Unlocking the power of IoT through

Big Data

IoThings NOW – Milan May 2017Barbara Pareglio – GSMA IoT Technical Director

Introduction of the GSMA

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Source: Internet of Things Study 2016, Evans Data Corporation

IoT challenges for Developers

Expect we will be flooded in data

but how usable will it be?

Some of the challenges

Device protocols/ APIs

Context data protocols (JSON/

CSV/ XML etc)

Knowing the accuracy of devices

Consistency of units

Device reliability

Device stability

Finding the data

The Challenge of Data set Diversity

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The problem … at an EU level

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So I want to study air quality across the EU?

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What about the UK? Air Quality

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What about the UK? Total government datasets

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What about the UK? Air Quality

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Obtaining 14 months of hourly weather observations for two London

weather stations took … 559 minutes elapsed

Even though this amounts to just 1.5 Mb of data

Each hourly data point was obtained by submitting a form, parsing the HTML

response, downloading a CSV file and reading the contents

Approximately 20 minutes per month of data per weather station

There are ‘approximately’ 140 Met Office weather observation sites across

the UK

So downloading 5 years of historical weather observations for the whole of the UK

would take an estimated 168,000 minutes, or 2,800 hours or 116 days!

Also it took a few days to implement a resilient method to download the data

due to the server being unstable

Another experience with historical weather data from the

Met Office

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EACH developer has to

Search for potentially useful data

Assess every data source for usefulness i.e. does it contain the data they

want

Assess every data source / data feed for quality / quirks

Work out how to access the data e.g. do they need to register on a

developer programme, accept a license, obtain access keys

Implement a ‘connector’ to the source e.g. FTP/HTTP, API, CSV/ XML/ PDF/

KML

Sanitise the data e.g. deal with nulls or values that represent ‘no reading’

Ensure the data units are consistent with other data

What is the impact?

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The IoT Big Data ecosystem shift

IoT TODAY

Disparate systems

Silos of heterogeneous data

Valuable data

Innovation

VISION FOR IoT

Sharing of data with greater consistency

Common ecosystem enablers

Data / service monetisation

Increased innovation

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GSMA IoT Big Data Vision

Creation of new business models, products and services by

applying various capabilities, such as mash-up and analytics, to

IoT and context data captured from multiple sources“

“ Enable an ecosystem, including mobile operators and other players,

through which data can be harmonised and shared from multiple

sources

Develop new commercial products and services by applying

capabilities to harmonised IoT and context data

Key areas of focus for GSMA 2017/2018

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Evolve IoT Big Data enablersImplementation and ecosystem

adoption

Proof of Concept demonstrating

operator value generation

Evolve common API to address

requirement for historical data

analysis and linked data

Evolve IoT Big Data architecture to

address areas such as analytics

Expand harmonised entities and

APIs

Evolve IoT Big Data Directory to

foster developer engagement and

showcasing of applications

Implementation of common enablers

(e.g. APIs, data entities)

Drive adoption through developer

programmes and partnerships

Liaison with industry bodies (e.g.

ETSI, FIWARE, schema.org) to drive

ecosystem adoption of common

approach

Create a live proof of concept utilising

the industry agreed IoT Big Data

enablers

Develop a guide describing how value

can be generated by this service and

the value of the common enablers

Define solution design for at least one

Social Development Goal Big Data for

Good initiative

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IoT Big Data Use Cases

Utility

Utility metering

Smart grid

Agriculture

Farms

Greenhouses

Environment

Air quality

Water quality

Weather

Smart Cities

Transport

Parking

Waste management

Automotive

Vehicle

Faults

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Particular Challenges for IoT/Big Data

The potential scale of data being stored, processed or queried given

The number of IoT devices and update frequency

Combining current / historical IoT data with external data

Managing privacy issues including consent, aggregation, anonymisation

The large variety of devices in the field where each vendor/ device type

might have its own specific interface

Differences in device attributes – names, units etc - even where the device

is reasonably generic

Initial proprietary interfaces (I2) will be

augmented with OneM2M

Substantial simplification of

interfacing layers

Gateways handle local data

acquisition & control

Dedicated IoT applications delivered

using oneM2M stack

Hybrid architecture supports richer

applications stack including advanced

analytics, visualisation, machine learning

Applications which span broader set

of devices and context data

Data transformation/ storage to be

used in rich analytics/ machine

learning

AE

CSE

NSE

Mca

Mcn

oneM2M

Functional

Architecture

I2 = Mca

Technical Architecture Framework

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Create an entity including one or more

attributes;

Retrieve entity (/specified attribute`s) by

entity identifier;

Update one or more attributes of an existing

identity;

Remove an entity;

Get an attribute value;

Update an attribute value;

Remove an attribute of an entity;

List entities matching specified criteria;

List known entity types;

Retrieve entity type information;

Create a subscription;

List subscriptions;

Retrieve details of a subscription;

Update details of a subscription;

Delete a subscription;

Batch update;

Batch query.

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Interface Operations

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Agriculture

AgriCrop

AgriGreenHouse

AgriParcel

AgriParcelRecord

AgriParcelOperation

AgriPest

AgriProduct

AgriProductType

AgriSoil

Environment

AirQuality

EnvironmentObserved

PointOfInterest

WaterQuality

WeatherForecast

WeatherObserved

Smart Home

Building

BuildingType

BuildingRecord

BuildingRecordType

Connected Car /Smart

Cities

Vehicle

VehicleType

VehicleFault

Road

RoadSegment

General IoT

Device

DeviceRecord

DeviceRecordType

Machine

MachineType

Subscriber

SubscriptionService

Entity definitions published at : https://github.com/GSMADeveloper/HarmonisedEntityDefinitions

Harmonised Data Entities

GSMA has published three technical documents defining the

approach for delivery of IoT Big Data services to the general

third party application developer ecosystem

1. Technical Architecture Framework

2. API Specification for Data & Control exposure towards third

party applications

3. Initial set of Harmonised Entity Definitions

Documents available from: http://www.gsma.com/iot/iot-big-

data/

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GSMA Technical Documentation

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How to Participate

We welcome ecosystem participants to participate in the project and lay the

foundations for the provision of global data-driven IoT services.

iot@gsma.com

http://www.gsma.com/iot/iot-big-data/

How does the GSMA support the IoT

Mobile IoT

Accelerate availability of

standard LPWA

solutions in licensed

spectrum (EC-GSM-IoT,

LTE M, NB-IoT)

IoT Business Enablers

Enable operators to capture the

Internet of Things opportunity, by

fostering relevant, flexible and

technology-neutral policies and

regulation

IoT Security

Help IoT Ecosystem

providers to protect

themselves from

cybersecurity threats

(Guidelines & Self-

Assestment)

IoT Big Data

Harmonised data sets

from multiple sources

available to developers

and third parties through

common APIs.

Smart Cities

The GSMA is working with all

stakeholders to agree a common

approach to smart city solutions

with long term benefits to citizens

and businesses.

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Thank You

For more information:

http://www.gsma.com/iot/iot-big-data/