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IoT LPWAN Geolocation –

LoRaWAN™ bringing disruption…

Thierry Lestable, Ph.D Director, Head of IoT Business Development

& Strategic Partnerships

Vice-Chair LoRa Alliance ™

This document and the information contained are Sagemcom property and shall not be copied or disclosed to any third party without Sagemcom prior written authorization 2

Sagemcom brings IoT Tracking Solutions to Life !

Tracking

capability is

THE Key

Disruptive

Enabler in IoT,

fueling

Innovative

Marketing

services!


GeoFencing, GeoTargeting, GeoPositioning, Tracking…

“Trigger geofences based on events like

- entering, dwelling, or leaving a place

- time of day

- crossing a speed threshold

- entering a city, state or country

- after traveling a given distance

- after a particular change in altitude ”

Source: Geoloqi

Smart Cities

http://cdn3.raywenderlich.com/wp-content/uploads/2015/02/geofence.png


IoT platforms play a critical Role for Business Adoption

Easy-to-use & Flexible

Interface to define Geo-

Fences

Scenarios & Rules Mgt

easy to implement

(Guidance)

Courtesy of

5 This document and the information contained are Sagemcom property and shall not be copied or disclosed to any third party without Sagemcom prior written authorization 5 This document and the information contained are Sagemcom property and shall not be copied or disclosed to any third party without Sagemcom prior written authorization


Geolocation – End-to-End (E2E) Complete Solution Roll-out

Sagemcom is the ONLY

player providing End-to-

End Complete Solution for

Geolocation:

- Gateway v2 (Geoloc

enabled)

- Location Solver

(Algorithms processing)

- LNS API/Metadata (not

standardized yet by

Alliance)

- - Devices with IMU

Fast, Trustworthy, Reliable E2E Geolocation Roll-out:

Joint optimization feasible depending on SLA targets


Filtering – Stop & Go illustration – Overall Evaluation Methodology

Sagemcom has established a whole Evaluation Methodology with system

Level Dynamic simulators to secure mid/long-term new Algorithms


Filtering – Illustration, Constant Velocity Models

i) Reliability of Dynamic Models

ii) Detection of Model Switching

iii) Capability to Adapt

Dyn

am

ic M

ode

l

Pro

ba

bili

ty u

sag

e

mobility detected


Chipset

End Point/Device

Gateway

LoRa™ Network Server (LNS)

IoT Platform

Service/Application

Waste Collection Street Lighting Smart ParkingSmart Build ingSecurity/safety

Asset Management/Tracking

Data Analytic Algorithms(incl. Geoloc)

Smart Agriculture


Data Fusion – Inertial Measurement Unit (IMU) – Part 1 (no GPS)

Gyroscope

accelerometer


IoT LoRaWAN™ – 1st Generation Siconia™ Multi-usage Device

Compatible with all LoRaWAN 1.0

network • Public

• Private

• Private + extension radio partner

• Frequency Unlicensed Band ISM • EU, FCC (on-going)

• Multiples sensors for multiple use

cases • Temp, Humidity • Acceleration + Gyro • Magneto • Pressure

• Hardened medium

• -20 ° C / + 65 ° C • IP65 or more

• JavaScript / application

• NFC interface

• Cases of flexible use • 1 product • N uses

• Adaptive Form Factor depending

of requirement (IEC compliancy…)


IoT LoRaWAN™ – Siconia™ {GPS/WiFi/BLE/LoRaWAN™} Tracking

Compatible with all LoRaWAN 1.0

network • Public

• Private

• Private + extension radio partner

• Frequency Unlicensed Band ISM • EU, FCC (on-going)

• Multiple sensors

• Acceleration • Pressure

• Hybrid Positioning:

• GPS /GNSS (incl. Offload) • WiFi sniffing • BLE Proximity Services • LoRa™ Triangulation

• Hardened medium

• -20 ° C / + 65 ° C • IP65 or more

• JavaScript / application • Cases of flexible use

• 1 product/N uses

• Rechargeable battery (micro-usb)

• Adaptive Form Factor depending of requirement (IEC compliancy…)


Data Fusion – IMU – GPS Offload Technology– Part 2 (with GPS)

GPS Offloading is outstanding good fit for such IoT LPWAN

scenarios & power consumption critical constrants!!


From Innovation to Disruption – Accelerating the IoT Market

Partner Innovation Program with real

industrial design Targets

(Cost , Quality, Time-to-Market efficiency)

Thanks to Leading Silicon Vendors willing

to accelerate the IoT LPWAN Market

Sagemcom will

circulate (VERY

SOON) this

New Generation

Tracker as a

Catalyst for

Asset

Management/Lo

gistics, and

Child/Elderly

care.

MAIN Target:

Disrupt the

Market!


Data Fusion - Map layout (exploitation example)

• Use of OpenStreetMap to get all roads around Sagemcom’s HQ

• Black box

• Area requested

• Blue line

• Road

• Green line

• Pedestrian


GPS information, machine learning (1/2)

Fingerprinting usually requires an initial ‘Offline’ phase, where calibration is

done for building an initial Radio Map (RSS, TOA, TDOA…).

Then, Machine Learning Algorithms can be used to classify/detect identical

patterns, and thus identify the Positions.

Ultimately, Online matching can also be used to adapt/enhance the initial

dataset/database.


GPS information, machine learning (2/2)

Supervised Learning

(Calibration phase needed)

1) K-Nearest Neighbor (k-NN)

2) Decision Tree (DT)

3) Neural Networks (NN)

4) Support Vector Machines

(SVM)

5) Bayesian inference


802.11 channels @2.4GHz – 14 channels

For sake of battery consumption, an Adapted

Scanning Policy, taking into account Radio

environment (e.g. Channel Popularity [1,6,11]) is

likely to improve the battery life. This could be

adapted by Application Layer.


Passive Vs Active Sniffing

Only 10% additional captured packets via Active sniffing

In our case, the IoT Device is only using Passing Sniffing,

for sake of battery consumption.


Spectrum Scanning Policy Design

WiFi Scanning Policy could be configured & driven by cloud requests,

and/or application (JS)


WiFi Sniffing: Database is the KEY (1/4)

https://developers.google.com/maps/documentation/geolocation/intro

{

"homeMobileCountryCode": 310,

"homeMobileNetworkCode": 410,

"radioType": "gsm",

"carrier": "Vodafone",

"considerIp": "true",

"cellTowers": [

// See the Cell Tower Objects section below.

],

"wifiAccessPoints": [

// See the WiFi Access Point Objects section below.

]

}

{

"macAddress": "00:25:9c:cf:1c:ac",

"signalStrength": -43,

"age": 0,

"channel": 11,

"signalToNoiseRatio": 0

}

WiFi sniffing principle is applicable to any database, similar to Google API. Once Location of AP is

stored, together with their MAC address (Id), then RSSI algorithm can be applied.



https://developers.google.com/maps/documentation/geolocation/intro#cell_tower_object




https://developers.google.com/maps/documentation/geolocation/intro#wifi_access_point_object






{

"considerIp": "false",

"wifiAccessPoints": [

{

"macAddress": "00:25:9c:cf:1c:ac",



},

{

"macAddress": "00:25:9c:cf:1c:ad",



}

]

}

{

"location": {

"lat": 51.0,

"lng": -0.1

},

"accuracy": 1200.4

}

Example of request towards API, with device seen

from 2 Access Points

AP#1

AP#2

Example of Geoloc result/answer



http://hotspots.wifi.comcast.com/

http://wifi.xfinity.com/default.php#find-a-hotspot

Clearly, COMCAST

could have the

relevant Database

information, through

its Xfinity® Wifi

service.










WiFi Sniffing: Database (& community) is the KEY (4/4)

https://wigle.net/

https://location.services.mozilla.com/

https://wigle.net/

https://wigle.net/




Finally….

Sagemcom is deeply committed to make IoT LPWAN Asset

Management/Tracking a Business reality.

LoRaWAN™ via its inherited Time stamping capabilities from

CSS, together with Very Low Power & High Range

takes a definitely a Key Disruptive position in that Market.

Combining with other IoT Technologies such as GPS Offload,

WiFi scanning and BLE secures a strong Leader’s Position.

This is a joint Optimization Problem from the Device towards

Cloud Processing/Data Analytics & Anchors positions

Databases, via Gateways.

Thanks for your interest…

Join our Innovation Program…

Download - Io t lorawan geolocation trends lpwan - v1

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