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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08/01/2015
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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Lighting
13%
Cooling
23%
Audio video
20%
Washing
15%
Personal
computer
15%
Others
14%
Other Specific Devices
Energy 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
WAN
PLC 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 ∝ 𝐶𝑉2 𝑓 [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 Technologies
Technologies ZigBee/
6LoWPAN
(over 802.15.4)
Bluetooth Low
Energy
WiFi
IEEE spec 802.15.4 802.15.1 802.11 a/b/g
Frequency
Band
868/915 MHz;
2.4 GHz
2.4 GHz 2.4 GHz;
5 GHz
Nominal
Range
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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08/01/2015
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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08/01/2015
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 selection
Video 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 Devices
Power 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 States
Service execution time
Less than 4 hours
After 4 hours idling time
User controls manually
to turn on/off
Noservice
Userservice
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
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
• 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
735.3
527.1 575.2
244.8
188.3
197.6
475.5
425.2
441.2
0
200
400
600
800
1000
1200
1400
1600
Self-controlled ZMS ZOS
Annual Power ConsumptionKwh
13.9 160 16
12.3
436
44
11.6
457
46.2
7.4
86
8
0
200
400
600
800
1000
1200
Self-controlled ZMS ZOS
Daily DelaySecond
Energy Consumption and Delay Results
in 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 deviceZigBee
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 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_𝑟𝑒𝑞𝑢𝑒𝑠𝑡
𝑡_𝑎𝑣𝑎𝑖𝑙𝑎𝑏𝑙𝑒
Operating
Idling
Starting

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• Three use cases

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Refined overlay algorithms
Two 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_𝑟𝑒𝑞𝑢𝑒𝑠𝑡
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
Video Push
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)
Requesttime(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=1
𝑁𝑏_𝑠𝑒𝑟
𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡𝑖,𝑗
𝑘
𝑁𝑏_𝑠𝑒𝑟
𝑡_𝑡𝑟𝑎𝑑𝑒𝑜𝑓𝑓 = 𝑡_𝑟𝑒𝑞𝑢𝑒𝑠𝑡_𝑙𝑒𝑎𝑟𝑛𝑒𝑑_𝑣𝑎𝑟𝑖𝑎𝑛𝑐𝑒i,j × 𝛼
𝑡_𝑑𝑒𝑐_𝑜𝑛𝑖,𝑗
𝑘
= 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 98.7 140.3
0
100
200
300
400
500
Delay (s)
135.8
97.8
74.7 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
Delayperservice(s)
Energyconsumptionperservice(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

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