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WINGS ICT Solutions

Profile Overview

WINGS ICT Solutions Ltd. WINGS © 2017

Outline

Company Profile

Team and timeline

Company Highlights

Verticals

Big Data Analytics

Infrastructure

Smart Wireless Access

Cloud-to-Things Continuum

Software Networks

Security

Contact Information

Company Profile

WINGS is an SME that focuses on the development of software for various vertical sectors (namely, water, energy, smart cities, food safety, health, transportation, finance) through

advanced wireless, cloud/IoT, big data and security technologies.

Company Profile

Team and timeline

Our Team:

Team Timeline

5 Senior Business and Technical Development Strategists

21 Solution Architects

50 Software developers

Strategy

Our team

Platforms for Verticals

Verticals – Water

Platform Cataract proaCtive mAnagemenT plAtform foR smArt Connected waTer

Member of:

Automatic Metering

& Control Communication

Technologies Analytics Dashboard

Use Cases &

Benefits

1. Water Metering and

Quality Control

2. Data Collection and

Transmission to the utility

center

3. Data analysis and

data correlation

Short range distance ( <25m ) Long range distance

a. Complex Event

Processing

b. Predictive Analytics

c. Data Correlation

d. Demand-Response

Management

a. Gateways (Arduino,

Raspberry)

b. Protocols (Modbus,

DNP3, IEC 870-5-104)

c. Technologies (NB-IoT,

LoRa/LoRaWAN,

Zigbee, Sigfox)

a. Physical (Flowrate,

Pressure,

Conductivity,

Temperature,

Volume)

b. Chemical (pH)

4. Real time data monitoring,

data visualization

Uses Cases Leakage Disinfection Intrusion Contamination Blockage Overflow Benefits Public health Reduced OPEX, CAPEX

Project PROTEUS, Collaboration

with IBM

Verticals – City|Home

Platform Starlit cloud baSed ioT smARt LIving plaTform

City environment

monitoring

Recommended optimal

route based on user

health status and smart

city conditions

Pollution sensors Pollen sensors

Humidity Temperature

Traffic sensors Weather APIs Smart watch

(wearable

device) for

deriving user

data.

HTTP

REST

MQTT

Smart Mobility:

Retrieval and analysis of

data from smart city

devices and sources

(APIs), as well as from

wearable devices, so as

to provide sophisticated

notifications and

recommendations

regarding real time

navigation based on user

health status and smart

city data.

City dashboard: Data

ingestion from

heterogeneous smart city

data sources, and

visualization of

measurements on

interactive heat-maps that

aim to present the overall

city picture at a glance.

Smart Mobility/

City Dashboard Sensors &

Data Sources

Communication

Technologies

Analytics User

Dashboard

Benefits Notifications/Al

arms Smart Mobility

- Possibility to monitor the position of an elderly/impaired person outdoors. - Notifications when an individual moves towards/enters into a high risk zone/hazardous area. - Personalised navigation instructions and public transportation help considering user preferences and health/well-being status. City Dashboard

- Visualisation of real time and historical data as well as derived knowledge - Prediction of city status

Project iKaaS

Verticals – City|Home


Learning of user preferences for home automation

based on Bayesian statistics

Notifications/Alarms

Home environment monitoring

and adaptation

Sensors:

Temperature,

Humidity,

Luminosity.

Actuators:

Lights,

Heating/Air-

conditioning.

HTTP

REST

MQTT

Learning and

forecasting of

user preferences

regarding indoor

environment/

home appliances

configuration

Prescriptive

analytics for

proactively taking

actions/offer

recommendations

Home environmental conditions

monitoring and automated

adjustment

Sensors &

Actuators

Communication

Technologies

Analytics Home

Dashboard

Benefits

Ease of every day life

Personalised indoor

environment adaptation and

assistance

Contribution to home

energy efficiency

Project iKaaS

Verticals – Health


Blood pressure forecasting using Timeseries

Notifications/Alarms

Wearable

devices.

Smart watch

with

accelerometer,

blood pressure,

heart rate

monitoring.

Bluetooth

Wi-Fi

REST

MQTT

Learning patterns

in user physical

status to identify

any abnormality

in usual patterns.

Family members

and/or

professional

caretakers can be

informed and

appropriate

alarms may be

raised if

necessary.

Remote Health Monitoring &

predictions. Input from user Sensors

Communication

Technologies

Analytics Dashboard

Benefits

Continuous well-being and health

monitoring and forecasting.

Notifications for individuals/family

members/health-care professionals

on potential health issues (e.g. in

case something unusual/potentially

problematic is observed such as

increasing blood pressure).

Project iKaaS|MigraineNet

MigraineNet infrastructure is based on:

FI-WARE Generic Enablers (GEs) and a Specific Enabler (SE) and

2 WINGS components: Knowledge Building and Inference

MigraineNet can be implemented also based on Hadoop and/or any other proprietary

platform.

MigraineNET

http://wings-ict-

solutions.eu/migrainenet

Framework: FI-Adopt

Open Call project

Duration: 04.2015–03.2016

Verticals – Health Platform MigraineNet

MigraineNet is a low-cost, personalized, cloud

based system for predicting migraines.

Mobile application for the user.

It can interact with other apps and wearable

devices for minimizing user’s effort and for

increasing pervasiveness.

Current performance: prediction of 3 out of 4

migraines, with the accuracy of one day.

Intelligent mechanisms in the cloud analyze the user

profile, the daily habit and the symptoms of the

users’ migraine incident so as to predict and

provide personalized insights on The next possible migraine incident

Potential migraine causes

Relief actions

Supporting info (pharmacies, doctors, articles)

Verticals – Health Platform MigraineNet

MigraineNet

https://www.facebook.com/MigraineNetApp

Verticals – Food Safety & Security

Project PhasmaFOOD

Food Quality Control Sensing Components

and Smart Devices Communication

Technologies Analytics

BLE

USB

WINGS focuses on embedded hardware design for the

PhasmaFOOD sensing device and contributes to the smart

embedded software, the development of mobile application and

the PhasmaFOOD cloud platform.

Verticals – Energy Project/Platform Energywater

Improving energy efficiency in industrial water processes through benchmarking

and benchlearning tools in Europe manufacturing industry http://energywater-project.eu/ | H2020 | 02.2016–01.2019

The EnergyWater project aims to provide

support to SMEs by enabling energy

efficient water processing, through the

development of the Energy Management

Self-Assessment (EMSA) web tool and

the creation of an Energy Angels Network

WINGS role:

WINGS is the main developer of the

EMSA web tool which is used to compare

and benchmark the energy performance

in industrial water process in European

manufacturing industries.

EMSA web-tool created by WINGS for

identifying and assessing saving

potentials

Verticals – Energy Project/Platform Energywater

Improving energy efficiency in industrial water processes through benchmarking

and benchlearning tools in Europe manufacturing industry http://energywater-project.eu/ | H2020 | 02.2016–01.2019

User friendly dashboards for selecting various

functions and scenarios

Benchmarking between different companies and

scenarios

Results and analysis

Verticals – Transportation Project MyCorridor

MyCorridor aims at achieving sustainable

travel in urban and interurban areas and

across borders by replacing private vehicle

ownership by private vehicle use.

WINGS role:

Theoretical analysis of the MaaS landscape

Specification of the system's architecture,

Design/development/evaluation of core

software modules and services,

Pilot realization (offering an advanced

navigation service – smart mobility)

Dissemination activities

Mobility as a Service in a multimodal European cross-border corridor http://mycorridor.eu/ | H2020 | 06.2017–05.2020

WINGS ICT Solutions participates as a shareholder in

Incelligent, which is a company focused on “Proactive

network resource and customer experience

management”.

Incelligent processes Big Data, with its advanced,

proprietary Machine-Learning algorithms, therefore

generating Predictive Analytics and Proactive

Actionable Intelligence

Verticals – FinTech Platform Incelligent

Verticals – FinTech Platform Incelligent Intelligent Classification of Customer Complaints

• Unbiased,

effortless

classification

• Real-time text

categorization

• Real-time

extraction of

keywords

• Accuracy reaching

85-95%

INLP methodology

Verticals – FinTech Platform Incelligent Proactive Loan Risk Management

Incelligent’s

Loan Risk

Management

module

Data input

• Demographics • Customer’s credit

vs. debit profile

• Products

• Income

• Payment history

• Concurrent debts • Macroeconomic

metrics

• Etc.

Is customer A going to

default within the next X

months?

o Yes/No classification

How likely is customer A

to default a loan within

the next X months?

o Prediction of probability

Accuracy >80%

on prediction of

loan default

Verticals – FinTech Platform Incelligent Next Best Offer Determination

Which product is a specific customer

X more likely to buy?

AND

Which is the appropriate channel?

Customer • Personalized approach

• Next best offer

• Increase in up-/cross-selling

• Targeted marketing actions

Data input

• Demographics • Customer’s credit vs.

debit profile • Products & product

history

• Income

• Payment history

• Concurrent debts

Propensity to buy

model

He/she has P % probability of accepting an offer on product A.

Verticals – FinTech Platform Incelligent Customer 360° - Segmentation

Creating customer microsegments

Hybrid customer segmentation based not

only on business rules/ criteria or demographics

but also on usage, behavioral, geographical

and other criteria -if necessary. Not based on

filtering

Detection of high-value customers

Used for:

Proactive marketing strategies,

Personalized services/ offers,

Next Best Offer.

Verticals – Collaboration with IBM

Finance (Banking & Insurance)

Loan consulting and management

Financial analysis and consulting

Client operations and gap analysis

Enterprise Data Warehouse (EDW) management;

data analysis and report

Bank vouchers' management

MS SQL Server &Oracle database tables

management

Design, built and deploy SSIS packages for ETL

Reporting and debugging in ETL system

UAT testing

House-keeping the production's HPS repository

Real Estate Credits' i-apply loan granting system

management (mainly mortgage loan disbursement)

Web banking application development for loan

creation and management

Energy & Water

Data analysis and database management

Data and code validation

Defect analysis and solution recommendations

Client consulting

Telecommunications

Network & Service management

Customer management (CRM)

Software Testing Process (UAT Testing assistance)

Data and code validation

Defect Analysis and solution proposing

WINGS collaborates with IBM regarding big data and associated services/applications on energy,

water and finance projects, as well as on projects with telecom providers

Artificial Intelligence – Big Data Analytics Competences

Artificial Intelligence – Big Data Analytics Introduction

Telecommunications Incelligent

Water

Smart Cities|Home

Healthcare Migrainet project

Energy

Finance

Transportation

Agriculture

Predictive & prescriptive

analytics

Data management Supervised learning

Unsupervised learning

Reinforcement learning

Deep learning

Handling of noisy or

incomplete data

Data storage optimisation,

etc.

Knowledge visualisation

Collaboration

with IBM and

other vendors

and SMEs Extensive publications

and patents in all the

areas above.

Artificial Intelligence – Big Data Analytics Products, Services & Systems

WINGS ICT Solutions has experience with SAP HANA platform and technically validated by SAP Startup Focus Program for the

telecommunication area

Problem statement:

Prediction of network load in time and space

Solution:

Allows operators to:

Dynamically allocate their resources

Offer free services on the event of low resources utilization (targeted campaigns)

Offer the highest possible QoS to all of his clients

Be energy efficient

Results:

Runs over SAP Hana

Cloud-based application

KGV tool

Infrastructure

Smart Wireless Access Collaboration (projects) with Intel

Challenge: Achieve the ambitious 5G goal for 1000x

capacity increase as cost-efficiently as possible

Investigated Solution – 5G capacity enablers – including

WINGS contribution:

Enabling more efficient usage of spectrum below 6 GHz –

Emergence of new spectrum sharing technologies

(Note: WINGS in other projects works on the management of

dense and ultra-dense networks, as well as on the holistic

management of spectrum, including bands of millimeter-wave

ranges)

WINGS ICT Solutions collaborates with Intel Mobile Communications on advanced

spectrum management and related RRM/MAC protocols


Europe: Licensed Shared Access (LSA) -- Operation of 3GPP

LTE network on licensed shared basis in the 2.3-2.4 GHz

frequency band LSA supports:

1st tier: Incumbent User

2nd tier: Licensee

1Markus Dominik Mueck, Srikathyayani Srikanteswara, Biljana Badic, “Spectrum Sharing: Licensed Shared Access (LSA) and Spectrum Access System (SAS)”, Intel

Whitepaper, October 2015

LSA System1

SAS architecture1

USA: Spectrum Access System (SAS) -- Operation of

Citizens Broadband Radio Service (CBRS) network (e.g.

3GPP LTE network) on licensed shared basis in the 3.55-

3.7 GHz frequency band

SAS supports: 1st tier: Incumbent User

2nd tier: Primary Access License (PAL) User

3rd tier: General Authorized Access (GAA) User

Scope of collaboration (including WINGS role): Focus on SAS architecture

Advanced MAC and RRM


Design and development of advanced access protocol for GAA users

Experimental evaluation (in addition to mathematical models and simulation)

SAS high-level architecture SAS testbed implementation

WINGS

Testbed


Use Cases:

WINGS is developing a platform ported on Android that

includes the following features:

Context acquisition

Network interface selection based on: Policies, Algorithms &

Machine learning

WINGS ICT Solutions collaborates with Intel Mobile Communications on Device Management

Problem statement:

“Dynamic network selection based on context/policy information

by multi-homing user equipment (UE) operating in evolving

heterogeneous wireless environments”.

Result:

Simultaneous multimedia

download from both of the

interfaces and playback on

the device

Solution: Multi-homing capable devices.

Support for simultaneous multimedia

download from both of the interfaces and

playback on the device

Simultaneous data download from both

of the interfaces Current ANDSF policy

Current context info


Development of a Spectrum Access Sharing (SAS) enabled SW/HW testbed/prototype

Design and Implementation of a Hybrid MAC for supporting access under SAS operation

Proposed testbed supported

technologies

•7 nodes

•10+ WiFi interfaces

•2.4GHz, 3.5GHz , 5GHz

Boards:

3x PC Engines ALIX.2D2

4x PC Engines APU1D

3x Raspberry Pi

1x Routerboard CloudRouter

1x Server

MiniPCI cards:1x Compex WLM200NX

2x Wistron DNMA-921x Routerboard R52NM

1x R11e-5HacD1x R11e-2HPnD

Antennae:1x 2.4GHz 14 dBi Sector1x 5-5.9GHz 19dBi 2x2

Panel 1x 2.4 GHZ 5dBi Omni1x 2.4 GHz 8dBi Panel1x 5.4-5.8 12dBi Omni

1x 5GHz Panel

…

Smart Wireless Access|5G System-level simulation platform. Project Fantastic5G

Flexible Air iNTerfAce for Scalable service delivery wiThin wIreless Communication networks of the 5th Generation http://fantastic-5g.eu/|H2020/5GPPP Phase 1|07.2015-06.2017

WINGS role:

To develop and conduct system-level simulations for proving benefits of new multi-service air interface

Traffic classification solutions and frame design aspects

Novel frame design aspects for supporting new multi-service air interface for 5G

Performance evaluation aspects

Service modelling

Traffic generation modeling

Modeling of service process

Mobility modeling

User/device behavior modeling

Environmental modelling

Cellular deployment

Urban/Suburban/Rural

Indoor/outdoor

Distribution of users

MAC layer modeling

PHY layer modeling

WINGS tool: Visualization of user’s density

Smart Wireless Access|5G System-level simulation platform. Project Fantastic5G

Flexible and efficient hardware/software platforms for 5G network elements and devices http://www.flex5gware.eu|H2020 5GPPP Phase 1|07.2015-06.2017

Smart Wireless Access|5G Hardware/software air interface components for 5G. Project Flex5GWare

WINGS role:

Reconfiguration management/ control functionality for dynamic HW/SW functional split and placement

Software modules for:

Policy-based and context-aware decision on (individual/multiple) resources reconfiguration

Cognitive dynamic HW/SW partitioning and function placement

Supported by:

Load prediction based on highly heterogeneous data

Gathering/filtering sensors measurements

Cognitive, dynamic HW/SW partitioning

On top of functions virtualization including

network centralization (Cloud-RAN)

Context aware decision making to whether a

function will be executed

In SW or HW

Either inside a network stack layer and/or between multiple network stack layers

Cooperation with management programs and

monitoring agents

5G SW modules and functions

Hardware abstraction of sensors in 5G context for

retrieving useful info and facilitating the dynamic

selection of communication technologies and

protocols

Flexible, reprogrammable and reconfigurable

functional composition of SW and HW components

at runtime

Smart Wireless Access|5G Hardware/software air interface components for 5G. Project Flex5GWare

Flex5GWare: Dynamic reconfiguration of future 5G HW/SW systems

https://www.youtube.com/watch?v=wxGhy6B_ZqE

Smart Wireless Access|5G Proof of Concept platforms. Cognitive Edge/ MAC/RRM. Project Speed-5G

quality of Service Provision and capacity Expansion through Extended-DSA for 5G

http://speed-5g.eu|H2020 5GPPP Phase 1|07.2015-12.2017

WINGS role:

Architectural components for

supporting eDSA

Novel RRM and MAC solutions for the

realization of intelligent nodes

capable of supporting eDSA as

defined by the project

5G system-level simulations on mobile

broadband scenarios (MBB) and

machine-type communications (MTC);

Proof-of-concept and demonstration

aspects through the usage of

equipment capable of supporting

licensed, lightly-licensed (e.g. 3.5GHz)

and un-licensed bands.

The project focuses on two

major innovations:

Resource management

techniques across

technology ‘silos’, and

Medium access

technologies to address

densification in mostly

unplanned environments.

Main use cases that WINGS

is interested are:

Mobile Broadband (MBB)

Vehicular Communications

(V2X)


SPEED-5G Dynamic Channel Assignment implementations by WINGS

https://www.youtube.com/watch?v=-Du6KvUKLoI

Main elements used for experimentation:

USRPs with the Open Air Interface (OAI)

framework;

Wi-Fi APs (COTS);

In-house software platform for network

measurement collection, analysis an

optimization;

5G system-level simulator

Experimentation can support licensed (LTE),

unlicensed (Wi-Fi) and lightly-licensed

(3.5GHz) bands.


Smart Wireless Access|5G Proof of Concept platforms. Cognitive Edge/ MAC/RRM. Project One5G

E2E-aware Optimizations and advancements for the Network Edge of 5G New Radio

https://5g-ppp.eu/one5g/|H2020/5GPPP Phase 2|06.2017-05.2019

WINGS role:

to lead the prototyping activities

to implement the technologies and

optimization techniques and integrate

them into the PoCs, as well as into the

system level simulation tool

Serve as an Innovation Manager and

supervise the work towards the market

and the respective business cases.

to propose and evaluate E2E user and

network optimization techniques

to study cell-less architectural aspects in a

multi-service environment regarding the

functional split of C-RAN functionalities

among RRHs and BBU pools.

One5G main goals:

to propose the necessary 5G extensions, to address

“megacities” and “underserved areas”

to build consensus on new features and provide

technical recommendations for moving 5G towards

“5G advanced (pro)”

to research and deliver highly generic performance

optimization schemes for achieving successful

deployment and operation, including optimizations

for both the network operator and the E2E user-

experienced performance.

to identify the cost driving elements for the roll-out

and operation and to propose adaptations to allow

sustainable provision of wireless services in

underserved areas under constrained circumstances.

to validate the developed extensions and

modifications through different approaches

Smart Wireless Access|5G Factories of the Future. Project Clear5G

Converged wireless access for reliable 5G MTC for factories of the future

http://www.clear5g.eu|H2020/5GPPP Phase 2, EU-Taiwan|09.2017-02.2020

WINGS role:

lead the integration and prototyping

activities

contribute to the definition of the FoF use

cases, requirements and system

architecture and to the design of the new

frame structure suitable for FoF

environments

contribute to the validation process of the

FoF prototype against the defined KPIs

contribute to the design of massive access

MAC

protocols

contribute to the design and

implementation of machine-learning and

data analytics capabilities distributed in

the network.

Clear5G objectives:

To define, investigate and develop physical layer

(PHY) enhancements for reliable MTC supporting

massive numbers of devices.

To design and implement Medium Access Control

(MAC) layer enhancements for integrated convergent

access.

To design, configure and optimize radio network

architectures and management mechanisms to fulfil the

needs of FoF applications.

To provide security enhancements at the physical layer,

contributing to the overall security solutions in a FoF

environment.

To validate and demonstrate the performance of the

project use cases in a realistic environment using

testbed facilities both in Europe and Taiwan.

To support the ongoing 5G Standardization

Smart Wireless Access|4G/5G QoE/QoS mechanisms. Project MONROE/ UNIQUE

UNIQUE will evolve, implement

and experiment with a unified

utility-based framework based

on both QoS (network-centric)

and QoE (user-centric) features

and parameters, which allows to

enhance, formulate and improve

various advanced network

operations in mobile

broadband (MBB)

infrastructures.

Utility-based Networking experiments for Improving QUality of Experience in mobile broadband environments

https://www.monroe-project.eu/|H2020|06.2016-02.2018

Smart Wireless Access|4G/5G QoE/QoS mechanisms. Project MONROE/ UNIQUE

UNIQUE targets at two key

runs of experiments and

respective software

extensions, depicting the

operation and benefit of

the proposed utility-based

framework.

WINGS/INCELLIGENT will deliver the web-based application and work on the design of the

extensions

WINGS/INCELLIGENT will implement the selection and multilink extensions required for

running and evaluating the QoE-based experiments.

Smart Wireless Access|4G Resource management mechanisms. Project CREW

Cognitive Radio Experimentation World http://www.crew-project.eu/|EC/FP7|10.2010-09.2015

WINGS joined as an Open Call partner for conducting experimentation on “Experiment-based Validation of Control Channels for Cognitive Radio Systems (EVOLVE)”

WINGS role:

Utilization of Wireless Mesh Networks (WMN) and Control

Channels for Cognitive Radio Systems for opportunistically

providing the necessary resources to sustain connectivity

Proceed to optimal creation and configuration of the WMN

Conveying info on the: context of operation, profiles, policies

Java-based implementation in the

w-iLab.t testbed

Real-time QoS measurements

Mobility provided by robot nodes

Smart Wireless Access|4G Resource management mechanisms. Project CREW

Main advantages for WINGS were:

Opportunity to conduct various experiments in order to assess the performance of D2D

networks under real conditions of operation of wireless networks

Usage of 802.11s protocol, in order to create an opportunistic/ D2D network

Availability of many mobile and static wireless nodes

Friendly user interfaces

Cloud-to-Things Continuum Introduction

Cloud-to-Things Continuum Cloud mechanisms. Project iKaaS

Intelligent Knowledge as a Service http://ikaas.com/|H2020 EU-Japan collaboration|10.2014-10.2017

WINGS role:

Distributed data processing

and storage

Data and service

descriptions and catalogues

Knowledge as a service

Autonomic service

management/orchestration

Knowledge-based IoT

applications

Validation through

experiments WINGS and iKaaS received the “Best Booth Award”

in the context of the EuCNC2017, June 12-15,

Oulu, Finland

Global and Local Cloud Service Catalogues for discovery of

(simple/complex) services

Global and Local Cloud Service Management functionality for

autonomous, dynamic composition, deployment, migration and

orchestration of services

Global and Local Cloud Data processing for big data analytics,

learning, knowledge inference and reasoning to support

autonomous behavior and self-adaptation of applications.

@Home

@Retail

@Home

@Transport/

Leisure

Cloud-to-Things Continuum Cloud mechanisms. Project iKaaS

Knowledge as a Service for Assisted Living in Smart City

https://www.youtube.com/watch?v=_QIoorrjAWM

Ubiquitous assisted living application including smart home,

health monitoring and forecasting and smart city services

Virtual Entities (software) that provide generic interfacing

to Smart Home, Smart City and Body area (wearables)

devices such as indoor temperature, pollen sensors, blood

pressure, etc.

Cloud-to-Things Continuum Cloud mechanisms. Project Select for Cities (Phase 1)

Select for Cities http://www.select4cities.eu|H2020 Pre-Commercial-Procurement|06.2017-07.2017

WINGS role:

Development of an open,

standardized, data-driven,

service-oriented and user-centric

platform that enables large-scale

co-creation, testing and validation

of urban Internet of Everything

(IoE) applications and services.

Phase 1: Concept Design

Phase 2: Prototype

Phase 3: Living Labs pilots

Antwerp, Copenhagen , Helsinki

Cloud-to-Things Continuum Cloud mechanisms. Project Select for Cities (Phase 1)

Data ingestion

Heterogeneous smart city data

sources

Visualization of measurements

on interactive heat-maps

Presentation of overall “city picture

at a glance”.

Visualisation

Real time / historical data

Knowledge / insights

Visual exploration (Zoom In

/Out functions).

Data source processing

Level of detail tuning

Representative maps

AdaPtive micROfluidic- and nano-enabled smart systems for waTEr qUality Sensing http://www.proteus-sensor.eu|H2020|02.2015-01.2018

WINGS role:

Embedded intelligence for context

awareness and reactive

capabilities (smart behaviour)

Embedded and cloud-based

intelligence for cognitive and

predictive smart systems (smart

application)

Cloud-to-Things Continuum IoT, Embedded intelligence. Project PROTEUS

WINGS and PROTEUS received the

“WssTP Water

Innovation SME

Award” in the context

of the Water Innovation

Europe 2016, June

21-23, Brussels

FIGURE 1 with WINGS’ overall

contribution to the project

Embedded intelligence for smart behaviour Identification of critical events (alerts/alarms)

Self-adaptation of measurement and transmission

profiles, readjustment of range profiles

Energy management

Embedded and cloud-based intelligence for

smart application Prediction of critical events

Data correlation for virtual sensing, event detection

based on combined measurements, detection of faulty

sensor (e.g. damage, calibration, calcium deposit)

for maintenance

Automatic

calibration

for supporting

sensor

manufacturing

Cloud-to-Things Continuum IoT, Embedded intelligence. Project PROTEUS

Demonstration of Embedded and Cloud-based software for Smarter Water Monitoring Systems

https://www.youtube.com/watch?v=A8luC9t13eU

WINGS role:

Adaptive and multi-objective

mapping algorithms for

application execution over

heterogeneous multi-core

HW platforms

Code parallelization tools

Cross-layer runtime

monitoring and analytics

Programming model

targeting a range of

‘application classes’ covering

the entire computing

continuum

Cross-Layer and Multi-Objective Programming Approach for Next Generation Heterogeneous

Parallel Computing Systems www.phantom-project.org|H2020 Customised and low power computing|12.2015-11.2018

Cloud-to-Things Continuum Cloud mechanisms. Project PHANTOM

Programmer- and productivity- oriented

software tools

Component-based programming model From embedded to the cloud

Enumerates shared data and communications

Captures non-functional requirements

Schemes/algorithms to parallelize code,

optimized for heterogeneous platforms

(CPUs, GPUs, FPGAs)

Multi-dimensional optimization on

heterogeneous systems

Multi-objective mapping of application

components to heterogeneous multi-core

HW platforms User requirements satisfaction and maximum

performance (latency, energy, security)

Data analytics Prediction of upcoming system states

Dependency detection between monitoring metrics

Cloud-to-Things Continuum Cloud mechanisms. Project PHANTOM

WINGS role:

Design, implementation and evaluation of the electronic board on which the non-

integrated sub-systems (sensing components array, WiFi/Bluetooth antenna, energy supply

system) will be mounted

Definition of APIs, data models and

cloud database organization

Integration of the multifunctional

electronic board and processing unit,

with the sensing array frontend,

the communication actors, the final

housing and by installing the

embedded software

Photonic miniaturised smart system for on-the-spot food quality sensing www.phasmafood.eu|H2020 SSI - Smart System Integration|01.2017-01.2020

Cloud-to-Things Continuum Cloud, IoT mechanisms. Project PhasmaFOOD

Design, implementation and evaluation of

the required electronic board Electronic board optimization for the resistive

type of sensors

Low power programmable analogue signal

conditioning and processing

Analogue routing/switching matrix

Smart signal processing capabilities and

advanced communication functions

Smart algorithms integration into the hardware

design

FPGAs/μ-controllers investigation

System Integration, packaging &

prototypes Manufacture of system housing according to

the design

Integration of sensing array frontend, the

communication actors, the final housing and by

installing the embedded software

Initial prototype testing of sensor operation,

data transfer to a smart phone, recording of

test spectra and images

Cloud-to-Things Continuum Cloud, IoT mechanisms. Project PhasmaFOOD

Software Networks Products, Services & Systems Platform realising management and orchestration functionality for various SDN/NFV infrastructures

AF_1 AF_2 AF_n

Data

Center Core network

RAN

Problem statement: address complexity of distributed management

Heterogeneity among domains, vendors and technologies

Use case: Unified Management Framework (UMF):

Key abstraction: Autonomic Function (AF) A core for the management of distributed AFs Specification of AF management interfaces

and implementation of APIs

Solution: A software platform for the

development, plug’n’play deployment, coordination/orchestration, and management of AFs

Results: Cross-segment applicability

Scope: Management & Orchestration functionality NFV competencies

A novel reconfigurable by design highly Distributed applications development paradigm

over programmable infrastructure

www.arcadia-framework.eu|H2020|01.2015-12.2017

WINGS role:

Lead the Arcadia

Component Development

Toolkit activities and be

the main contributor to the

IDE plug-in

implementation

Design the Arcadia Policy

framework and contribute

to the policy management

and adaptive

configuration features

Software Networks Knowledge-based orchestration. Project ARCADIA

Software Networks Knowledge-based orchestration. Project ARCADIA

The Arcadia framework includes a

Context Model for the underlying

cloud infrastructure

It is based on source level

annotations, improving the

developers’ ability to leverage the

benefits of cloud computing

Arcadia Policies affect the

deployment and configuration

facets of an application and can be

defined by the Service Provider

and the IaaS provider, as well as

being intrinsic to the application.

Software Networks Research & Development SLAs

Tackle the complexity of cloud computing SLAs and

contracts

Consider implications from software networks

Legal Model Terms to serve as

baseline for balanced, fair and

clear SLA contracts.

ISO compliance Technical model specifications for

supporting the SLA lifecycle

Requirements from all stakeholders

(cloud providers, cloud adopters,

legal entities, etc)

Technical gap analysis Close the gap between cloud

providers, cloud adopters and SOTA

technologies

Reach consensus among stakeholders

(cloud providers, cloud adopters,

policy makers, standards, research)

Identify research and

development activities and SOTA

technologies

Legal constraints and regulations

Legal Model Terms

Technical Models specifications

Cloud Service

Providers Cloud

Adopters

Research Legal

Regulation & Standardization

Security

Security Cybersecurity Architecture & Competences.

WINGS ICT Solutions Ltd.

Address: 336, Syggrou Avenue, 17673, Athens, Greece

Phone: +30 215 5011 555

Web: http://wings-ict-solutions.eu

E-mail: [email protected]

Contact information

@WINGS.ICT

@WINGS_ICT

@WINGS ICT Solutions

@WINGS ICT

Thank You!

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