20180204 wf iot tutorial - small

From Research to Innovation in IoT: why is
technology transfer so hard ?
February 2018
IEEE WF-IOT
Raffaele Giaffreda
Chief IoT Scientist
Twitter: @giaffred

outline
•a layered perspective on IoT challenges
•focus on some key research / business areas
•turning research into concrete solutions
•are we ready for business?

• Chief IoT Scientist - CREATE-NET, Italy
• 20yrs experience in the telecom domain: BT and Telecom
Italia
• large projects, patent holder, public speaking
• >5mEur funding acquisition
• IEEE IoT newsletter editor-in-chief
• MSc, Telecoms Engineering, University College London, U.
of London
• MSc, Electronic Engineering, Optical Telecommunication
Systems, Politecnico di Torino
4
About me

Information Digital World
Real World of “information”
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Real World Digital World
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what does it take?
THE IOT ENABLER

SENSING
having something to say…
SENSORS COMMS
EMBED’D
SYSTEMS
PROTOC’S
DATA
STRUCT’S
PLATF’S

transistor density / space efficiency
Turing’s Pilot ACE: Automatic
Computing Engine
TINY
CHEAP
LOW POWER
density doubling every 2 yrs

sensing technology enabler
Internet of *** things
noisy things
vehicles
smelly things
radioactive things
underwater things
nano things
floating things
tasty things
“delle cose belle”
…

RESEARCH CHALLENGES…
MEMS (Micro-Electro-Mechanical Systems) – see FBK J
nanotechnology
intrabody sensing for healthcare applications
higher granularity in spectrum of sensed entities

Graphene Sensors
• Single layer of carbon atoms arranged to form a two-dimensional honeycomb
lattice
• Graphene will enable sensors that are smaller and lighter
• Graphene is thought to become especially widespread in biosensors and
diagnostics.
• The large surface area of graphene can enhance the surface loading of desired
biomolecules, and excellent conductivity and small band gap can be beneficial
for conducting electrons between biomolecules and the electrode surface.
• Biosensors can be used, among other things, for the detection of a range of
analytes like glucose, glutamate, cholesterol, hemoglobin and more.
• Graphene-based nanoelectronic devices have also been researched for use in
DNA sensors (for detecting nucleobases and nucleotides), Gas sensors (for
detection of different gases), PH sensors, environmental contamination sensors,
strain and pressure sensors, and more.
http://www.manchester.ac.uk/discover/news/manchester-scientists-develop-graphene-sensors-that-
could-revolutionise-the-internet-of-things/
https://www.graphene-info.com/graphene-sensors

Quantum Technologies
Squeezing the area by million times !
Volume reduced by 1011 times !
Courtesy of Mher Ghulinyan (FBK, CMM)

50 microns
Courtesy of Mher Ghulinyan (FBK, CMM)

no doubt we can sense / produce digital data
from our real world
Real World Digital World
100101101100010011
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EMBEDDED SYSTEMS
(a system
integrator’s perspective)
giving voice to your thoughts…
SENSORS COMMS
EMBED’D
SYSTEMS
PROTOC’S
DATA
STRUCT’S
PLATF’S

Cent
MegaHertz
KiloByte
courtesy of Mattia Antonini

Constrained Nodes (IETF classification)
Data (RAM) Code (ROM)
Class 0
(Too constrained)
<< 10 KB << 100 KB
Class 1
(Quite constrained)
~ 10 KB ~ 100 KB
Class 2
(Not so constrained)
~ 50 KB ~ 250 KB

IoCT OSes Features
• Real-Time OS
• Full IPv6 Stack
• Multi-hops support
• Multitasking
• Power Management
• Application-agnostic
RTOS
Kernel
I/O
Management
Task
Management
Memory
Management
Interrupt &
Event
Handling
Timer
Management
Synchronizati
on &
Communicatio
n

6LoWPAN
RPL
IPv6
UDPCoAP
CBOR
Flexible
Memory
Management
High
resolution
timersMulti-Threading
Multi-platform
8 – 16 – 32 bits

RESEARCH CHALLENGES
• …getting more and more crammed into RTOS…
• energy efficiency
• size

COMMUNICATIONS
making sure one can hear another…
SENSORS COMMS PROTOC’S
DATA
STRUCT’S
PLATF’S
EMBED’D
SYSTEMS

bandwidth / spectral efficiency
com
m
unication density
doubling every 2.5 yrs

The physics…
• Radio signal attenuation proportional to frequency
• Longer wavelength, longer range
• Sub-1GHz band
• robust and reliable communication with low-power budgets
• bandwidth limitation
• Modulation techniques
• (U)NB vs. Spread Spectrum

wireless technologies for M2M
“horses for courses…”
LoRa™ Alliance White Paper © Mobile Experts, 2015
BLE – Bluetooth Low Energy
LPWA – Low Power Wide Area
RPMA – Random Phase Multiple Access
indoor coverage, low cost, long battery life and large number of devices (>10K per AP)
http://glowlabs.co/wireless-protocols/ - table comparing wireless protocols for IoT

trendy comms for IoT: LPWANs
• LoRa (non managed)
• SIGFOX (managed services)
• Vodafone and Huawei (NB-IOT – 3GPP LTE standard)

LoRa basic features
• 868 MHz
• 125 KHz channel
• 250 bps – typical
• non-managed
• star topology, thousands of nodes / gateway
• LoRaWAN L2 protocol for networking (security, duplication etc.)

SIGFOX
• UNB – 100 Hz
• 12 bytes per message
• 140 messages per day max (ISM bands regulation, 1% duty cycle)
• 100 bps
• 2-way communication
• very high power efficiency
• 1 Eur / year

NB-IoT
• the telco operators’ bet
• 3GPP LTE
• announcement
• piggybacking existing infrastructure
• low-cost to deploy, wide coverage, but
• subscription based, quality league
• Prototypes exist but no commercial hardware / deployments yet

LPWAN world vendors
• Semtech Corporation (California),
• LORIOT (Switzerland),
• NWave Technologies (London),
• SIGFOX (France),
• WAVIoT (Texas),
• Actility (France),
• Ingenu (San Diego),
• Link Labs (Maryland),
• Weightless SIG, and
• Senet, Inc. (Portsmouth),
• Other stakeholders of the Low Power Wide Area Network market include telecom operators such
as Vodafone (U.K.) and Orange (France), among others who integrate these smart devices and sell
them to end users to cater to their unique business requirements.

LPWAN
• Ovum Research 2017: Five Internet of Things Trends to Watch
“IoT connectivity: LPWA technologies become mainstream”

A reasonably well designed technology
different spreading factors
(12) for different data rates

LoRa WIDE
COVERAGE
not every human
being runs as fast
as Usain Bolt!

LoRa/LoRaWAN: Test and prototyping
LoRaWAN coverage tests (Trento) Prototyping gateway LoRaWAN and
monitoring stations with open hw & sw
LoRaWAN Gateway PoE
Waterproof Case
Indoor LoRaWAN
Gateway

5G anyone?
• while LPWANs and the IoT world is going ahead at its own pace
• wireless networking research focusing on
• issue of latency
• tactile internet scenarios
• bandwidth…
• but…not only radio technologies

• 5G is not just about speed and more flexible networks!
• 5G is about having a better mobile network that can lead to
improved/futuristic application smart scenarios
• 5G will in fact leverage on:
• Virtualised/programmable high speed dynamic access & transport
networks
• Decreased latency thanks to Mobile Edge/Fog computing (Tactile
Internet, Enhanced Virtual Reality, Telerobotics,…)
• Secure and interoperable IoT infrastructures for a huge variety of Smart
Scenarios (Industry 4.0, Smart Cities, Connected Cars,…)
things to remember about 5G…
42

RESEARCH CHALLENGES?
• cheaper
• energy efficient
• longer range
• higher bandwidth
• low latency
• …
• some little extras (positioning)

PROTOCOLS
don’t speak all at the same time…
SENSORS PROTOC’S
DATA
STRUCT’S
PLATF’S
EMBED’D
SYSTEMS
COMMS

6LowPAN, CoAP, MQTT etc. protocol
adaptations to optimise the use of wireless,
low power, limited proc power…
THIS IS ABOUT GETTING THE MOST OUT OF THE COMM MEDIUM
TCP to optimise use of “Best Effort Internet”…
…an example from Z-Wave,
home automation protocol…

Research Challenges
efficient use of the medium
M. Vecchio et al: WSNs compression schemes
5G (?) for Tactile Internet reducing latency below ms

DATA STRUCTURES
understanding the contents…
SENSORS
DATA
STRUCT’S
PLATF’S
EMBED’D
SYSTEMS
COMMS PROTOC’S

…preparing gathered data to be
exploited by the application…

From standards to bespoke data structures
• develop applications once, deploy many times
• no additional coding for adding new sensors…provided they all sing
from the same standard sheet
• about semantic interoperability
UNREAD EMAILS
effort needed for archiving them largely
outweighs simple searches we so much
got used to these days

the UNCAP examplechannel
channel
channel
channel
channel
stream
User_ID
Device_ID
Timestamp
Type
Payload
POSIT’N
User_ID
Device_ID
Timestamp
Type
Payload
MEASURM’S
User_ID
Device_ID
Timestamp
Type
Payload
ALARMS
Payload
"properties": {
"blood_glucose": {
"allOf": [
{
"$ref": "#/definitions/unit_value”
},
{
"properties": {
"unit": {
"enum": [
"mg/dL",
"mmol/L”
]
}
}
}
]
}
i.e. location stream
x,y,z channels

Research challenges
RESEARCH CHALLENGES…

PLATFORMS
easy learning books…
SENSORS PLATF’S
EMBED’D
SYSTEMS
COMMS PROTOC’S
DATA
STRUCT’S

The IoT is dead. Long live the IoT

what is a platform?
• a comprehensive (software) offer of services that puts together a mix
of what presented so far
• main purpose for IoT platforms is to provide more or less automated
features that help easily create applications that exploit data for a
purpose
• enable you to innovate without worrying about the details
• fast implementation, testing, validation, delivery cycles
• yet, n-dimensional choice

In the case of IoT a platform will consist of…
source: IoT Analytics

doi:10.1016/j.comcom.2016.03.015
…this is also where it starts to get more crowded!
Open source
PaaS vs. SaaS
Security
Discovery
Remote management
Interoperability
Supported standards

what makes a platform a good one?

whose chestnuts do we pull out of the fire?

Facebook Platform open API made it possible for third-party developers to
create applications.
src: http://www.digitaltrends.com/features/the-history-of-social-networking/
AppleStore Android GooglePlay
Software advances
(Hardware enablers)
touch screens
tablets / smartphones
mobile computing
Rather than offering a comprehensive social networking experience like the now-defunct
Myspace and the struggling Google+, they instead specialize in a specific kind of
interaction service that involves the sharing of public images (Instagram), the private
sharing of images sharing (Snapchat), augmented reality (Foursquare), and location-
based matchmaking (Tinder). People essentially use the various services in conjunction
with other platforms to build a comprehensive, digital identity.
what is the target?
ease of use for its intended audience!!!
ability to tinker and personalise it!!!!
contextual background awareness…

three FBK CREATE-NET examples
generic, target
SMEs willing to
digitilise their
services, products,
processes
target SMEs and
innovators in the
African context
modular gateway
platform, target
developers mostly

Integration API
Raptorbox
Problem addressed
• Challenges for integration of IoT devices
into existing product/service portfolio:
• Complexity of integration of heterogeneous
IoT devices into an existing infrastructure:
• Interaction with IoT devices (device identification,
protocol handling)
• Security: secure communication, device and data
access control
• Scalability:
• From few devices in trial phase to massive
deployment of IoT connected devices
• How to perform rapid prototyping to address
fast business and tech validation cycles and
fast delivery
Service Bus
Enterprise Systems
Device integration and
management made eas
in a secure, scalable,
configurable way
courtesy of Fabio Antonelli

Our solution
u Device Virtualization:
u Common Device Modeling (“Web
of Things” paradigm)
u IoT Message Brokering:
u Scalability by design
u Multiprotocol support (http/https,
MQTT, JMS, AMQP)
u Data chaching for real-time event
processing and querying
u Configure your Business Logic for Rapid IoT Application Prototyping (Data
and events workflow Editor)
u Flexible Access Control & Authorization (ACLs) for devices and users
u Secure Communication and Interaction with devices
u Easy Integration via APIs exposing all available capabilties

Integration API
Raptorbox Service Bus
Enterprise Systems
the Raptorbox IoT Data Broker
COMMUNICATIONS
SENSING
GOODDATA
VALUE GENERATION
ROUTING
FILTERING
the more I understand the data,
the better value I can provide…
AGGREGATING
INTERPRETING
VALUE
PROCESSINGlow high
low
high
BADDATA
JSON structured vs. stringified data

store significant data…
Payload
"properties": {
"blood_glucose": {
"allOf": [
{
"$ref": "#/definitions/unit_value”
},
{
"properties": {
"unit": {
"enum": [
"mg/dL",
"mmol/L”
]
}
}
}
]
}
“literate”
(relevant plugins / libraries)
Raptorbox IoT
Data Broker
higher processing but…
save storage space
facilitate interpretation
save network use
“all blood glucose levels above a threshold”

Raptorbox target
• system integrators mainly
• focus on core service provisioning competences
while exploiting interoperable platform for enriching
those with interoperable IoT data harvesting
• examples: SMEs digitalisation support, smart cities,
e-health
Integration API
Raptorbox
Service Bus
Enterprise Systems
why is technology transfer so hard?

WAZIUP Platform
The EU-AFRICA WAZIUP platform (Actor view)
App. Development
App. Deploy
Sensor registration
App. Execution
Developer
Sensor owner
App user
Third party API
integration
Data provider
courtesy of Corentin Dupont
App source
code
data
processing &
analytics
IoT PF IoT sensors

Architecture

Behind the scenes

A generic platform for many applications

System overview
CLOUD
LORA
GATEWAY
SENSOR
courtesy of Abdur Rahim

ElevUpIncubateur Connecté
Benin
Cattle rustling
Senegal
Fish farming
Ghana
Urban waste
Togo
Urbanatic
Togo
African IoT entrepreneurs
courtesy of Abdur Rahim why is technology transfer so hard?

WAZIUP target
• African community of developers
• focus on core competences while exploiting ready-to-use open-source
tools and components to cater for the needs of African businesses
• examples: fish farming, precision agriculture, cattle rustling etc.
App source
code
data
processing &
analytics
IoT PF IoT sensors

From Research to Innovation in IoT: why is
technology transfer so hard ?
February 2018
IEEE WF-IOT
Raffaele Giaffreda
Chief IoT Scientist
Twitter: @giaffred
PART 2

AGILE – Open Source Modular Gateway for IoT
The Challenges
Decentralized IoT -
GW Empowerment
Control Devices
Store and manage Data locally
Create and run Apps
Extensibility and
Adaptability
Adapt to different Verticals
Modular extensible design
Interoperability
Protocols (for devices)
Devices
Cloud services
GW HW platforms
Developer communities
Ease of Use
Cloud-like DevOps
Integrated management features
Embedded devel. environment
Facilitate code reuse
Courtesy of Csaba Kiraly – AGILE Technical Coordinator

AGILE overview
Dbus + REST APIs + SDK
Low-level components
connectivity, things, data, security, …
Docker containerization
Java, Node.js, Python, C++ components
Docker compose based startup
Yocto based OS
lean OS, broad HW support
App execution
Embedded Dev UI, Cloud integration, Apps
Open Modular HW
simplify IoT GW design
Pilot development
5 Pilots, 1 Testbed, 4 Artists, 2 Open Calls

AGILE HW Platforms
Makers
Gateway
Industrial Gateway
(Reference Design)
Monitoring Station
(Consolidated Design)
Design for Modularity
ATHENS
Event
Intrinsic modularity
Modularity by expansion
Faster delivery cyclesCourtesy of Paolo Azzoni – Eurotech

AGILE Makers’ gateway
Courtesy of David Remon – Libelium

Industrial gateway (see D1.1-D.12 for details)
Carrier module
Courtesy of Paolo Azzoni – Eurotech

Rapid Prototyping overview
Graphical App
Development
Maker’s Gateway
Hardware
Industrial
Gateway
Local Management
Remote / Fleet
Management
Device Discovery
Embedded
Storage
Visualization
Software Stack
Push to Cloud

a more comprehensive picture
• IoT and Cloud (infrastructure)
• Edge computing and Cognitive IoT
(data)
• Blockchains for Secure IoT
• Promosing IoT (Industrial + eHealth)
SENSORS
PLATF’S
EMBED’D
SYSTEMS
COMMS
PROTOC’S
DATA
STRUCT’S
IoT &
Cloud
Promising
IoT
Decentr.
AI & IoT
Existing and emerging trends in IoT
Blockchains
& IoT
T-Shaped Model

IOT PLATFORM AS A SERVICE
AKA
IOT SERVICES SUPPORTED BY THE CLOUD
IoT &
Cloud
Promising
IoT
Decentr.
AI & IoT
Blockchains
& IoT

IoT, Edge Computing, Fog Computing challenges
K. Skala, D. Davidovic, E. Afgan, I. Sovic, Z. Sojat: Scalable Distributed Computing Hierarchy: Cloud, Fog and Dew
Computing

Improve IoT through Cloud
• constrained devices
• limited processing power
• limited battery power
• limited networking
• limited storage
• limited support for scalable applications
• advances in cloud computing (edge / fog computing, containers, micro-
services)
constrained to unconstrained offload, separating concerns…
Cloud
IoT

IoT and Cloud: derived trends
Cloud
IoT
IoT in islands,
localised
applications,
dawn of IoT
experimentation
Backend storage,
security, processing,
wider scope IoT services:
common baseline
supporting many apps
Latency + privacy
problem addressed
Edge Computing for IoT
( )
Android “compliance” and integration

Why is edge / fog computing becoming more
and more attractive
• problems with latency
• problems with systems reactivity
• need for data privacy and ownership
• technology progress – powerful cloud backend is a given
• GPUs are enhancing the capabilities of affordable edge devices
• problems with shipping increasing amounts of data from increasing
amounts of devices
• Internet of Media Things and Wearables
personal bet? a future where you own
your data and decide who gets to use it
why tech transfer is so hard?

Edge for IoT: horizontal and vertical migration
Dynamic instantiation of IoT functions
(microservices) on edge cloud infrastructure
GIoTS 2017: C. Dupont et al. “Edge computing in IoT
context: horizontal and vertical Linux container migration”

More on IoT trends: distribution,
decentralisation, resource sharing
• IoT has increased the monitoring fabric
• More and more IoT platforms claim to be providing the
glue for addressing interoperability
• With increasing numbers and pervasiveness, come the
issues of control and capillary ownership
• services become volatile
• edge services for IoT bear a locality constraint
• leading to three dimensional problem
• 1. control of owned resources between Cloud, Edge, IoT
• 2. variability over time
• 3. blanket coverage impossible without additional cooperation
Cloud
Edge
IoT
Time
Administrative
domain

IoT &
Cloud
Promising
IoT
Decentr.
AI & IoT
Blockchains
& IoT

IoT and Security
https://www.pentestpartners.com/blog/new-wi-fi-kettle-same-old-security-issues-meh/

MIRAI DDOS ATTACK – October 2016

many levels of security
• data encryption at transmission level
• data encryption at storage level
• policy-based access control
• anonymise data
• etc.
• IoT and blockchains…(enable secure and logged exchange of IoT
messages)

What is a Blockchain
• Network of nodes offering a distributed database (ledger), that
tracks transactions in “chains” of immutable blocks replicated among
all participating nodes
• Consensus mechanism: guarantees non-repudiable transactions
• Rewarding mechanism: to incentivize mining activities and resources
exchange (use of cryptocurrencies)
Courtesy of Fabio Antonelli

How a blockchain works: an example

Blockchain Types
History:
• Bitcoin (Satoshi Nakamoto)
• 2nd Generation: “programmable” blockchain (Smart Contracts
creation)
Types:
• Public/consortium/private blockchains
Different implementations:
• Bitcoin, Ethereum, Hyperledger project (Linux Foundation)

Blockchain main characteristics
• Decentralized: There is no single central database. Every transaction
is recorded on every ‘block’ of a chain. Any block can be used to verify
digital records.
• Immutable: The decentralized nature of the database makes
blockchain immutable. Publicly verifiable blocks with a permanent
record of all transactions lend themselves well to automating auditing
services.
• Programmable: Blockchain can be programmed to execute
transactions automatically, if certain pre-decided conditions have
been met (Smart Contracts)
Courtesy of Fabio Antonelli

Added Value for IoT
• Trust and Reputation of IoT devices:
• Non-Repudiable Device Identity
• Security enforcement at the edge
• Secure Traceability of Transactions and of Information:
• in financial transactions, supply chains, and other processes involving involving IoT devices
• transparency, auditability without the need to leverage on 3rd party trusted entities
• Make consumer data more private
• More Resiliency:
• No single point of failure
• IoT devices can autonomously interact with humans and other IoT devices:
• including capabilities to perform automatic payments/value exchange tracking (digital
currencies)

Use of blockchains in IoT related applications
• more automated control of IoT devices “actions”
• mart contracts for exchange of edge resources
• new opportunities for localised IoT resources
owners
• more flexibility Cloud
Edge
IoT
Time
Administrative
domain
i.e. Ethereum lets you:
Design and issue your own cryptocurrency
Create a tradeable digital token that can be used as a currency,
a representation of an asset, a virtual share, a proof of
membership or anything at all.

Locality_X
@Loc_X IoT Resources Pool
Blockchains in IoT Edge Computing scenarios
request
commit
probe
reward /
deny transact
BC Client
Smart
Contract
Record of (non-)
fulfilment
Blockchain for federated IoT resource pool generation
X
request

MAKING SENSE OF HARVESTED IOT DATA
IoT &
Cloud
Promising
IoT
Decentr.
AI & IoT
Blockchains
& IoT

The AI Revolution: The Road to Superintelligence
http://waitbutwhy.com/2015/01/artificial-intelligence-revolution-1.html

Machine learning and IoT
• same type of problems ever since Marc Weiser seminal
paper on Ubiquitous Computing was published (1993)
• most successful technologies are those that disappear weaved
into the fabric of our surrounding physical world
• physical world needs to be represented digitally
• modelling reality is still a complex problem
• compared to 1993, we can certainly produce lots of more
data with IoT capabilities and monitoring pervasiveness

unlocking a huge potential
data
data
data data
data
data
data
data
data
data
data
data
data
data
H/W
motion
presence
location
status
patterns exist ...
cause-effect in
gathered data
change over time
SENSING
constrained
resources
data goldmine
and lots of
siloed
applications
The Craft IoT Cognitive IoT
motion
presence
location
status
observe
cause-effect
relationships
train & gradually replace
human in the loop
derive patterns of ...
interpret
data
adapt over time

how cognitive technologies and
IoT can be leveraged upon to
optimise network resource
usage in a smart-city security
monitoring application
Alcatel Lucent Bell Labs / Thales
courtesy of Marc Roelands
2011-14 EU Project

Extracting knowledge from data – domain
expert modeling…
• many bespoke machine-learning applications exist
• however, still substantial overhead needed
• loads of training data required…
• smart-agriculture example
• domain expert models need to assist machine learning experts to help them design
algorithms that, based on collected data, can actuate according to model
expectations
• sometimes models need to be created through observation (lengthy process)
• in both cases, a lot of validation data is needed to train and tweak algorithms
• no wide applicability, no general purpose machine learning…
• experience from iCore EU collaborative project

Problems we face
• monitoring capability of edge devices has considerably increased
• videos collected everywhere (also in “crowd-sourcing”)
• TBytes of sensor data produced by a flight
• cannot upload it all
• need local processing, yet with limited capability devices (compared to powerful cloud
computing racks)
• interpret data with the objective of considering reducing its size without loss of
information
• compare a 5min video with picture of a cat at frame 259 of test-video.mp4
• camera is one type of sensor producing a stream of video frames
• generalise to any sensor producing a stream of IoT data
• noise, temperature, humidity…you name it
• anomaly detection @ sensing sample #2234

An infrastructure perspective
DUMB
EDGE
SMART
EDGE
FULL DATA
RELEVANT
METADATA
FROM A…
WE ARE MOVING TO A …

courtesy of Janjua Zaffar
Feature extraction

Role of “augmented IoT” in Digital Twin representation
Real
World
Situation
Cam Mic
Child
a car, a hot
pan etc.
Digital
Twin
Situation
DANGER
ASSET
“DISTANCE”
ALARM
THRESHOLD

AI at the edge – why? and why now?
https://www.tractica.com/artificial-intelligence/artificial-intelligence-processing-moving-from-cloud-to-edge/
Federated Learning by Google
Core ML at Apple
Streaming (Facebook) vs.
model update (Google)

GPU
cluster
BE Cloud
AI Model 2
From monolithic to modular AI
GPU
cluster
BE Cloud
AI Model 1
GPU
clusters
BE Cloud
AI Model
ability to recognise a
person, a car, a bus
Cloud
BackEnd
(BE)
IoT sensing / actuating fabric
SENSE
ACTUATE
SENSE
ACTUATE
ability to recognise
an unsupervised
child, a hot stove, an
electric plug
Cloud BE
Edge Cloud
AI Model 1 “break-up”
into separate modules
AI Model 1
?
?

Preparing the Infrastructure – decentralised AI
Infrastructure flexibility research
Deep learning, machine learning
and AI mapped onto such an
infrastructure
Innovation in application domain Verticals
Innovation infrastructure
management enablers for flexible
resources allocation machine learning and AI distributed models
DUMB
EDGE
SMART
EDGE
FULL DATA
RELEVANT
METADATA

DEEP LEARNING
Illustration by Justin Metz
W
ATCH
THIS SPACE!!!!
applicable in all
scenarios exposing
highly structured data
the emergence of unsupervised learning…
(+ advances in edge cloud computing)

Setting the scenes on IoT
applications that are promising
“It's no secret that the industrial IoT is where folks are hoping to make the big bucks.”
Stacey Higginbotham
IoT &
Cloud
Promising
IoT
Decentr.
AI & IoT
Blockchains
& IoT

INDUSTRIAL IOT
“It's no secret that the industrial IoT is where folks are hoping to make the big bucks.”
Stacey Higginbotham

Image Credit: The Industrial Internet Consortium – April 2015 Infographic

TREND:
we can sense and transmit more
and more efficiently

why do we want to do that in
an industrial context?

Software and hardware…
• software industry
• appliance / electronics
• “SAP and Bosch team up on Internet of Things”
• … The technology, for example, allows a production system to select
the torque for each screwdriver's task, increasing efficiency ...
• wow...what does it take to tighten a screw?
• how much torque to apply when? what about replacing the
screw driver? what about ensuring it is the right one for the
type of screws?
• sensing system and an actuator...plus contextual knowledge
about type of screw, screw pitch and size, material (pre-
sales)...data collection, interpretation (after-sales)
enhance a particular task
components, tools
integration, know-how

for what purpose???
Raccoltadati
Descriptive
what happened?
Diagnostic
why did it happen?
Predictive
what will happen?
Preventive
what should I do?
Decision
Actuation
Decision support
Decision automation
human input requiredanalytics

TREND: servitization
(sell & forget vs. sell and assist)

Advantages of 4th industrial revolution
• digitalisation of production process
• digitalisation of product
• monitoring during and after production
• manufacturers and software house join forces
• “just in time” production – with management of stock, stores,
production value chain
• products personalisation
• reduced production and final product costs – competition
• new business models tied to servitisation

all well, but…
• need reliable technology
• sensing and communications
• security, dependability, servitization
• (pre-sales / after-sales)
• need performance
• data-processing and edge cloud
• need competences (choice, integration, deployment)
• infrastructure
• choice of technologies
• interface between standards
• middleware
• flexible architectures
• services and applications
• what knowledge do we want to extract from data?
• need technologists + domain experts, working side by side
how
to make it happen?

need reliable technology
• (sensing) – what to sense, size, durability, etc.
• securely getting data out of sensors to the applications
• what options for your production plant, assembly line, deployment
environment…
• 5G is a key enabler
• reliable communications / protocols
• energy efficiency
• short round-trip delays
• NB-IoT vs. Sigfox vs. LoRa
components, tools

need performance
appsense sense appprocess
vs.
edge cloud / fog computing
components, tools

need competences
• know-how!
• infrastructure
• choice of technologies
• middleware
• flexible architectures
• services and applications
• domain experts + technologists
• solution design
integration, know-how

We live in an ageing society…
The Economist: by 2050 the number of people aged over 80 will have doubled in
OECD countries, and their share of the population will rise from 3.9% to 9.1%
KPMG: number of care-home residents could grow by 68% over the next 15 years
Problem: government subsidies reduced, ¼ of total care homes in the
UK may close within 3 years (2016 article from The Economist)
Solution: residential, home care increasingly attractive market
FACT: home care on the rise

Wide spectrum of monitoring possibilities
• Health parameters
• Mobility (Indoor location)
• Appliances usage
• Environmental conditions
• Progress towards goals
Trend: consumer-grade devices becoming cheaper and more
and more accurate and miniaturised, less invasive
“What we call the “healthcare” industry is really a disease industry, dependent on an
endless supply of distressed customers” M. Geddes
More and more opportunities in the “wellness” and quantified self sector
FACT: wide set of requirements

Tutorial Map
SENSORS
PLATF’S
EMBED’D
SYSTEMS
COMMS
PROTOC’S
DATA
STRUCT’S
Emerging trends in IoT
ResearchChallengesinIoT
The business of IoT,
business models,
economic issues?
IoT &
Cloud
Promising
IoT
Decentr.
AI & IoT
Blockchains
& IoT

but…
• many devices, as many apps and cloud backends…

furthermore…IoT standards…

health and wellbeing monitoring
• quantified self in a smart home
• plethora of devices
• all use “device (gateway) cloud app” chains
device-gateway
protocols
gateway-cloud
IP
cloud-app
IP
RESTful APIs MQTT pub/sub
biggest “source of troubles”
Operating Systems

one (not the only one) reason…
https://qz.com/771727/chinas-factories-in-shenzhen-can-copy-products-at-
breakneck-speed-and-its-time-for-the-rest-of-the-world-to-get-over-it/
FACT: IoT fragmentation

a bit of detail…
• hardware products will be copied
• hardware manufacturers need to minimise “copycats” risk factor
• high sell vs cost markup (make profit while you can)
• bundle software services (i.e. smart ways of processing / visualising
collected data)
• software lock-in realised with additional cloud services (i.e. a “cool
App” that everyone wants to use)

and so what?
• many apps to install
• devices more expensive than they need to be
• apps not interoperable
• but the worst is we give away the right to control who uses our
personal data and for what reason…
FACT: dreadful user experience
FACT: we lost control of our data

IoT devices and gateways – the vendor
strategy
• Cannot create a business based only on hardware
• Software lock-in realised with additional cloud services (i.e. a “cool
App” that everyone wants to use)
• Reinforce the message: “all your personal data are in the hands of the
companies whose hardware you use to collect it!”
• Moral need to intervene and do something about it…

a quick recap…
• contextual IoT technology background
• highlighted two main problems
1. interoperability hurdle
2. control over my own data
what can we do about it?

Walking the “research – innovation –
business” path
• EU FP7 COMPOSE 2011-14
• EU H2020 IA UNCAP 2015-17
• EIT Digital ESSENCE 2017
• EU H2020 AGILE 2016-18
research on IoT
interoperability
services
Innovation Action
with integration of an
IoT Broker into an
eHealth project
business solution
leveraging on
developed assets
ASSETS
Interoperable Gateway

Infrastructure assets
Rapid IoT Application Prototyping (Data and
events workflow Editor)
Easy Integration via APIs exposing all
available capabilities
I can chose for a subset of my data never to
leave my home gatewayInteroperable Gateway
Interoperate your own IoT devices
Data Mgmt APIs
Modular IoT gateway
Scalability by design
Multiprotocol support (http/https,
MQTT, JMS, AMQP)
Data caching for real-time event
processing and querying
Rapid IoT Application Prototyping (Data
and events workflow Editor)
Easy Integration via APIs exposing all
available capabilities
Flexible Access Control & Authorization
(ACLs) for devices and users
my data in the cloud BUT…I am in control

Secure,
Permanent
Storage
IoT Data
Broker
(cloud)
data sources
data sources
data sources
data sources
data sources
data sources
IoT Data
Broker
(gateway)
IoT data (direct)
IoT data
(via gateway)
APPLICATIONS
CEP, data
processing
access
control
PROCESSING
SENSING
MQTT, STOMP, CoAP,
REST, WebSockets
eHealth solution – building blocks
1
2
3
2a

Secure,
Permanent
Storage
IoT Data
Broker
(cloud)
data sources
data sources
data sources
data sources
data sources
data sources
IoT Data
Broker
(gateway)
IoT data (direct)
IoT data
(via gateway)
APPLICATIONS
CEP, data
processing
access
control
PROCESSING
SENSING
MQTT, STOMP, CoAP,
REST, WebSockets
eHealth solution – our assets
1
2
3
2a

Specialising the architecture
Dignity
Autonomy
Independence
MONITOR
GAIN Better level
of life
Biosensors
Indoor/outdoor localization
Home automation

Router
Exploiting IoT in the Health & Wellbeing domain
WEB
ESSENCE
GuardiApp
DOCTOR
GUARDIAN
ESSENCE
Friends&Family
PATIENT
HOME
CLOUD
APPS
APPS

ESSENCE in one (busy) slide J
Android
App
Router
Fibaro
GW
Hue
GW
oxi
sca
prs
lamp
motion
light
smoke temp panic
button
CHINO
CEP
Notification
WebApp
ESSENCE
GuardiApp
PATIENT
DOCTOR
GUARDIAN
ESSENCE
Friends&Family
Comm
Module
Auth&Login
glu
HOME

“Gateway – Cloud” IoT Platform
FBK MAIN FOCUS
interoperability hurdle
control over my own data

Value-add infrastructure – a business context

The collaboration with Nively startup
• Help an existing product to extend their solution
• huge enhancement potential with IoT
• visual alerts
• notifications
• aided support
• smart home interactions
• but “off the shelf” products not
easy to integrate

innovation catalyst…the ESSENCE project
Diversity of requirements
Diversity of siloed IoT solutions
+
=>
+
=>users
technology
startup
FACT: wide set of requirements
FACT: IoT fragmentation
FACT: dreadful user experience
FACT: home care on the rise

The role of our research center
technology enhancement
market reach
integrate more IoT devices
differentiate from competition
value-add services
enlargemarket
segment
value-add creation

Pilots
• Municipality of Nice (France)
• APSP Vannetti (Italy)
la Direction de la Santé de la Ville de NiceApartment
Apartment
Apartment
Apartment
Reception
Doctor
Family

Next Steps…
• Huge market potentials in the eHealth domain
drafting a commercial collaboration framework…

Another example…Smart Agriculture

Courtesy of Paolo Spada, Luca Capra

the problem

the solution

in a nutshell

business aspects

where is the IoT?
• no broad set of applications encompassing “one IoT”
• with mobile phones and personal computers it was easier
• IoT devices very diverse, yet we tend to blur boundaries
• losing ability to tackle separately different markets
DISCLAIMER: no business expert but have matured insights into the business of IoT that might be useful to share

All IoT examples but…
smart locks
thermostats
lights
health
“Home”
power OK
costs LOW
“industrial”
power LOW
costs No constraintsWIDE SPECTRUM OF
REQUIREMENTS

SOME KEY QUESTIONS
•what business model?
•is this worth x Eur/month…
•to me?
•to my intended market audience?
•to my public administration?

Return on investment (ROI)
• EXAMPLE 1
• I spend a $ to buy a bottle of water
because I am thirsty
• the (immediate) need = I am thirsty
• who benefits? = me (private)
• willingness to pay for it = I need it badly
• when do I benefit = as soon as I get my
bottle
• I make an (private) investment, the
benefit is immediate
• VERY SHORT CYCLE, TANGIBLE,
UNAMBIGUOUS, CONCRETE
B2C
• EXAMPLE 1.b
• I spend $ to buy an iPhone
• the (immediate) need = I need a cool
device
• who benefits? = me (private)
• willingness to pay for it = can do cool
things with it
• when do I benefit = as soon as I get it
• I make an (private) investment, the
benefit is immediate
• VERY SHORT CYCLE, TANGIBLE,
UNAMBIGUOUS, CONCRETE
location is key – booth next to a fountain? “coolness” is key – no “cheap look” please…
IDENTIFY YOUR POTENTIAL MARKET TARGET…

• EXAMPLE 2
• I spend money to make my house energy efficient
• the (not so immediate) need = I need to save money on
my energy bills
• the (good for a common cause) need = I need to make
my life more sustainable
• who benefits? = me (private), the environment
• willingness to pay for it = I need it (not so badly), the
environment needs it (not so badly)
• TIME DIMENSION
• when do I benefit = after I paid the bills for needed
equipment with the money I saved
• I make an investment, the benefit might be for someone
else or not materialise until later
• LONG-ISH CYCLE, TANGIBLE, UNAMBIGUOUS, CONCRETE BUT…
B2G2CB2C
• EXAMPLE 2.b
• smart-lighting
• the (not so immediate) need = I need to save money on
my energy bills
• the (good for a common cause) need = I need to make
my city more sustainable
• who benefits? = the environment
• willingness to pay for it = the city balance sheet needs it
(in a couple of years, not so badly), the environment
needs it (not so badly)
• TIME DIMENSION
• when do I benefit = after I paid the bills for needed
equipment with the money I saved
• I make an investment, the benefit might be for someone
else or materialise when it is too late
• LONG-ISH CYCLE, TANGIBLE, UNAMBIGUOUS, CONCRETE BUT…

• EXAMPLE 3
• I have a business and I want to digitilise it
• spend money to make my production process more modern and efficient…
• the (not so immediate) need = I need to gain competitive advantage
• the (good for a common cause) need = I need to gain insights into my business operations
• who benefits? = my biz (private)
• willingness to pay for it = I need it (not so badly), long-term gains
• TIME DIMENSION
• when do I benefit = as soon as I am in a position to transform gathered data into differential
advantage that drives more customers to buy what I sell or reduces operating costs etc.
• I make an investment, the benefit is not immediate and depends on a proper strategy
• LONG CYCLE, UNTANGIBLE
B2B2C

The value (and diversity) of data
• the importance of bespoke modeling – multi-disciplinarity and
adjacent domain experts interactions
• cycles of learning (modeling) before I can be predictive and even
longer before I can be prescriptive…
• sensing and influence on results...
• IS IT WORTH IT?
(SENSE – DECIDE – ACTUATE)
Example: motors manufacturing biz
vibration, current, torque
MTBF: 60000 hours (!)
Raccoltadati
Descriptive
what happened?
Diagnostic
why did it happen?
Predictive
what will happen?
Preventive
what should I do?
Decision
Actuation
Decision support
Decision automation
human input requiredanalytics
the ROI CYCLE

market segmentation
ROI
COSTS
IMPACT
B2C
B2B
B2B2G
SHORT LONG
LOW
LOW
HIGH
HIGH
Descriptive
what happened?
Diagnostic
why did it happen?
Predictive
what will happen?
Preventive
how to avoid it?
build hindsight
what insight
do I need?
foresight and
optimise
Time
complexity
potential
gains

California US trip 2016 – know who are your
best customers

WHO we solve the problems for and WHY
• WHO
• application developers (rapid
prototyping)
• system integrators
• system admin of eHealth
• API framework managers
u WHY
u rapid development saves costs & time
u agility
u easy integration
u hide complexity, Web-based APIs
9

key message – who is your target?
• Cisco (Jasper), IBM (Bluemix), GE (Predix) …
• IoTango, Trilogis etc.
• propose a reference framework for validation of how to break-down a
complex problem space into more “palatable” “mouth-sized” chunks

up-front investments and ROIs
IS IT WORTH IT?

OPPORTUNITIES ARE TREMENDOUS
WARNING!!!
THIS IS DAUNTING IF YOU WANT TO EAT IT ALL
ROI CYCLE LENSES MIGHT HELP
NEED TO BREAK IT DOWN IN SMALLER CHUNKS
BUSINESS MINDSET

Conclusions and Future Directions
• IoT technology challenges are giving way to integration challenges and
most importantly to business challenges
• Interoperability becoming less and less of a stumbling block, focus on IoT
platforms that address also those issues
• yet, platform assets without a focus on application domain lead nowhere
• T-shaped models currently best bet for building success business stories
• Decentralisation technologies
• Increasing distribution and wide-coverage footprint
• Blurring of boundaries between Cloud and IoT
• Blockchains-based solutions
• Artificial Intelligence embedded in IoT

20180204 wf iot tutorial - small

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