) and the JavaScript Object Notation (JSON)

as standard formats for the exchange and storage of

data, development of controlled vocabularies, and

mashups.

Data consolidation refers to the collection of

data from multiple sources and its integration into a

single persistent data store (see fig. 1). It allows to

cope with data duplication and reduce the costs

associated with the reliance on multiple data

management points and databases. It will enable

organizations to do reporting and efficient data

analysis as in data warehousing. The data store can

act as a data source for downstream applications as

in an operational database system. Since data

originates from multiple data sources, there is

always a delay between the time data is generated or

updated in a data source and the time those changes

appear in the data store. Depending on the

underlying communication infrastructure and the

nature and size of updated data, this delay might

range from a few seconds to several days (Loshin,

2009) (Levin, 2004).

Data federation represents an alternative model

for data storage and usage by organizations. Data

federation technology refers to software resources

that provide users with a single logical view to

present and access data stored throughout one or

more data sources. This technique is also known as

data virtualization technology. When the data

sources are traditional databases, data federation

leverages the native data management and search

capabilities of individual source databases and

creates a single, unified, logical view of the

federated databases (Haas, 2002). Business

applications are presented with a combined data

schema even though the source database schemas

are distributed across many federated databases (see

fig. 2). When a business application issues a request

against this logical view, the data federation engine

retrieves data from the appropriate data source,

adapts it to match the virtual view, and sends the

results to the requesting business application

(Loshin, 2009) (Barnaghi, 2015) (Haas, 2002).

Data propagation denotes the movement of data

from one or multiple data sources to target locations.

Data propagation systems usually push data to target

locations. Most often, they are event-driven, and

data propagation is performed according to

propagation rules (see fig. 3). Data updates in a

source system may be propagated to the target

system synchronously or asynchronously (Loshin,

2009). Propagation ensures the delivery of data to

the target system irrespective of the type of

synchronization used. This data delivery guarantee is

a key distinctive feature of data propagation. For

instance, in data warehouses and operational data

stores based systems, updates involve moving large

volumes of data from one system to another. Data

movement is carried out in batches to avoid

impacting the performance of the operations on the

data warehouse.

XML is a markup language that facilitates

sharing of data across heterogeneous computing

systems (Bertino, 2001). Many databases, software

applications, and tools are XML-compliant. XML

facilitates data integration and application

interoperability by adopting standards for

representing certain types of data.

JSON is an open-standard file format that uses

text to transmit data objects consisting of attribute–

value pairs and array data types. It is a language–

independent and light-weight data-interchange

format, which is easy for humans to read and write

and easy for machines to generate and parse. JSON

is more and more becoming the preferred format for

data exchange and integration using RESTful Web

services.

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Figure 1: Consolidation of data from multiple sources.

Figure 2: Federation of Data from Multiple Sources.

Figure 3: Data propagation from data sources to

operational data store.

Controlled vocabularies offer a form of data

integration by enforcing naming conventions for

data elements that ultimately appear in databases.

One example of a controlled vocabulary is an

ontology developed in the context of a smart city

(Nemirovski, 2013). The ontology acts as a mediator

for distinct schemas of individual data sources and

as a reference schema for federated data queries.

Also, researchers at the DISIT Lab at the University

of Florence (http://www.disit.org) have created an

ontology for a smart city, which integrates

regulatory elements, sensors, points of interests,

people, etc. and is used in other smart city projects

(DISIT Lab, 2015).

A Mashup is a technique for building new Web

applications that combine data from multiple sources

to create an integrated experience. Mashup

applications can be constructed using widgets, open

APIs, Web services, and data sources. An example

of mashups developed in the case of smart cities is

FixMyCity (Fraunhofer, 2012), a government

mashup that allows citizens to contact the

appropriate person in a local administration quickly

to report damages in public spaces.

4 ARCHITECTURE OVERVIEW

Figure 4 depicts our proposed architecture to address

the data integration and processing issues in smart

cities.

4.1 Infrastructure Layer

This layer is made up of various smart city data

sources such as smart IoT devices, traditional

databases, Web servers, and edge servers. An IoT

device detects some input from its surrounding

environment and responds to it. The particular input

could be light, motion, speed, vibration, pressure,

water level, heat, or any other environmental

phenomenon. The device reading is then converted

into a human-readable form or sent over a network

to a gateway for further processing. An IoT device,

with typically an IP address, can connect to a

network to exchange data. Smart IoT devices enable

automating operations of a city by collecting data on

various physical assets (equipment, vehicles,

buildings, facilities, etc.) to monitor their behavior

and status, and using collected data to optimize

resources and processes. IoT devices and actuators,

which do not have operating systems, connect to

edge devices or edge gateways using Wi-Fi or

Ethernet connections of a Local Area Network

(LAN) or using Bluetooth, ZigBee, and Ultra-Wide-

band (UWB) of Personal Area Network (PAN).

The realization of smart energy, smart transport,

smart health, smart agriculture, etc. will be permitted

by IoT technologies, which require the deployment

of a vast number of IoT devices and sensors. Web

servers’ logs also represent an essential data source

for the various city systems. Log streaming permits

troubleshooting connectivity problems and

diagnosing the causes of service disruptions. Also,

clickstream analysis can be used to assess the

effectiveness of providing online city services.

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Figure 4: Architecture for Smart City Data Integration.

4.2 Edge Computing Layer

As sensors and IoT smart objects generate massive

amounts of data, traditional data management

systems and practices will no longer be sufficient to

take full advantage of the IoT. The fundamental idea

behind Edge Computing (EC) is to place storage and

computation resources at the network edge, in the

proximity of the data generation location. IDC

predicted that: “By 2019, at least 40% of IoT-created

data will be stored, processed, analyzed, and acted

upon close to, or at the edge of, the network”

(IDC.com, 2017). Thus, the processing of urban data

streams can be pushed from the cloud to the edge.

EC reduces traffic bottleneck towards the core

network by processing the data locally and

expediting data streams by using various techniques

(i.e., caching and compression). Besides, it helps to

shorten end-to-end latency, enabling the offload of

heavy computation load from power constrained

user equipment to the edge. This can be very

beneficial when IoT devices are deployed on remote

locations suffering from poor network coverage or

when stakeholders aim to reduce the costs of

expensive cellular connectivity technologies.

Edge devices, which are often battery-powered,

run complete operating systems such as Linux,

Android, or iOS. They process raw data they receive

from IoT devices and sensors, and they send

commands to actuators. They are connected to the

data layer directly or through edge gateways. Edge

gateways also run complete operating systems and

have unrestricted power supply, more CPU power,

memory, and storage. They can aggregate data and

support analytics at the edge of the network, and

they act as intermediaries between the data layer and

the edge devices.

Both edge gateways and devices forward

selected raw or pre-processed IoT datasets to the

data layer services, like storage services, machine

learning or analytics services, and they

symmetrically receive commands from the above

layers, like configurations or data queries.

Centralized databases are indispensable for

carrying out the various operations of the smart city

systems. Nevertheless, as the data incessantly

spreads from sensors and IoT devices at the edge,

central databases only need to cope with data inflow

at a more controlled rate for instance once per

minute. Using edge servers, which typically have

limited computing and storage capacities, permits

conveying data in real-time and receiving

instructions in a timely fashion. Data streams can be

aggregated and merged at the edge and then

transported to the central databases as averages of

sensed data over well-controlled periods of time (see

figure 5). Thus, moving data management partially

from primary databases towards the edge of the

network is crucial for coping with real-time data

feeds.

4.3 Data Layer

This layer is in charge of storing and providing

access to data, obtained from the infrastructure, and

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processing and analyzing data that other layers can

use to generate valuable insights.

4.3.1 Data Storage and Access

The resources across a smart city infrastructure

together with people’s wearable devices and

smartphones incessantly generate vast amounts of

data in structured and unstructured formats. IoT

devices and sensors monitor in real-time the

operations of many city systems such as

transportation, water, and energy systems.

Furthermore, social media networks such as Twitter,

Google+, and Facebook, often considered as social

sensors, represent a new source of real-time data.

The data layer allows city stakeholders to store

and access these large urban datasets using

conventional and modern management tools. Over

the last few years, Data-as-a-Service (DaaS)

emerged as a new delivery model for data storage

and provisioning wherein data are provided on-

demand to the consumer regardless of their

geographic locations (Olson, 2009). This delivery

model relies on the service-oriented architecture

(SOA) and advocates the view that data management

can be done in a centralized place where datasets are

cleansed, aggregated, and enriched to be accessed by

different applications or users irrespective of their

location or network.

4.3.2 Data Aggregation

Data aggregation typically deals with large volumes

of data to reduce the size of raw sensory

measurements (Jugel, 2014). It allows reducing the

communication overhead and helps to perform more

advanced tasks in large-scale systems such as

clustering or event detection. To efficiently access

and use sensory data, semantic representation of the

aggregations and abstractions are crucial to

providing machine interpretable observations for

higher-level interpretations of the real-world context

(Jugel, 2014).

Data aggregation is common in many

applications. For example, in the healthcare industry,

to meticulously analyze the situation of a patient, it

is necessary to aggregate data from various IoT-

based healthcare service providers that collect data

of that patient using multiple sensors. Fig. 5 depicts

the aggregation of data from one or several data

streams.

Figure 5: Aggregation of data from one or multiple data

streams.

4.3.3 Data Semantic Integration and

Interoperability

The IEEE defines interoperability as: "The ability of

two or more systems or components to exchange

information and to use the information that has been

exchanged" (IEEE, 1990).

If two or several systems can communicate and

exchange data, they are demonstrating syntactic

interoperability. Specified communication protocols

and data formats are essential for successful data

exchange. XML or SQL standards provide syntactic

interoperability. Syntactical interoperability is a

requirement for any efforts of additional

interoperability. Beyond the ability of two or several

systems to exchange information, semantic

interoperability means the ability to interpret the

data exchanged meaningfully and accurately to

produce useful results as defined by the end users of

both systems. Semantic interoperability requires that

both sides agree on a mutual information exchange

reference model.

In smart cities, achieving semantic

interoperability is a more critical and difficult task

given the complexity of the city ecosystem

(Ramparany, 2016) (Psyllidis, 2015) where

government entities and private businesses often use

different terminologies. Therefore, a semantic data

model should be developed to standardize terms and

descriptors whose meanings are defined. Concerning

this issue at the sensor level, the W3C incubator

group created the Semantic Sensor Network (SSN)

ontology (Compton, 2012).

4.3.4 Data Processing and Analytics

In addition to providing efficient storage and access

to data, the data layer allows city stakeholders to

efficiently transform, and analyze these vast urban

data streams so that applications of the smart city

can use it to generate valuable insights. The real val-

ue of such integrated data will be gained by acquir-

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ing new knowledge through the utilization of data

analytics using a variety of data mining, machine

learning, and statistical methods. A growing set of

reputable open source and commercial solutions is

available for data streams processing. This set in-

cludes: Apache Kafka (http://kafka.apache.org),

Apache Storm (http://storm.apache.org), Apache

Samza (http://samza. apache.org), Google Cloud

Dataflow (https://cloud. google.com/ dataflow), and

Amazon Elastic MapReduce (https://aws.

amazon.com/elasticmapreduce). These solutions

allow processing both streaming and historical data,

which is a vital aspect of current smart cities.

For instance, by using Apache Kafka together

with Apache Storm, Apache HBase and Apache

Spark, real-time (or near real-time) data streams can

be processed efficiently. Deployed as a cluster on

multiple servers, Kafka handles its entire publish

and subscribe messaging system with the help of its

four APIs, namely, producer API, consumer API,

streams API and connector API.

4.4 Application Layer

The application layer provides a comprehensive set

of methodologies and tools for efficient design,

development, distribution, and operation of smart

city applications and services. The Service Oriented

Architecture (SOA) embodied by Web services has

emerged as a fundamental technology for providing

services over the Web. Web services are

interoperable across platforms and neutral to

languages, which makes them suitable for access

from heterogeneous environments. Web services

technology has all the potential to be a significant

component in the integration endeavor because it

provides a higher layer of abstraction that hides

implementation details from applications.

In this work, we consider the service-orientation

as the major design principle for the interoperability

foundation for smart city systems facilitating the

ground for the support of security assurance,

semantic layer, IoT integration, business process

management capabilities, and a multimodal portal

with mobile device support. Service orientation will

be the basis for the development of a Smart City

Service Bus (SCSB). The SCSB will be the

backbone of services from the different government

agencies and private businesses. It will enable

creating new value-added services and deliver

updated information at all times to city stakeholders,

citizens, and businesses.

5 URBAN DATA INTEGRATION

CHALLENGES

Data integration and semantic interoperability

involve continuous change management and a

tedious engineering effort. It is a long-term effort

that requires the organization of processes for

consensus-building and cooperation among all

players involved.

The following factors might impact the success

of the data integration endeavor:

• Security and privacy issues (Privacy of

personal data, high cost of security applications

and solutions, threats from hackers and

intruders, etc.)

• Resistance to sharing data or lack of interest in

data integration by some city entities.

• Lack of alignment of organizational goals and

the high cost of IT professionals skillful in data

integration.

• Required effort to coordinate data resources

that have conflicting conceptualizations and

representations, which makes the smart city

data integration endeavor harder.

• The lack of standards for data integration.

Standardization would significantly alleviate

the above challenges. Standards take too much

time before being approved and implemented.

As we mentioned earlier, already many smart

city initiatives are underway based on the integration

of data obtained from multiple stakeholders. It

remains to be seen to what extent such efforts can

deliver promised intelligent services.

6 CONCLUSIONS

Creation of value-added services and single-entry

point of services for city citizens involves the

integration of data from several governments and

private entities. IoT technologies, semantic

interoperability, service orientation, edge computing,

and cloud computing will play a primary role in the

achievement of the smart city goals. A clear

understanding of the requirements of citizens and

smart city governance goals could reveal the

integration tasks to undertake by the various city

stakeholders and the challenges that have to be

faced. A conceptual data integrative framework is

here proposed to cope with the heterogeneity of

systems at different levels including data models,

data semantics, service implementation, and

interfaces. Edge computing, semantic

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305

interoperability, service orientation, and cloud-based

data analytics are the cornerstones of the proposed

framework.

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