Borgiattino, Carlo

Casetti, Claudio Ettore

Chiasserini C.-F.

Rossi, Claudio

English

Claudio Rossi

Carlo Borgiattino

Claudio Casetti

Carla-Fabiana Chiasserini

Crossref

Energy-efficient wi-fi gateways for federated residential networks

PORTO Publications Open Repository TOrino

Energy-efficient Wi-Fi Gateways for Federated Residential Networks
Claudio Rossi, Carlo Borgiattino, Claudio Casetti, Carla-Fabiana Chiasserini
Department of Electronics and Telecommunications, Politecnico di Torino
Email: {carlo.borgiattino,claudio.rossi,claudio.casetti,chiasserini}@polito.it
Abstract—Cooperation among federated APs in dense urban
areas can yield energy saving by allowing under-used devices
to hand over their wireless stations (WS) to nearby APs
and temporarily switch off while meeting user expectations in
terms of throughput. We demonstrate the effectiveness and the
benefits of our energy-efficient cooperative protocol through a
real deployment emulating a residential scenario. The demo
we propose is highly interactive, as users can generate traffic
within a BSS through a wireless station, like a smartphone or
a notebook, and observe, through a web interface, the protocol
behavior and the network topology changes caused by the new
traffic scenario.
I. INTRODUCTION AND NETWORK ARCHITECTURE
Today ICT is accountable for 2-4% of the worldwide
carbon emissions and this number is projected to double
by 2020 [1]. Telecoms infrastructure and devices account
for 34% of the total ICT consumption, and the 95% of this
share is due to home and access networks [2]. Within the
EU FP7 FIGARO project [3], we address the emerging need
for green technologies by proposing a cooperation scheme
among “federated” residential Gateways (GW). A Federation
is a logical overlay relationship among trusted home GWs
with the purpose of content exchange and resource sharing.
Federated GWs can communicate and coordinate with each
other using an out-of-band channel, which runs through
their backhaul Internet connection. Each GW offers local
wireless access acting as Access Point (AP) and creating a
802.11 a/b/g/n BSS (Basic Service Set) over independently-
managed (but possibly coordinated) frequency channels.
The cooperation scheme enables GWs within radio range
of each other to manage their associated devices, or Wireless
Stations (WS): (i) under low traffic conditions, a GW can
hand over the associated WSs to nearby GWs and temporar-
ily switch off; (ii) under high traffic conditions, a GW can
selectively hand over one WS at a time in order to lower
the congestion. When GWs are “off”, they no longer have
wired/wireless connectivity and only run a low-cost, low-
power radio interface, e.g., a IEEE 802.15.4 card, that can
be used as wake-on WLAN interface [4].
II. LOAD ESTIMATION AND THE ASSOCIATION
MANAGEMENT PROTOCOL
The implementation of the energy-efficient cooperation
scheme for federated GWs consists of three main steps.
The first one is the estimation of the current load of the
wireless channel and of its saturation throughput, through
passive traffic measurements as detailed in [5]. By com-
paring the current load to the saturation throughput S, a
GW classifies its status as either Light, Regular, or Heavy.
In the Light status, traffic likely comes from background
communications to/from the WSs, prompting the GW to
try and relocate them, switch itself off and save energy.
The Heavy status, instead, characterizes an overloaded BSS,
where some WSs should associate to other BSSs to benefit
from load balancing. In the Heavy status a GW tries to
relocate one WS at a time, starting from the one having
the lowest bit rate, until the Regular status is reached. A
GW in Regular status is too busy to switch itself off while
it does not need to be relieved of some of its WSs. It might,
however, accommodate relocated WSs within its BSS.
Passive traffic measurements account for the second step
too, in which a GW trying to relocate one or more of its
WS compiles a traffic profile of each WS, detailing the
throughput of its active (downlink/uplink) traffic flows.
The third and final step amounts to an inter-GW commu-
nication in which a requesting GW, aiming at relocating one
or more of its WSs, sends a handover request, along with
the traffic profiles of relocatable WSs, to candidate GWs.
A computation of the projected load (current estimated load
and expected load from the incoming WS profile), and its
comparison against the estimated saturation throughput S,
allow a candidate GW to assess its suitability to provide
help to others (i.e., if the additional WS does not plunge
the GW in Heavy status). Responses returned by candidate
GWs let the requesting GW identify a feasible relocation
strategy. Among the feasible solutions that allow a GW to
relocate its WSs, the allocation maximizing the average data
rate of the WSs is selected. For a Heavy initiated request,
if no viable relocation is found the requesting GW needs to
wake up a neighboring “off” GW and repeat the requesting
procedure. Further details about the protocol can be found
in [5].
III. TESTBED ARCHITECTURE
As shown in Figure 1, the testbed is a federated network
composed by three GWs, six WSs, a web server and a router.
Each GW acts as AP of a 802.11g network operating on a
different channel in the 2.4 GHz band, and secured with
WPA-PSK, which is part of the 802.11i standard and it is
widely adopted in residential networks. Every WS knows all
access keys in the federated network, hence it can associate
ROUTER
GW1 GW2
GW3
web
server
Figure 1. Testbed architecture.
to any GW. We included four laptops and two smart-phones
as WSs, so as to create the heterogeneity of a real-life
residential environment.
When “on”, GWs notify the web server about the WSs
associated to them, along with the WSs traffic profile. The
web server has two functions: (i) it provides contents to
WSs and (ii) it graphically shows the testbed status over
time in a web page. Specifically, such testbed monitoring
interface shows the actual association of the WSs, the GWs
current load (L), the aggregated throughput, the saturation
throughput (S), and L/S, which is used to determine the
GWs status. On top it displays the total energy consumption
within the federation and, when the protocol is started, the
total energy saving with respect to the case where all GWs
are always “on”. The layout of the monitoring interface is
given in Figure 2.
A. Hardware and software description
GWs feature an Alix PC Engines motherboard, equipped
with an AMD Geode 500 MHz processor, one IEEE 802.11
b/g compliant Wistron DCMA-82 Atheros wireless card and
one omnidirectional antenna with a gain of 5 dBi. Each Alix
runs OpenWrt Backfire, a Linux distribution for embedded
devices, while the WSs can run any operating system.
Figure 2. Testbed monitoring interface.
The passive traffic measurements needed by the protocol
are implemented on the GW within the mac80211 module of
the Linux wireless driver compact-wireless 2011-21-01 [6].
Note that, since we modified only the mac80211 module and
not the GW hardware, such measurements work on any de-
vice. Specifically, at the BSS level we keep track of: average
size of the packet payload, average SNR, average data rate,
average packet error rate (PER), number of associated WSs.
For each WS, at the MAC layer we measure: all throughput
contributions (elastic/inelastic, downlink/uplink traffic) with
the corresponding number of handled packets, the average
packet payload, the average data rate, the average SNR. The
PER is computed as the estimated fraction of the erroneously
received/transmitted packets. For received packets we count
the CRC errors (at the PHY and MAC layer), while for
transmitted packets we count all unsuccessful transmission
attempts at the physical layer. This results in a worst case
PER estimation, as collisions are also included in the count 1.
All measurements are made available to the application by
the mac80211 module every 2 s.
B. Testbed scenario
Among all realistic traffic patterns, we consider three
kinds of traffic flows: mice TCP, elephant TCP and UDP.
A mice TCP flow represents a whispering WS, e.g., a user
browsing the web without any kind of background traffic.
We emulate whispering stations by implementing the traffic
model for mobile web browsing proposed in [7], where
the mean web page dimension is 4 KB and the mean
reading time (interval time between two consecutive web
page requests) is 15 s. An elephant TCP flow consists of
a bulk HTTP download of a content locally stored at the
web server. Finally, we introduce UDP flows with a bit rate
similar to widely used audio/video peer-to-peer real-time
applications, like Skype. All flows are established between
WSs and the web server.
Initially we consider two WSs associated to each GW.
Then, we show the behavior and performance of the offload
procedure in the following cases: (1) one or more GWs are
in Light state and try to get rid of their WSs in order to
switch off; (2) one or more GWs are in Heavy state and try
to relocate their WSs without waking up additional GWs,
(3) one or more GWs are in Heavy state and a GW in “off”
state has to be woken up to accommodate for the relocation.
During the demo users will be allowed to interact with one
client, either a mobile phone or a notebook, adding traffic
to the BSS to test the reaction of our scheme, e.g., a user
could surf the web or start a bulk TCP-based transfer. The
system behavior will be shown by the testbed monitoring
interface (see Figure 2).
1Collisions cannot be discriminated from errors due to harsh channel
conditions without changing the WS software or the 802.11 protocol.
C. Energy consumption model
We consider that the power consumption of an Alix
equipped with an 802.11g radio interface is equal to P i =
3.68 W in idle mode [8]. Depending on the data rate, the
consumption in transmission mode is Pt ∈ [0.24, 0.44] W
and in receive mode is Pr ∈ [0.27, 0.38] W [8]. The
consumption of the low-cost, low-power interface (assumed
to be an 802.15.4 radio) is ps=186 µW in sleep mode and
pa =165 mW in receive/transmit mode [9]. As proposed
in [8], we take into account the fraction of energy consumed
by each packet crossing the protocol stack, distinguishing
between transmission, Pxt = γxtRt, and reception, Pxr =
γxrRr. The parameter Rt (Rr) is the packet transmission
(reception) rate, while γxt and γxr are hardware-dependent
factors. Thus, we compute the energy consumed by a GW
in an observation period T as follows:
T · [ton (Piton,i + (Pr + Pxr)ton,r + (Pt + Pxt)ton,t + ps)
+toffpa]
where ton (toff ) is the time fraction during which the GW
is “on” (“off”), and ton,i, ton,r and ton,t are, respectively,
the idle, receive and transmit time fraction during the “on”
period.
D. The handover problem
Despite the broad spectrum of schemes and protocols
aimed at providing seamless or faster handover in 802.11
networks, none of them has reached wide adoption. Solu-
tions based on Mobile IP are complex because they rely
on many dependencies, requiring both new hardware and
new software to be deployed. This would imply additional
costs for both hardware vendors and network operators, with
no practical business models that justify these additional
expenses [10]. The Inter Access Point Protocol (IAPP), stan-
dardized as 802.11f [11], cannot be used to provide faster
re-authentication with 802.11i-based security standards, as
the latter does not allow security context transfer between
GWs. 802.11i provides some alternatives to reduce hando-
ver delays, namely Pairwise Master Key Caching (PMK
Caching) and pre-authentication over the distribution system.
The main problem of these schemes is that they are WS
initiated, which makes them unsuitable for a gateway-centric
solution like the one we propose. The recent 802.11v [12]
IEEE standard includes a BSS transition management that
enables a GW to request WSs to handover to another GW, or
to indicate a set of preferred GWs to a WS, based on network
load balancing needs. However, the GWs must be part of
the same Extended Service Set (ESS) and the WSs should
support this scheme. In conclusion, nowadays there is no
inter-domain network-managed seamless handover solution
able to be deployed without changing both GWs and WSs,
and without requiring new network element such as the
home agent of Mobile IP. As a result, current GWs and
WSs come with no fast handover technology ready to use.
Thus, since we want to propose an easily deployable and
adoptable solution, we relax the need of a seamless handover
and handle the handover as follows. For each WS to be
relocated, the selected candidate GW inserts the WS’s MAC
address in its white list, while all other GWs (including the
offloading one) insert the WS’s address in their black list.
This triggers the handover of the WS: the network manager
of the WS will automatically scan for GWs and it will have
no choice but to associate to the selected candidate GW.
We recall that the WS network manager knows all network
parameters necessary to associate to any GWs within the
federated network. In the demo, we show that a handover
takes a couple of seconds, thus causing a very short service
disruption for the users; however, interrupted flows must be
restarted at the application level. Future work will focus on
the design and the implementation of a seamless handover
technique and will specifically address this issue.
IV. CONCLUSION AND FUTURE WORK
Our testbed shows a practical approach for real networks
aiming at providing considerable energy savings without
(i) significantly affecting user experience nor (ii) requiring
changes in the WS hardware or software. Savings can be
extended to ISPs by adopting DSLAM line aggregation as
shown in [13]. Furthermore, our framework provides guide-
lines for the design of the GW hardware so as to benefit from
potential energy savings. Our future work will focus on how
to achieve a seamless handover in heterogeneous networks
and on how to account for the presence of interference in
the capacity estimation.
REFERENCES
[1] Global e-Sustainability Initiative (GeSI), http://www.theclimategroup.org/assets/
resources/publications/Smart2020Report.pdf.
[2] R. Bolla, R. Bruschi, K. Christensen, F. Cucchietti, F. Davoli, S. Singh, “The
potential impact of green technologies in next generation wireline networks - is
there room for energy savings optimization?,” IEEE Communications, vol. 49,
no. 8, 2011.
[3] EU FP7 FIGARO project, http://www.ict-figaro.eu/.
[4] N. Mishra, K. Chebrolu, B. Rama, A. Patha, “Wake-on-WLAN,” International
Conference on World Wide Web (WWW), Edinburgh, Scotland, May 2006.
[5] C. Rossi, C. Casetti, C.-F. Chiasserini, “Energy-efficient wireless resource
sharing for federated residential networks,” IEEE WoWMoM, San Francisco, CA,
June 2012.
[6] Wireless driver, http://linuxwireless.org/
[7] G.-F. Zhao, Q. Shan, S. Xiao, C. Xu, “Modelling web browsing on mobile
Internet,” IEEE Communications Letters, vol. 15, no. 10, 2011.
[8] A. Garcia-Saavedra, P. Serrano, A. Banchs, G.Bianchi, “Energy Consumption
Anatomy of 802.11 Devices and its Implication on Modelling and Desing,” Co-
NEXT’12, Nice, France, Dec. 2012.
[9] http://www.libelium.com/support/waspmote
[10] Mobile IP Deployment Difficulties http://www.ietf.org/proceedings/83/slides/
slides-83-mobopts-0
[11] IAPP http://gredes.ifto.edu.br/wp-content/uploads/802.11F-20031.pdf
[12] 80211v http://standards.ieee.org/findstds/standard/802.11v-2011.html
[13] E. Goma, M. Canini, A. Lopez Toledo, N. Laoutaris, D. Kostic, P. Rodriguez,
R. Stanojevic, Pablo Yague Valentin, “Insomnia in the access or how to curb
access network related energy consumption,” ACM SIGCOMM, Toronto, Canada,
Aug. 2011.


Energy-efﬁcient wireless resource sharing for federated residential networks,”

Global e-Sustainability Initiative (GeSI),

Modelling web browsing on mobile Internet,”

Pablo Yague Valentin, “Insomnia in the access or how to curb access network related energy consumption,”

The potential impact of green technologies in next generation wireline networks - is there room for energy savings optimization?,”

Energy-efficient Wi-Fi Gateways for Federated Residential Networks

ROSSI, CLAUDIO

BORGIATTINO, CARLO

CASETTI, CLAUDIO ETTORE

CHIASSERINI, Carla Fabiana

PORTO@iris (Publications Open Repository TOrino - Politecnico di Torino)

04 August 2020POLITECNICO DI TORINORepository ISTITUZIONALEEnergy-efficient Wi-Fi Gateways for Federated Residential Networks / Rossi C.; Borgiattino C.; Casetti C.; ChiasseriniC.-F.. - STAMPA. - (2013), pp. 1-3. ((Intervento presentato al convegno IEEE WoWMoM 2013 Demo Session tenutosi aMadrid (Spain) nel June 2013.OriginalEnergy-efficient Wi-Fi Gateways for Federated Residential NetworksPublisher:PublishedDOI:10.1109/WoWMoM.2013.6583364Terms of use:openAccessPublisher copyright(Article begins on next page)This article is made available under terms and conditions as specified in the  corresponding bibliographic description inthe repositoryAvailability:This version is available at: 11583/2507385 since:IEEE / Institute of Electrical and Electronics Engineers Incorporated:445 Hoes Lane:Piscataway, NJ 08854:Energy-efficient Wi-Fi Gateways for Federated Residential NetworksClaudio Rossi, Carlo Borgiattino, Claudio Casetti, Carla-Fabiana ChiasseriniDepartment of Electronics and Telecommunications, Politecnico di TorinoEmail: {carlo.borgiattino,claudio.rossi,claudio.casetti,chiasserini}@polito.itAbstract—Cooperation among federated APs in dense urbanareas can yield energy saving by allowing under-used devicesto hand over their wireless stations (WS) to nearby APsand temporarily switch off while meeting user expectations interms of throughput. We demonstrate the effectiveness and thebenefits of our energy-efficient cooperative protocol through areal deployment emulating a residential scenario. The demowe propose is highly interactive, as users can generate trafficwithin a BSS through a wireless station, like a smartphone ora notebook, and observe, through a web interface, the protocolbehavior and the network topology changes caused by the newtraffic scenario.I. INTRODUCTION AND NETWORK ARCHITECTUREToday ICT is accountable for 2-4% of the worldwidecarbon emissions and this number is projected to doubleby 2020 [1]. Telecoms infrastructure and devices accountfor 34% of the total ICT consumption, and the 95% of thisshare is due to home and access networks [2]. Within theEU FP7 FIGARO project [3], we address the emerging needfor green technologies by proposing a cooperation schemeamong “federated” residential Gateways (GW). A Federationis a logical overlay relationship among trusted home GWswith the purpose of content exchange and resource sharing.Federated GWs can communicate and coordinate with eachother using an out-of-band channel, which runs throughtheir backhaul Internet connection. Each GW offers localwireless access acting as Access Point (AP) and creating a802.11 a/b/g/n BSS (Basic Service Set) over independently-managed (but possibly coordinated) frequency channels.The cooperation scheme enables GWs within radio rangeof each other to manage their associated devices, or WirelessStations (WS): (i) under low traffic conditions, a GW canhand over the associated WSs to nearby GWs and temporar-ily switch off; (ii) under high traffic conditions, a GW canselectively hand over one WS at a time in order to lowerthe congestion. When GWs are “off”, they no longer havewired/wireless connectivity and only run a low-cost, low-power radio interface, e.g., a IEEE 802.15.4 card, that canbe used as wake-on WLAN interface [4].II. LOAD ESTIMATION AND THE ASSOCIATIONMANAGEMENT PROTOCOLThe implementation of the energy-efficient cooperationscheme for federated GWs consists of three main steps.The first one is the estimation of the current load of thewireless channel and of its saturation throughput, throughpassive traffic measurements as detailed in [5]. By com-paring the current load to the saturation throughput S, aGW classifies its status as either Light, Regular, or Heavy.In the Light status, traffic likely comes from backgroundcommunications to/from the WSs, prompting the GW totry and relocate them, switch itself off and save energy.The Heavy status, instead, characterizes an overloaded BSS,where some WSs should associate to other BSSs to benefitfrom load balancing. In the Heavy status a GW tries torelocate one WS at a time, starting from the one havingthe lowest bit rate, until the Regular status is reached. AGW in Regular status is too busy to switch itself off whileit does not need to be relieved of some of its WSs. It might,however, accommodate relocated WSs within its BSS.Passive traffic measurements account for the second steptoo, in which a GW trying to relocate one or more of itsWS compiles a traffic profile of each WS, detailing thethroughput of its active (downlink/uplink) traffic flows.The third and final step amounts to an inter-GW commu-nication in which a requesting GW, aiming at relocating oneor more of its WSs, sends a handover request, along withthe traffic profiles of relocatable WSs, to candidate GWs.A computation of the projected load (current estimated loadand expected load from the incoming WS profile), and itscomparison against the estimated saturation throughput S,allow a candidate GW to assess its suitability to providehelp to others (i.e., if the additional WS does not plungethe GW in Heavy status). Responses returned by candidateGWs let the requesting GW identify a feasible relocationstrategy. Among the feasible solutions that allow a GW torelocate its WSs, the allocation maximizing the average datarate of the WSs is selected. For a Heavy initiated request,if no viable relocation is found the requesting GW needs towake up a neighboring “off” GW and repeat the requestingprocedure. Further details about the protocol can be foundin [5].III. TESTBED ARCHITECTUREAs shown in Figure 1, the testbed is a federated networkcomposed by three GWs, six WSs, a web server and a router.Each GW acts as AP of a 802.11g network operating on adifferent channel in the 2.4 GHz band, and secured withWPA-PSK, which is part of the 802.11i standard and it iswidely adopted in residential networks. Every WS knows allaccess keys in the federated network, hence it can associateROUTERGW1 GW2GW3webserverFigure 1. Testbed architecture.to any GW. We included four laptops and two smart-phonesas WSs, so as to create the heterogeneity of a real-liferesidential environment.When “on”, GWs notify the web server about the WSsassociated to them, along with the WSs traffic profile. Theweb server has two functions: (i) it provides contents toWSs and (ii) it graphically shows the testbed status overtime in a web page. Specifically, such testbed monitoringinterface shows the actual association of the WSs, the GWscurrent load (L), the aggregated throughput, the saturationthroughput (S), and L/S, which is used to determine theGWs status. On top it displays the total energy consumptionwithin the federation and, when the protocol is started, thetotal energy saving with respect to the case where all GWsare always “on”. The layout of the monitoring interface isgiven in Figure 2.A. Hardware and software descriptionGWs feature an Alix PC Engines motherboard, equippedwith an AMD Geode 500 MHz processor, one IEEE 802.11b/g compliant Wistron DCMA-82 Atheros wireless card andone omnidirectional antenna with a gain of 5 dBi. Each Alixruns OpenWrt Backfire, a Linux distribution for embeddeddevices, while the WSs can run any operating system.Figure 2. Testbed monitoring interface.The passive traffic measurements needed by the protocolare implemented on the GW within the mac80211 module ofthe Linux wireless driver compact-wireless 2011-21-01 [6].Note that, since we modified only the mac80211 module andnot the GW hardware, such measurements work on any de-vice. Specifically, at the BSS level we keep track of: averagesize of the packet payload, average SNR, average data rate,average packet error rate (PER), number of associated WSs.For each WS, at the MAC layer we measure: all throughputcontributions (elastic/inelastic, downlink/uplink traffic) withthe corresponding number of handled packets, the averagepacket payload, the average data rate, the average SNR. ThePER is computed as the estimated fraction of the erroneouslyreceived/transmitted packets. For received packets we countthe CRC errors (at the PHY and MAC layer), while fortransmitted packets we count all unsuccessful transmissionattempts at the physical layer. This results in a worst casePER estimation, as collisions are also included in the count 1.All measurements are made available to the application bythe mac80211 module every 2 s.B. Testbed scenarioAmong all realistic traffic patterns, we consider threekinds of traffic flows: mice TCP, elephant TCP and UDP.A mice TCP flow represents a whispering WS, e.g., a userbrowsing the web without any kind of background traffic.We emulate whispering stations by implementing the trafficmodel for mobile web browsing proposed in [7], wherethe mean web page dimension is 4 KB and the meanreading time (interval time between two consecutive webpage requests) is 15 s. An elephant TCP flow consists ofa bulk HTTP download of a content locally stored at theweb server. Finally, we introduce UDP flows with a bit ratesimilar to widely used audio/video peer-to-peer real-timeapplications, like Skype. All flows are established betweenWSs and the web server.Initially we consider two WSs associated to each GW.Then, we show the behavior and performance of the offloadprocedure in the following cases: (1) one or more GWs arein Light state and try to get rid of their WSs in order toswitch off; (2) one or more GWs are in Heavy state and tryto relocate their WSs without waking up additional GWs,(3) one or more GWs are in Heavy state and a GW in “off”state has to be woken up to accommodate for the relocation.During the demo users will be allowed to interact with oneclient, either a mobile phone or a notebook, adding trafficto the BSS to test the reaction of our scheme, e.g., a usercould surf the web or start a bulk TCP-based transfer. Thesystem behavior will be shown by the testbed monitoringinterface (see Figure 2).1Collisions cannot be discriminated from errors due to harsh channelconditions without changing the WS software or the 802.11 protocol.C. Energy consumption modelWe consider that the power consumption of an Alixequipped with an 802.11g radio interface is equal to P i =3.68 W in idle mode [8]. Depending on the data rate, theconsumption in transmission mode is Pt ∈ [0.24, 0.44] Wand in receive mode is Pr ∈ [0.27, 0.38] W [8]. Theconsumption of the low-cost, low-power interface (assumedto be an 802.15.4 radio) is ps=186 µW in sleep mode andpa =165 mW in receive/transmit mode [9]. As proposedin [8], we take into account the fraction of energy consumedby each packet crossing the protocol stack, distinguishingbetween transmission, Pxt = γxtRt, and reception, Pxr =γxrRr. The parameter Rt (Rr) is the packet transmission(reception) rate, while γxt and γxr are hardware-dependentfactors. Thus, we compute the energy consumed by a GWin an observation period T as follows:T · [ton (Piton,i + (Pr + Pxr)ton,r + (Pt + Pxt)ton,t + ps)+toffpa]where ton (toff ) is the time fraction during which the GWis “on” (“off”), and ton,i, ton,r and ton,t are, respectively,the idle, receive and transmit time fraction during the “on”period.D. The handover problemDespite the broad spectrum of schemes and protocolsaimed at providing seamless or faster handover in 802.11networks, none of them has reached wide adoption. Solu-tions based on Mobile IP are complex because they relyon many dependencies, requiring both new hardware andnew software to be deployed. This would imply additionalcosts for both hardware vendors and network operators, withno practical business models that justify these additionalexpenses [10]. The Inter Access Point Protocol (IAPP), stan-dardized as 802.11f [11], cannot be used to provide fasterre-authentication with 802.11i-based security standards, asthe latter does not allow security context transfer betweenGWs. 802.11i provides some alternatives to reduce hando-ver delays, namely Pairwise Master Key Caching (PMKCaching) and pre-authentication over the distribution system.The main problem of these schemes is that they are WSinitiated, which makes them unsuitable for a gateway-centricsolution like the one we propose. The recent 802.11v [12]IEEE standard includes a BSS transition management thatenables a GW to request WSs to handover to another GW, orto indicate a set of preferred GWs to a WS, based on networkload balancing needs. However, the GWs must be part ofthe same Extended Service Set (ESS) and the WSs shouldsupport this scheme. In conclusion, nowadays there is nointer-domain network-managed seamless handover solutionable to be deployed without changing both GWs and WSs,and without requiring new network element such as thehome agent of Mobile IP. As a result, current GWs andWSs come with no fast handover technology ready to use.Thus, since we want to propose an easily deployable andadoptable solution, we relax the need of a seamless handoverand handle the handover as follows. For each WS to berelocated, the selected candidate GW inserts the WS’s MACaddress in its white list, while all other GWs (including theoffloading one) insert the WS’s address in their black list.This triggers the handover of the WS: the network managerof the WS will automatically scan for GWs and it will haveno choice but to associate to the selected candidate GW.We recall that the WS network manager knows all networkparameters necessary to associate to any GWs within thefederated network. In the demo, we show that a handovertakes a couple of seconds, thus causing a very short servicedisruption for the users; however, interrupted flows must berestarted at the application level. Future work will focus onthe design and the implementation of a seamless handovertechnique and will specifically address this issue.IV. CONCLUSION AND FUTURE WORKOur testbed shows a practical approach for real networksaiming at providing considerable energy savings without(i) significantly affecting user experience nor (ii) requiringchanges in the WS hardware or software. Savings can beextended to ISPs by adopting DSLAM line aggregation asshown in [13]. Furthermore, our framework provides guide-lines for the design of the GW hardware so as to benefit frompotential energy savings. Our future work will focus on howto achieve a seamless handover in heterogeneous networksand on how to account for the presence of interference inthe capacity estimation.REFERENCES[1] Global e-Sustainability Initiative (GeSI), http://www.theclimategroup.org/assets/resources/publications/Smart2020Report.pdf.[2] R. Bolla, R. Bruschi, K. Christensen, F. Cucchietti, F. Davoli, S. Singh, “Thepotential impact of green technologies in next generation wireline networks - isthere room for energy savings optimization?,” IEEE Communications, vol. 49,no. 8, 2011.[3] EU FP7 FIGARO project, http://www.ict-figaro.eu/.[4] N. Mishra, K. Chebrolu, B. Rama, A. Patha, “Wake-on-WLAN,” InternationalConference on World Wide Web (WWW), Edinburgh, Scotland, May 2006.[5] C. Rossi, C. Casetti, C.-F. Chiasserini, “Energy-efficient wireless resourcesharing for federated residential networks,” IEEE WoWMoM, San Francisco, CA,June 2012.[6] Wireless driver, http://linuxwireless.org/[7] G.-F. Zhao, Q. Shan, S. Xiao, C. Xu, “Modelling web browsing on mobileInternet,” IEEE Communications Letters, vol. 15, no. 10, 2011.[8] A. Garcia-Saavedra, P. Serrano, A. Banchs, G.Bianchi, “Energy ConsumptionAnatomy of 802.11 Devices and its Implication on Modelling and Desing,” Co-NEXT’12, Nice, France, Dec. 2012.[9] http://www.libelium.com/support/waspmote[10] Mobile IP Deployment Difficulties http://www.ietf.org/proceedings/83/slides/slides-83-mobopts-0[11] IAPP http://gredes.ifto.edu.br/wp-content/uploads/802.11F-20031.pdf[12] 80211v http://standards.ieee.org/findstds/standard/802.11v-2011.html[13] E. Goma, M. Canini, A. Lopez Toledo, N. Laoutaris, D. Kostic, P. Rodriguez,R. Stanojevic, Pablo Yague Valentin, “Insomnia in the access or how to curbaccess network related energy consumption,” ACM SIGCOMM, Toronto, Canada,Aug. 2011.

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Energy-efficient Wi-Fi Gateways for Federated Residential Networks

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