Soutenance han2014v3

Smart Devices Collaboration for Energy Saving in Home Networks

Han YAN Ph.D defense 19 December, 2014

Orange Labs, France IRISA, France

Pr. Ye-Qiong SONG

Pr. Mohamed Yacine GHAMRI DOUDANE

Dr. Stéphane LOHIER

Pr. Bernard COUSIN

Dr. Cédric GUEGUEN

Dr. Jean-Paul VUICHARD

Jury

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Why Green Home ?

Economic Reason [2] [3]:

• In 2012, electricity price increases 2% faster than inflation

• In 2014, the total annual cost of electricity is estimated at 1,775€. In 2020, it is

expected to reach 2,486 €

[1] IPCC. Climate change 2013: The physical science basis, 2013

[2] Christophe Dromacque and Anna Bogacka. European residential energy price report, 2013

[3] Vers electricite plus chere, ecosocioconso, 2014

Environmental Reason [1]:

• Information and Communication technology sector represents 2% CO2emssion

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Global energy consumption is increasing with a rhythm at 3% every year

Residential energy consumption has multiplied 5 times [4]

Twh

Energy Consumption Evolution in France

[4] Le bilan énergétique de la France pour 2010. Technical report, SOeS, 2010

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Lighting13%

Cooling23%

Audio video 20%

Washing 15%

Personal computer

15%

Others14%

Other Specific DevicesEnergy Consumption [5]

Power Consumption in Digital Home

More and more energy is consumed for the entertainment usage

Devices work no more alone, the peripheral devices spread rapidly[5] Statistics Explained Eurostat. Consumption of energy. Technical report, 2012

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Energy Saving Challenges in the Home Network

NAS

Laptop

HGW

STB

Pad

controller

PLC

plug

WANPLC plug

WiFi Ethernet

ADSL PLC

Devices should be used efficiently:

• In collaborative services

• Energy consumption efficiency

Turned on / off efficiently

User is important in the home network

• Increasing satisfaction

• Behavior learning

User satisfaction

Energy efficiency

WAN

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Device level

• Advanced configuration and power interface specification

• Dynamic power management

• Ethernet, memory etc.

Network level

• Low power network technologies

• Home automation energy control

• Network connection power control

• Power control elements

State of the Art in Power Management

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Device level

• Advanced configuration and power interface specification [6]



Network level






[6] ACPI Advanced configuration and power interface specification

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Advanced Configuration and Power Interface

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Device level


• Dynamic power management [7]-[9]


Network level






[7] Trevor Pering, Tom Burd, and Robert Brodersen, The simulation and evaluation of dynamic voltage scaling algorithms,1998 international

symposium on Low power electronics and design, pages 76--81. ACM, 1998

[8] Padmanabhan Pillai and Kang G Shin. Real-time dynamic voltage scaling for low-power embedded operating systems. In ACM SIGOPS

Operating Systems Review, 35, pages 89–102. ACM, 2001

[9] Dirk Grunwald, Charles B Morrey III, Philip Levis, Michael Neufeld, and Keith I Farkas. Policies for dynamic clock scheduling. In

Proceedings of the 4th conference on Symposium on Operating System Design & Implementation-Volume 4, pages 6–6. USENIX

Association, 2000

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Dynamic Power Management

𝑃 ∝ 𝐶𝑉2𝑓 [10]

Capacitance

Voltage Frequency

Dissipated power

The voltage or the frequency or both parameters could reduce the power

consumption of the system:

• Dynamic voltage scaling power management [7] [8]

• Dynamic frequency scaling power management [9]

[7] Trevor Pering, Tom Burd, and Robert Brodersen, The simulation and evaluation of dynamic voltage scaling algorithms,1998 international

symposium on Low power electronics and design, pages 76--81. ACM, 1998

[8] Padmanabhan Pillai and Kang G Shin. Real-time dynamic voltage scaling for low-power embedded operating systems. In ACM SIGOPS

Operating Systems Review, 35, pages 89–102. ACM, 2001

[9] Dirk Grunwald, Charles B Morrey III, Philip Levis, Michael Neufeld, and Keith I Farkas. Policies for dynamic clock scheduling. In

Proceedings of the 4th conference on Symposium on Operating System Design & Implementation-Volume 4, pages 6–6. USENIX

Association, 2000

[10] Neil Weste and David Harris. Cmos vlsi design. A Circuits and Systems perspective, Pearson Addison Wesley, 2005

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Device level



• Ethernet, memory etc. [11]-[13]

Network level






[11] Chamara Gunaratne and Ken Christensen. Ethernet adaptive link rate: System design and performance evaluation. In Local

Computer Networks, Proceedings 2006 31st IEEE Conference on, pages 28–35. IEEE, 2006

[12] Maruti Gupta and Suresh Singh. Dynamic ethernet link shutdown for energy conservation on ethernet links. In Communications

ICC’07. IEEE International Conference on, pages 6156–6161. IEEE, 2007

[13] Kiran Puttaswamy, Kyu-Won Choi, Jun Cheol Park, Vincent J Mooney III, Abhijit Chatterjee, and Peeter Ellervee. System level power-

performance trade-offs in embedded systems using voltage and frequency scaling of off-chip buses and memory. In Proceedings of the

15th international symposium on System Synthesis, pages 225–230. ACM, 2002.

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Device level




Network level

• Low power network technologies [14]-[16]





[14] Carles Gomez, Joaquim Oller, and Josep Paradells. Overview and evaluation of bluetooth low energy: An emerging low-power

wireless technology. Sensors, 12[9]:11734–11753, 2012

[15] ZigBee Alliance. Zigbee specifications, 2008

[16] Zach Shelby and Carsten Bormann. 6LoWPAN: The wireless embedded Internet, 43. John Wiley & Sons, 2011.

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Low Power TechnologiesTechnologies ZigBee/

6LoWPAN(over 802.15.4)

Bluetooth LowEnergy

WiFi

IEEE spec 802.15.4 802.15.1 802.11 a/b/g

FrequencyBand

868/915 MHz;2.4 GHz

2.4 GHz 2.4 GHz;5 GHz

NominalRange

10-100 m 10 m 100 m

Chipset cc2531 cc2540 cx53111

RX 25 mA 19.6 mA 219 mA

TX 34 mA 31.6 mA 215 mA

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Device level




Network level

• Low Power network technologies

• Home automation energy control[17][18]




[17] Han and Jae-Hyun Lim. Design and implementation of smart home energy management systems based on zigbee. Consumer

Electronics, IEEE Transactions on, 56[3]:1417-1425, 2010

[18] Il-Kyu Hwang, Dae-sung Lee, and Jin-wook Baek. Home network conguring scheme for all electric appliances using zigbee-based

integrated remote controller. Consumer Electronics, IEEE Transactions on,55[3]:1300{1307, 2009

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Device level




Network level



• Network connection power control [19][20]



[19] Olivier Bouchet, Abdesselem Kortebi, and Mathieu Boucher. Inter-mac green path selection for heterogeneous networks. In

Globecom Workshops (GC Wkshps), 2012 IEEE, pages 487-491. IEEE, 2012

[20] Vincenzo Suraci, Alessio Cimmino, Roberto Colella, Guido Oddi, and Marco Castrucci. Convergence in home gigabit networks:

implementation of the inter-mac layer as a pluggable kernel module. In Personal Indoor and Mobile Radio Communications (PIMRC),

2010 IEEE 21st International Symposium on, pages 2569{2574. IEEE, 2010

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Device level




Network level




• Power control elements [21]


[21] Youn-Kwae Jeong, Intark Han, and Kwang-Roh Park. A network level power management for home network devices. Consumer

Electronics, IEEE Transactions on, 54[2]:487-493, 2008

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NAS

HGW

STB

PLC

plug

PLC plug

WiFi Ethernet ADSL PLC

Audio Video Use Case

Laptop

Devices work no more alone in home network

Devices are not used efficiently

Film selectionVideo Push

Film selection Video Push

Local network connection

time

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Contributions of the Thesis

• ZigBee mandatory energy saving solution

• ZigBee optional energy saving solution

An overlay network for energy control

• Architecture of HOPE system

• Three use cases

HOme Power Efficiency (HOPE) system

• Collaborative services analysis and definitions

• Refined overlay and auto learning power management

• Power delay tradeoffs

Collaborative power control management

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• Three use cases






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Power Consumption of Home Network DevicesPower consumption (Watt) of the home network devices

in different power states [22]:

States

Device

Working Idling Sleeping Soft-off

HGW 12.6 11 3.9 2

STB 21 19.2 13.5 2.5

Workstation 205 123.5 4.9 3.2

Laptop 79 54 5 2.5

PLC plug 6 3 2.6 0.15

A great power consumption difference from state to state

How to go to the low power consumption states?

[22] Yan, H, Vuichard, J, Cousin, B, Gueguen, C, and Mardon, G "Green Home Network based on an Overlay Energy Control Network"

in the book "Green Networking and Communications" , CRC Press, USA, oct 2013

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Working

Sleeping

Idling

Soft off

21 watt

19.2 watt

13.5 watt2.5 watt

An Example of STB Power StatesService execution time

Less than 4 hours

After 4 hours idling time

User controls manually

to turn on/off

No s

erv

ice

User s

erv

ice

request

• Long waiting time from idling to sleeping

• Explicit user commands are required to go to soft off state

• User controls manually to

go to sleeping state

• User service request to

turn on

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Overlay Energy Control Network

Proposition:

Controlling the devices by a ultra-low power consumption overlay network

The Overlay Energy Control Network (OECN) is formed by:

• Control nodes associated to each device

• OECN manager

Overlay Energy Control Network (OECN) can switche devices:

• From working or idling to sleeping power state much more quickly

• From working to soft off power state automatically

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• Three use cases






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ZigBee Mandatory energy saving Solution (ZMS)

The advantages of ZMS:

• A device can be turned off and can also be started up by the associated ZigBee

module which is always on

• This device can go to a real low power consumption state (soft off state)

• All the energy control nodes are ZigBee

• Energy control messages are sent via ZigBee network

workstation

HGWSTB WANPLC plug


Laptop

PLC plug

ZigBee

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Working

Sleeping

Idling

Soft off

Service execution time

Device stays in soft off state

while there is no service

• ZMS turns on device for

service execution

• ZMS turns off device while

there is no service

Device Power States Control Model by ZMS

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• Three use cases






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• Device itself can become the energy control node

• Energy control messages could be sent via the data home network

workstation

HGWSTB WAN


Laptop

ZigBee Optional energy saving Solution (ZOS)

The advantages of ZOS:

• It might not be possible to connect a ZigBee module on device

• ZigBee transmission diameter is limited, we have another alternative solution

ZigBee

PLC

plug

PLC

plug

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Working

Sleeping

Idling

Soft off

Service execution time

• ZOS switches device to working state

for service execution

• ZOS switches device to sleeping state


Device stays in sleeping state


Device Power States Control Model by ZOS

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33.5 21.3 22.4

735.3527.1 575.2

244.8

188.3197.6

475.5

425.2441.2

0

200

400

600

800

1000

1200

1400

1600

Self-controlled ZMS ZOS

Annual Power ConsumptionKwh

13.9 160 1612.3

436

4411.6

457

46.2

7.4

86

8

0

200

400

600

800

1000

1200

Self-controlled ZMS ZOS

Daily DelaySecond

Energy Consumption and Delay Resultsin 4 Types of Days

ZMS is more energy efficient; but ZMS has a relatively high delay

ZOS is a good tradeoff between the energy gain and delay

ZMS ZOS

21.97% 16.96%

Energy gain of two solutions

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Expense results

• ZMS and ZOS are profitable after 1.2 year

• ZMS is more profitable than ZOS after 1.6 year

Total expense is a sum of total ZigBee modules expense and

total electricity expense

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• Three use cases






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UPnP device ZigBee

NAS

LaptopSTB

HGW

Pad

controller

PLC

plug

PLC plug


HOme Power Efficiency system

HOme Power Efficiency (HOPE) system has two controlling methods:

• ZigBee on laptop, STB, PLC plugs and HGW

• UPnP Low Power on Pad and NAS

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UPnP Low Power

UPnP Low Power is proposed to implement different power saving modes to save

energy for devices

There are three types of elements:

• UPnP Low Power device

• UPnP Low Power power management proxy

• UPnP Low Power aware control point

Announcement:

• Power states

• Methods of waking

• Entry & exit information

UPnP Low Power device

UPnP Low Power proxy

UPnP Low Power

aware control point

Discovery the UPnP Low

Power devices with their

waking methods

• Monitor

• Send a “wake up”

or “go to sleep”

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Software Architecture of HOPE System

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Testbed for Home Power Efficiency system

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• Three use cases






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• Three use cases






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NAS

LaptopSTB

HGW

Pad controller

WiFi activation of

the HGW through

ZigBee

PLC plugs

Three Use Cases for HOPE system

Energy Gain :

2.9 Watt

NAS is woke up

through ZigBee and

Wake-On-Lan

NAS is switched off

through ZigBee and

UPnP Low Power

+ 23.4 Watt

Wake up

PLC plugs

through ZigBee

PLC plugs are

switched off

through ZigBee

+ 12 Watt

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• Three use cases






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Definition of collaborative power management

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Audio Video Use Case

NAS

HGW

STB

PLC

plug

PLC plug

Laptop


Film selection Video Push


Film selection Content directory function block

Content directory function block

Transfer server, content directory function blocks

Video stream decoder; display interface;

authentication; transfer client function blocks

Connection function block

Video Push


time

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Energy model

Operating

Idling

StartingDifferent phases while device is on

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Delay model

time

time

𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑡_𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒

𝑡_𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒

Operating

Idling

Starting

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• Three use cases






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Refined overlay algorithmsTwo propositions based on the refined overlay algorithms:

• Refined Overlay Power Management (ROPM):

We assume that ROPM registered user habits of using collaborative

services

• Refined Overlay Auto Learning (ROAL):

ROAL learns the habits how user uses their collaborative services

The control decisions depend on:

• ROPM Pre-saved user habit information

• ROAL Learns user habit when they request services

time

time

𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑡_𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒

𝑡_𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒

Operating

Idling

Starting

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Video Push

Power Control Elements

Reference model:

• Devices are configured as power control elements

• All necessary power control elements are on at the beginning

of the service

NAS

HGW

STB

PLC

plug

PLC plug

Laptop


Film selection


time

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Simulation Results on the Auto Learning Period

Simulation time varies from 10 hours to 500 hours

ROAL needs about 200 hours to learn an accurate value of the request time

Simulation time (h)

Re

qu

es

tti

me

(s

)

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Simulation Result on the Energy Gain

In a collaborative service, if several functional blocks are

needed lately, we gain more energy in these use cases

ROPM ROAL

41.85% 35.25%

Energy gain of two solutions

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Simulation Result on the Delay

• ROPM and ROAL have greater delay than the PCE, but

much more efficient in the term of energy saving

• ROAL has less delay than ROPM

How to be more accurate in the user habits learning?

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• Three use cases






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𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡_𝑙𝑒𝑎𝑟𝑛𝑒𝑑i,j = 𝑘=1𝑁𝑏_𝑠𝑒𝑟 𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑖,𝑗

𝑘

𝑁𝑏_𝑠𝑒𝑟

𝑡_𝑡𝑟𝑎𝑑𝑒𝑜𝑓𝑓 = 𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡_𝑙𝑒𝑎𝑟𝑛𝑒𝑑_𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒i,j × 𝛼

𝑡_𝑑𝑒𝑐_𝑜𝑛𝑖,𝑗𝑘 = 𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡_𝑙𝑒𝑎𝑟𝑛𝑒𝑑i,j − 𝑡_𝑡𝑟𝑎𝑑𝑒𝑜𝑓𝑓

Collaborative Overlay Power management Power-Delay Tradeoff Algorithm

The t_tradeoff will be configured by the user satisfaction requirement α

and the variance of the request (𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡_𝑙𝑒𝑎𝑟𝑛𝑒𝑑_𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒) as shown

in formula

Our proposition Collaborative Overlay Power management Power-Delay

Tradeoff (COPM-PDT α) algorithm varies by tradeoff coefficient α.

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Energy Delay Results

COPM-PDT 99 COPM-PDT 75 COPM-PDT 50

Energy gain 23.62% 31.29% 34.15%

Increased delay

0.5% 20.06% 43.75%

404

78.9 79.3 98.7 140.30

100200300400500

Delay (s)

135.8

97.874.7 67.2 64.4

0

50

100

150

Energy (Watt-hour)

Energy gain and delay for different parameters of α

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Energy Delay Results by tuning the tradeoff coefficient

Tradeoff Coefficient Tradeoff Coefficient

Dela

y per

serv

ice (

s)

Energ

yconsum

ption

per

serv

ice (

watthour)

Energy consumption

• Control algorithms based on the learned information

• Good energy efficiency and low waiting delay obtained

Delay

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Conclusion: Responses for Energy Saving in Home Network

We proposed an always-on low power network over the traditional network

• ZMS is energy efficient, high delay

• ZOS is a good tradeoff between the energy gain and the delay

A testbed of home power efficiency system is implemented

• Devices are turned on by service requests

• Energy efficiency with a high ease of use

Collaborative power management

• Collaborative service analysis

• User behavior learning

• Control algorithms based on the learned information

• Good energy efficiency and low waiting delay obtained

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Perspectives for Future Works

Short Term Perspectives:

• Different types of function blocks

• Resource allocation

• Collaborative analysis

• Analysis the tradeoffs between energy and other metrics

• User habits learning could be more intelligent and adaptive

Long Term Perspectives:

• A more heterogeneous overlay control network

• The collected information could be explored for other usages

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List of Publications International papers with peer review:• Yan, H, Cousin, B, Gueguen, C and Vuichard, J. “Refined Overlay Power Management in the Home Environment” IEEE

GREENCOM '2014, Taipei, Taiwan• Yan, H, Gueguen, C, Cousin, B, and Vuichard, J. "Collaborative Overlay Power Management based on the Delay-Power

Tradeoffs" IEEE FGCT '2014, London (Best paper award)• Yan, H, Fontaine, F, Bouchet, O, Vuichard, J, Javaudin, Lebouc, M, Hamon, M, Cousin, B, and Gueguen, C. "HOPE: HOme

Power Efficiency System for a Green Network" IEEE INFOCOM'2013 Demo/Poster Session, Turin, Italy

Book Chapter:• Yan, H, Vuichard, J, Cousin, B, Gueguen, C, and Mardon, G "Green Home Network based on an Overlay Energy Control

Network" in the book "Green Networking and Communications" (Published by CRC Press, USA, oct 2013)

Poster:• Yan, H, "Smart devices collaboration for a greener home network" Presented at "Journée des doctorants" Orange Labs,

France , Sep 2014

French patents:• Yan, H, Mardon, G, and Gueguen, C (Dec 2012). "Economiser l’énergie du réseau domestique tout en maintenant la QoE de

l’utilisateur" Patent 1261565 • Yan, H, Fontaine, F, and Vuichard, J (Avr 2013) "Procédé de contrôle de la consommation énergétique d’équipements d’un

réseau de communication local" Patent 1352881• Yan, H, Fontaine, F, (Oct 2013) "Gestion améliorée des connexions réseau", Patent 1359446 • Fontaine, F, Yan, H, (Dec 2013) "Technique de communication dans un réseau local", Patent 1362832 • Fontaine, F, Yan, H, (Feb 2014) "Mécanisme de lissage de la consommation électrique des équipements UPnP", Patent

1452655• Yan, H, Fontaine, F, (Feb 2014) "An adaptive proxy for compliance of the equipments to IEEE 1905" Patent 1454940 • Yan, H, Fontaine, F, (Sep 2014) "A repetition proxy for long distance communication between Bluetooth devices" Patent

1459280 • Fontaine, F, Yan, H,(Sep 2014) "Detection mechanism of Bluetooth Low Energy devices (BLE) on the IP network using UPnP"

Patent 1459283

Education

Soutenance han2014v3