1

The Internet of (Important) Things

Thomas Watteyne

7 May 2019, Paris

Presented in partial satisfaction of the requirements for the degree of

“Habilitation à Diriger des Recherches” of Sorbonne University

PhD

• CITI, INSA Lyon

• Orange Labs (CIFRE)

2009 2015 2017

Postdoc

Prof. Kris Pister

Project lead OpenWSN

Sr. Networking Design Engineer

IETF 6TiSCH

co-chair

• MEng Telecom, INSA Lyon (2005)

• MSc, INSA Lyon (2005)

2011 2013 2019

Starting / Advanced Researcher Position

Associate teams

• UC Berkeley

• Univ. Michigan

• Univ. Southern California

Falco

co-founder

2/25

2

• PhD students1. Keoma Brun-Laguna1

2. Jonathan Munoz2

3. Mina Rady1

• Postdocs1. Mališa Vučinić

2. Tengfei Chang

3. Ziran Zhang

4. Remy Leone

• Research Engineers1. Trifun Savic

2. Yasuyuki Tanaka1

• Undergraduate Interns1. Ba Hai Le

2. Felipe Moran

3. Fabian Rincon Vija

4. Marcelo Augusto Ferreira1 co-advised with Pascale Minet2 co-advised with Paul Muhlethaler

Team(my sub-team since joining Inria in 2015)

3/25

• Positioning

• Representative Contributions• Channel Hopping

• Scheduling

• Characterization

• Research Program• Agile Networking

• Wireless Control

• Smart Dust

Outline

4/25

3

sensors

• pressure

• temperature

• flow

• level

• humidity

• current

• ...

actuators

• light

• valves

• buzzers

• diagnostics

• locks

• …

• computation

• communication

• Computer Science

• Networking

• Optimization

• Modeling and simulation

• Electronics Engineering

• Embedded software

• Testbed development

• A vehicle for transfer

• Standardization

• MVP and spin-off

Low-Power Wireless Mesh Networking

5/25

Environment Requirements

Reliability

Standards

Ease of use

Power consumption

Development cycles

Node size

0% 20% 20% 60% 80% 100%

source: OnWorld, 2005

1. Reliability: no data is lost

2. Predictability: power consumption and latency

3. Security: confidentiality, integrity, authentication

Grand ChallengeDependability“a network you can count on”

The Industrial Internet of Things (IIoT)

6/25

4

16 c

hannel offsets

e.g. 33 time slots

A

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FG

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• Motes are synchronized

• Communication follows a schedule

• Schedule gives tunable trade-off between

• packets/second

• latency

• robustness

…and energy consumption

7

Time Synchronized Channel Hopping

7/25

Approach

• minimal Viable Product (MVP)

• real-world validation

• cross-disciplinary research

• analysis

• simulation/emulation

• experimentation

• standardization

• Interop events

• benchmarkingTime

Synchronized

Channel

Hopping

8/25

5

SchedulingChannel Hopping Characterization

Representative Contributions

9/25

SchedulingChannel Hopping Characterization

Why Channel Hopping Makes Sense

1 T. Watteyne, A. Mehta, K. Pister., “Reliability Through Frequency Diversity: Why Channel Hopping Makes Sense“, ACM PE-WASUN, 2009.2 B. Kerkez, T. Watteyne, M. Magliocco, S. Glaser, K. Pister, “Feasibility Analysis of Controller Design for Adaptive Channel Hopping“, WSNPerf, 2009.3 J. Muñoz, P. Muhlethaler, X. Vilajosana, T. Watteyne. “Why Channel Hopping Makes Sense, even with IEEE802.15.4 OFDM at 2.4 GHz”. GIoTS, 2018.

2.405 GHz 2.480 GHz

Witnessing external interference1 Witnessing network dynamics2,3

O-QPSK

OFDM

10/25

6

SchedulingChannel Hopping Characterization

Quantifying Advantages of Channel Hopping

on average, without

channel hopping

almost proportional to

power consumption

and latency

• T. Watteyne, S. Lanzisera, A. Mehta, K. Pister, “Mitigating Multipath Fading Through Channel Hopping in Wireless Sensor Networks”, IEEE ICC, 2010.

• B. Kerkez, T. Watteyne, M. Magliocco, S. Glaser, K. Pister, “Feasibility Analysis of Controller Design for Adaptive Channel Hopping“, WSNPerf, 2009.

blind channel hopping

11/25

SchedulingChannel Hopping Characterization

• P. H. Gomes, T. Watteyne, B. Krishnamachari. “MABO-TSCH: Multi-hop And Blacklist-based Optimized Time Synchronized Channel Hopping”.

Wiley Transactions on Emerging Telecommunications (ETT), 2017.

Adaptive Channel Hopping using Game Theory

2.405 GHz 2.480 GHz

ACK=

1. ϵ-greedy algorithm

(here ϵ=0.025)

2. results embedded in ACK

(ordered list of frequencies)

“Multi-Armed Bandit” problem

37%

12/25

7

SchedulingChannel Hopping Characterization

A

BC

DE

FG

H

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Trade off:

• Bandwidth

• Reliability

• Latency

Lifetime

Approaches:

• Centralized

• Distributed

1 M. Vučinić, T. Watteyne, X. Vilajosana. Broadcasting Strategies in 6TiSCH Networks. Wiley Internet Technology Letters, 2018.2 R. Rivest, Network Control by Bayesian Broadcast. IEEE Trans. Inform. Theory. 1987;33(3):323–328.3 T. Chang, M. Vucinic, X. Vilajosana, S. Duquennoy, D. Dujovne. 6TiSCH Minimal Scheduling Function (MSF). IETF [work-in-progress], 2018.

Aspect 1: Common broadcast cell1

• Used for broadcasting beacons

• Today’s approach: periodic transmission

• Idea: apply Rivest’s Bayesian Broadcast Algorithm

results standardized3

13/25

SchedulingChannel Hopping Characterization

Trade off:

• Bandwidth

• Reliability

• Latency

Lifetime

Approaches:

• Centralized

• Distributed

1 T. Chang, T. Watteyne, Q. Wang, X. Vilajosana. LLSF: Low Latency Scheduling Function for 6TiSCH Networks. IEEE DCOSS, 2016.

Aspect 2: Latency1

• Idea: cascade cell allocation

• Built into the 6top Protocol

A

BC

DE

FG

H

I

J

14/25

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SchedulingChannel Hopping Characterization

1 M. Domingo-Prieto, T. Chang, X. Vilajosana, T. Watteyne. “Distributed PID-based Scheduling for 6TiSCH Networks”. IEEE Comm. Letters, 2016.

Aspect 3: Dynamic Resource Allocation1

A

BC

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Reason 1: F starts

producing more data

Reason 2: PDR of

GE degrades

time

cell u

sage 100%

80%

remove cells

add cells

Proportional

Integral

Derivative

15/25

SchedulingChannel Hopping Characterization

Real-World Deploymentsover 1,000 sensors on 3 continents

gather

store and analyze visualize

exploring applicability through system-level and cross-disciplinary research

Mendoza, Argentina

Lorient, FranceCalifornia, USA

16/25

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Machine learning:

• Random Forest

• K-Nearest-Neighbors

• Neural Network

• AdaBoost

SchedulingChannel Hopping Characterization

A Machine-Learning Based Connectivity Model

C. Oroza, Z. Zhang, T. Watteyne, S. Glaser, “Machine-Learning Based Connectivity Model for Complex Terrain Large-Scale Low-Power Wireless Deployments“,

IEEE Transactions on Cognitive Communications and Networking, 2017.

Goal: help deploy a network

predict connectivity

?

??

our 42,157,324 PDR

measurements

Annotations:

• Path ground distance

• Terrain complexity

• Vegetation variability

• Mean percent canopy

• Path angle

• Source canopy

• Receiver canopy

17/25

Transfer

Standardization

• Writing standards

• Interop events

• benchmarking

Deployments

Start-up

incubated at:

18/25

10

Wireless ControlAgile Networking Smart Dust

Research Program

19/25

OpenMote B

Agile Networking

Wireless ControlAgile Networking Smart Dust

13.2 km

100% PDR

RSSI -110 dBm

868 MHz

2-FSK@50-kbps

FEC

IEE

E8

02

.15

.4g

: 3

1 r

ad

io s

ett

ing

s

All setting, both 2.4

GHz and sub-GHz

A

BC

DE

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G

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IEEE802.15.4 PHY

IEEE802.15.4 MAC

6top [6P & SF]

IETF 6LoWPAN

IETF RPL

UDP

CoAP

• J. Munoz, T. Chang, X. Vilajosana, T. Watteyne, “Evaluation of IEEE802.15.4g for Environmental Observations”, MDPI Journal on Sensor Networks, 2018.

• J. Muñoz, P. Muhlethaler, X. Vilajosana, T. Watteyne, “Why Channel Hopping Makes Sense, even with IEEE802.15.4 OFDM at 2.4 GHz: GIoTS, 2018.

• J. Munoz, X. Vilajosana, T. Chang, “Problem Statement for Generalizing 6TiSCH to Multiple PHYs”, IETF 6TiSCH I-D [WIP], 2018.

20/25

longest range

longest range

good links

indoor applications

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Wireless ControlAgile Networking Smart Dust

Predictable Latency• today’s products guarantee delivery

• SmartMesh IP: 76,000 networks

• >99.999% end-to-end reliability

• no product guarantees latency

A

BC

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predicts

latency

PD

F

tail is infinite

cost of a narrower

distribution?

1 SmartMesh Power and Performance Estimator, analog.com2 K. Brun-Laguna, “Deterministic Networking for the Industrial IoT”, PhD thesis, 2018.

SmartMesh performance estimator output1

Goal: generalized methodology to turn

(schedule+topology) into latency distribution

21/25

Wireless ControlAgile Networking Smart Dust

Control Loops

1 Schindler, Watteyne, Vilajosana, Pister, "Implem. and Charac. of a Multi-hop 6TiSCH Network for Exp. Feedback Control of an Inverted Pendulum: IEEE WiOpt, 2017.

mass M

mass M

distance l

actuator

sensor (x)

sensor (ө)

full-state controller(critical delay 150 ms)

Step 1: 2-hop 6TiSCH network, hardcoded cascading1

Step 2: joint scheduling and control

latency has

distribution with

discrete increments

controller provides

multiple vaules of Fa,

for different latencies

22/25

12

Wireless ControlAgile Networking Smart Dust

Smart Dust1997

2019

• ARM Cortex-M0

• IEEE802.15.4-compliant

2.4 GHz radio

23/25

Wireless ControlAgile Networking Smart Dust

Crystal-Free Communication

OpenMote BWSN430

Telos B

• typical XTAL oscillators

offer 10-40 ppm drift.

• SCuM’s clock frequency

error up to 16,000 ppm.

Suciu, et.al.. “Experimental Clock Calibration on a Crystal-Free Mote-on-a-Chip”, IEEE INFOCOM, CNERT Workshop, 2019.

24/25

Step 1: demonstrating communication

between SCuM and OpenMote B1

Step 2: running the full OpenWSN stack on SCuM.

High-Risk High-gain

• Challenges:

• Clocking

• Power consumption and bootstrapping

• …

• Potential:

• Miniaturized wearables

• Everything wireless

• Very low cost solution

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• Positioning

• Representative Contributions• Channel Hopping

• Scheduling

• Characterization

• Research Program• Agile Networking

• Wireless Control

• Smart Dust

Outline

25/25

Representative Publications

source: Google scholar, 2 May 2019

1. Industrial Wireless IP-based Cyber Physical Systems. Thomas

Watteyne, Vlado Handziski, Xavier Vilajosana, Simon Duquennoy,

Oliver Hahm, Emmanuel Baccelli, Adam Wolisz. Proceedings of

the IEEE, Vol. PP, Issue 99, pp. 1-14, March 2016.

2. A Machine-Learning Based Connectivity Model for Complex Terrain

Large-Scale Low-Power Wireless Deployments. Carlos A. Oroza,

Ziran Zhang, Thomas Watteyne, Steven D. Glaser. IEEE

Transactions on Cognitive Communications and Networking,

21 August 2017.

3. OpenWSN: A Standards-Based Low-Power Wireless Development

Environment. Thomas Watteyne, Xavier Vilajosana, Branko Kerkez,

Fabien Chraim, Kevin Weekly, Qin Wang, Steven Glaser, Kris

Pister. Wiley Transactions on Emerging Telecommunications

Technologies. Volume: 23: Issue 5. 480–493. August 2012.

4. 6TiSCH: Industrial Performance for IPv6 Internet of Things

Networks. Xavier Vilajosana, Thomas Watteyne, Malisa Vucinic,

Tengfei Chang, Kristofer S.J. Pister. Proceedings of the IEEE, to

appear in 2019.

5. Constructive Interference in 802.15.4: A Tutorial. Tengfei Chang,

Thomas Watteyne, Xavier Vilajosana, Pedro Henrique Gomes.

IEEE Communications Surveys and Tutorials, Vol. 21, Issue 1,

pp. 217-237, September 2018.

All publications at www.thomaswatteyne.com.

(41 journals, 7 letters, 12 standards, 64 conference papers)