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sections etat de l'art, description de l'appli et conclusion

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@ -51,11 +51,6 @@ K.~{Wang}, Y.~{Wang}, Y.~{Sun}, S.~{Guo}, and J.~{Wu}, ``{Green Industrial
Internet of Things Architecture: An Energy-Efficient Perspective},''
\emph{IEEE Communications Magazine}, vol.~54, no.~12, pp. 48--54, 2016.
\bibitem{Samie2016}
F.~Samie, L.~Bauer, and J.~Henkel, ``Iot technologies for embedded computing: A
survey,'' in \emph{IEEE/ACM/IFIP International Conference on
Hardware/Software Codesign and System Synthesis (CODES)}, 2016.
\bibitem{Ejaz2017}
W.~Ejaz, M.~Naeem, A.~Shahid, A.~Anpalagan, and M.~Jo, ``Efficient energy
management for the internet of things in smart cities,'' \emph{IEEE
@ -72,11 +67,40 @@ F.~Tao, Y.~Wang, Y.~Zuo, H.~Yang, and M.~Zhang, ``{Internet of Things in
product life-cycle energy management},'' \emph{Journal of Industrial
Information Integration}, vol.~1, pp. 26 -- 39, 2016.
\bibitem{jalali_fog_2016}
\BIBentryALTinterwordspacing
F.~Jalali, K.~Hinton, R.~Ayre, T.~Alpcan, and R.~S. Tucker,
``\BIBforeignlanguage{en}{Fog {Computing} {May} {Help} to {Save} {Energy} in
{Cloud} {Computing}},'' \emph{\BIBforeignlanguage{en}{IEEE Journal on
Selected Areas in Communications}}, vol.~34, no.~5, pp. 1728--1739, May 2016.
[Online]. Available: \url{http://ieeexplore.ieee.org/document/7439752/}
\BIBentrySTDinterwordspacing
\bibitem{Sarkar2018}
S.~{Sarkar}, S.~{Chatterjee}, and S.~{Misra}, ``{Assessment of the Suitability
of Fog Computing in the Context of Internet of Things},'' \emph{IEEE
Transactions on Cloud Computing}, vol.~6, no.~1, pp. 46--59, 2018.
\bibitem{Samie2016}
F.~Samie, L.~Bauer, and J.~Henkel, ``Iot technologies for embedded computing: A
survey,'' in \emph{IEEE/ACM/IFIP International Conference on
Hardware/Software Codesign and System Synthesis (CODES)}, 2016.
\bibitem{Gray2015}
C.~{Gray}, R.~{Ayre}, K.~{Hinton}, and R.~S. {Tucker}, ``{Power consumption of
IoT access network technologies},'' in \emph{IEEE International Conference on
Communication Workshop (ICCW)}, 2015, pp. 2818--2823.
\bibitem{Nest}
Google, ``{Nest Learning Thermostat -- Spec Sheet},''
\url{https://nest.com/-downloads/press/documents/nest-thermostat-fact-sheet_2017.pdf},
2017.
\bibitem{ns3-energywifi}
H.~Wu, S.~Nabar, and R.~Poovendran, ``{An Energy Framework for the Network
Simulator 3 (NS-3)},'' in \emph{International ICST Conference on Simulation
Tools and Techniques (SIMUTools)}, 2011, pp. 222--230.
\bibitem{Andres2017}
P.~{Andres-Maldonado}, P.~{Ameigeiras}, J.~{Prados-Garzon}, J.~J.
{Ramos-Munoz}, and J.~M. {Lopez-Soler}, ``{Optimized LTE data transmission
@ -89,23 +113,20 @@ B.~{Martinez}, M.~{Montón}, I.~{Vilajosana}, and J.~D. {Prades}, ``{The Power
of Models: Modeling Power Consumption for IoT Devices},'' \emph{IEEE Sensors
Journal}, vol.~15, no.~10, pp. 5777--5789, 2015.
\bibitem{ns3-energywifi}
H.~Wu, S.~Nabar, and R.~Poovendran, ``{An Energy Framework for the Network
Simulator 3 (NS-3)},'' in \emph{International ICST Conference on Simulation
Tools and Techniques (SIMUTools)}, 2011, pp. 222--230.
\bibitem{Ehsan}
E.~{Ahvar}, A.-C. {Orgerie}, and A.~{Lebre}, ``Estimating energy consumption of
cloud, fog and edge computing infrastructures,'' \emph{IEEE Transactions on
Sustainable Computing}, 2019.
\bibitem{Sarkar2018}
S.~{Sarkar}, S.~{Chatterjee}, and S.~{Misra}, ``{Assessment of the Suitability
of Fog Computing in the Context of Internet of Things},'' \emph{IEEE
Transactions on Cloud Computing}, vol.~6, no.~1, pp. 46--59, 2018.
\bibitem{jalali_fog_2016}
\bibitem{mahadevan_power_2009}
\BIBentryALTinterwordspacing
F.~Jalali, K.~Hinton, R.~Ayre, T.~Alpcan, and R.~S. Tucker,
``\BIBforeignlanguage{en}{Fog {Computing} {May} {Help} to {Save} {Energy} in
{Cloud} {Computing}},'' \emph{\BIBforeignlanguage{en}{IEEE Journal on
Selected Areas in Communications}}, vol.~34, no.~5, pp. 1728--1739, May 2016.
[Online]. Available: \url{http://ieeexplore.ieee.org/document/7439752/}
P.~Mahadevan, P.~Sharma, S.~Banerjee, and P.~Ranganathan,
``\BIBforeignlanguage{en}{A {Power} {Benchmarking} {Framework} for {Network}
{Devices}},'' in \emph{\BIBforeignlanguage{en}{{NETWORKING} 2009}}, ser.
Lecture {Notes} in {Computer} {Science}.\hskip 1em plus 0.5em minus
0.4em\relax Springer, Berlin, Heidelberg, May 2009, pp. 795--808. [Online].
Available:
\url{https://link.springer.com/chapter/10.1007/978-3-642-01399-7_62}
\BIBentrySTDinterwordspacing
\bibitem{halperin_demystifying_nodate}
@ -120,6 +141,19 @@ A.~C. Orgerie, L.~Lefèvre, I.~Guérin-Lassous, and D.~M.~L. Pacheco,
{IEEE} {International} {Symposium} on a {World} of {Wireless}, {Mobile} and
{Multimedia} {Networks}}, Jun. 2011, pp. 1--6.
\bibitem{sivaraman_profiling_2011}
V.~Sivaraman, A.~Vishwanath, Z.~Zhao, and C.~Russell, ``Profiling per-packet
and per-byte energy consumption in the {NetFPGA} {Gigabit} router,'' in
\emph{Computer {Communications} {Workshops} ({INFOCOM} {WKSHPS}), 2011 {IEEE}
{Conference} on}.\hskip 1em plus 0.5em minus 0.4em\relax IEEE, 2011, pp.
331--336.
\bibitem{Serrano2015}
P.~{Serrano}, A.~{Garcia-Saavedra}, G.~{Bianchi}, A.~{Banchs}, and
A.~{Azcorra}, ``{Per-Frame Energy Consumption in 802.11 Devices and Its
Implication on Modeling and Design},'' \emph{IEEE/ACM Transactions on
Networking}, vol.~23, no.~4, pp. 1243--1256, 2015.
\bibitem{cornea_studying_2014-1}
B.~F. Cornea, A.~C. Orgerie, and L.~Lefèvre, ``Studying the energy consumption
of data transfers in {Clouds}: the {Ecofen} approach,'' in \emph{2014 {IEEE}

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2019-ICA3PP.org
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@ -48,10 +48,10 @@ to absorb around 3% of the global energy consumption
of 9% per year \cite{ShiftProject}. This alarming growth is explained
by the fast emergence of numerous applications and new ICT
devices. These devices supply services for smart building, smart
factories and smart cities for instance. Through connected devices,
with sensors producing data, actuators interacting with their
environment and communication means -- all being parts of the Internet of
Things (IoT) -- they provide optimized decisions.
factories and smart cities for instance. Through connected sensors
producing data, actuators interacting with their environment and
communication means -- all being parts of the Internet of Things (IoT)
-- they provide optimized decisions.
This increase in number of devices implies an increase in the energy
needed to manufacture and utilize them. Yet, the overall energy bill
@ -63,8 +63,8 @@ infrastructures to store, analyze and share their data.
In February 2019, a report by Cisco stated that ``IoT connections will
represent more than half (14.6 billion) of all global connected
devices and connections (28.5 billion) by 2022" \cite{Cisco2019}. This
will represent more than 6% of global IP traffic, against 3% in
2017 \cite{Cisco2019}. This increasing impact of IoT devices on
will represent more than 6% of global IP traffic in 2022, against 3%
in 2017 \cite{Cisco2019}. This increasing impact of IoT devices on
Internet connections induces a growing weight on ICT energy
consumption.
@ -119,13 +119,14 @@ Our contributions include:
application including the energy consumption of the WiFi IoT device
and the consumption induced by its utilization on the Cloud and
telecommunication infrastructures;
- an end-to-end energy model for low-bandwidth IoT applications.
- an end-to-end energy model for low-bandwidth IoT applications
relying on WiFi devices.
The paper is organized as follows. Section \ref{sec:sota} presents the
state of the art. The low-bandwidth IoT application is characterized
in Section \ref{sec:usec}, and details on its architecture are
provided in Section \ref{sec:model}. Section \ref{sec:eval} provides
our experimental results using real measurements and
our experimental results combining real measurements and
simulations. Section \ref{sec:discuss} discusses the key findings an
the end-to-end energy model. Finally, Section \ref{sec:cl} concludes
this work and presents future work.
@ -137,75 +138,125 @@ this work and presents future work.
** Energy consumption of IoT devices
The multiplication of smart devices and smart applications pushes the
limits of Internet: IoT is now used everywhere for home automation,
smart agriculture, smart industry, e-health, smart cities, logistics,
smart grids, smart buildings,
etc. \cite{Wang2016,Ejaz2017,Minoli2017}. IoT devices are typically
used to optimize processes and the envisionned application domains
include the energy domain, like for instance the energy management of
product life-cycle \cite{Tao2016}. Yet, few studies adress the impact
of IoT itself on global energy consumption
\cite{jalali_fog_2016,li_end--end_2018} or CO2 emissions
\cite{Sarkar2018}.
smart agriculture, e-health, smart cities, logistics, smart grids,
smart buildings, etc. \cite{Wang2016,Ejaz2017,Minoli2017}. IoT devices
are typically used to optimize processes and the envisioned
application domains include the energy distribution and management. It
can for instance help the energy management of product life-cycle
\cite{Tao2016}. Yet, few studies address the impact of IoT itself on
global energy consumption \cite{jalali_fog_2016,li_end--end_2018} or
CO2 emissions \cite{Sarkar2018}.
The underlying architecture of these smart applications usually
includes sensing devices, cloud servers, user applications and
telecommunication networks. Concerning the computing part, the cloud
servers can either be located on Cloud data centers, on Fog
infrastructures located at the network edge or on home gateways
infrastructures located at the network edge, or on home gateways
\cite{Wang2016}. Various network technologies are employed by IoT
devices to communicate with their nearby gateway; either wired like
Ethernet or wireless: WiFi, Bluetooth, Near Field Communication (NFC),
ZigBee, celular network (like 3G, LTE, 4G), Low Power Wide Area
Network (LPWAN), etc. \cite{Samie2016,Gray2015}. The chosen technology
depends on the smart device characteristics and the targeted
communication performance. The Google Nest Thermostat can for instance
use WiFi, 802.15.4 and bluetooth \cite{Nest}. In this paper, we focus
on WiFi as it is broadly available and employed by IoT devices
devices to communicate with their nearby gateway; either wired
networks with Ethernet or wireless networks: WiFi, Bluetooth, Near
Field Communication (NFC), ZigBee, cellular network (like 3G, LTE, 4G),
Low Power Wide Area Network (LPWAN),
etc. \cite{Samie2016,Gray2015}. The chosen technology depends on the
smart device characteristics and the targeted communication
performance. The Google Nest Thermostat can for instance use WiFi,
802.15.4 and Bluetooth \cite{Nest}. In this paper, we focus on WiFi as
it is broadly available and employed by IoT devices
\cite{Samie2016,ns3-energywifi}.
Several works aim at reducing the energy consumption of the device
transmission \cite{Andres2017} or improving the energy efficiency of
the access network technologies \cite{Gray2015}. An extensive
literature exists on increasing the lifetime of battery-based wireless
sensor networks \cite{Wang2016}. Yet, IoT devices present more
diversity than typical wireless sensors in terms of hardware
characteristics, communication means and data production patterns.
Smart industry \cite{Wang2016} : Archi with sensing devices, cloud
server, user applications and networks
Based on real measurements, previous studies have proposed energy
models for IoT devices. Yet, these models are specific to a given kind
of IoT device or a given transmission technology.
Martinez et al. provide energy consumption measurements for wireless
sensor networks using SIGFOX transmissions and employed for
smart-parking systems \cite{Martinez2015}. Wu et al. implement an
energy model for WiFi devices in the well-known ns3 network simulator
\cite{ns3-energywifi}.
IoT archi : devices, gateways, fog and clouds \cite{Samie2016}
Smart cities \cite{Ejaz2017}
Smart building \cite{Minoli2017}
home automation, smart agriculture, eHealth, logistics, smart grids
product life-cycle energy management \cite{Tao2016}
focusing on access network technologies \cite{Gray2015},
improving device transmission \cite{Andres2017}
modeling the energy consumption of WSN devices \cite{Martinez2015} or
the WiFi transmission \cite{ns3-energywifi}
on organizing wireless sensor communications to increase the network
lifetime \cite{Wang2016}
CO2 impact of IoT and fog computing architectures vs Cloud
\cite{Sarkar2018}
Fog archi to use more renewable energy \cite{li_end--end_2018} Or
reduce communication costs \cite{jalali_fog_2016}
** Energy consumption of network and cloud infrastructures
net models
server models + VM sharing
IoT architecture rely on telecommunication networks and Cloud
infrastructures to provide services. The data produced by IoT devices
are stored and exploited by servers located either in Cloud data
centers or Fog edge sites. While studies exist on the energy
consumption of network and cloud infrastructures in general
\cite{Ehsan}, they do not consider the specific case of IoT devices.
To the best of our knowledge, no study estimates the direct impact of
IoT applications on the energy consumption of these infrastructures.
Most work focusing on energy consumption, Cloud architecture and IoT
applications tries to answer the question: where to locate data
processing in order to save energy
\cite{jalali_fog_2016}, to reduce the CO2 impact \cite{Sarkar2018}, or
to optimize renewable energy consumption \cite{li_end--end_2018}.
In both cases, the network and cloud infrastructures, attributing the
energy consumption to a given user or application is a challenging
task. The complexity comes from the shared nature of these
infrastructures: a given Ethernet port in the core of the network
processes many packets coming from a high number of sources
\cite{jalali_fog_2016}. Moreover, the employed equipment is not power
proportional: servers and routers consume consequent amounts of
energy while being idle
\cite{mahadevan_power_2009,li_end--end_2018}.
The power consumed by a device is divided into two parts: a dynamic
part that varies with traffic or amount of computation to process, and
a static part that is constant and dissipated even while being idle
\cite{Ehsan}. This static part implies that a consequent energy cost
of running an application on a server is due to the device being
simply powered on. Consequently, sharing these static energy costs
among all the concerned users requires an end-to-end model
\cite{li_end--end_2018}.
In this paper, we focus on IoT devices using WiFi transmission and
generating low data volumes. Our model, extracted from real
measurements and simulations, can be adapted to other kinds of devices
and transmission technologies.
* Characterization of low-bandwidth IoT applications
#+LaTeX: \label{sec:usec}
In this section, we detail the characteristics of the considered IoT
application. While the derived model is more generic, we focus on a
given application to obtain a precise use-case with accurate power
consumption measurements.
The Google Nest Thermostat relies on five sensors: temperature,
humidity, near-field activity, far-field activity and ambient light
\cite{Nest}. Periodical measurements, sent through wireless
communications on the Internet, are stored on Google data centers and
processed to learn the home inhabitants habits. The learned behavior
is employed to automatically adjust the home temperature managed by
heating and cooling systems.
** Application Characteristic
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.6\linewidth]{./plots/home.png}
\caption{Overview of IoT devices.}
\label{fig:IoTdev}
\end{figure}
#+END_EXPORT
Each IoT device senses periodically its environment. Then, it sends
the produced data through WiFi (in our context) to its gateway or
Access Point (AP). The AP is in charge of transmitting the data to the
cloud using the Internet. Figure \ref{fig:IoTdev} illustrates this
architecture. Several IoT devices can share the same AP in a
home. We consider low-bandwidth applications where devices produces
several network packets during each sensing period. The transmitting
frequency can vary from one to several packet sent per minute
\cite{Cisco2019}.
#+BEGIN_COMMENT
The IoT part is composed of an Access Point (AP), connected to several sensors using WIFI. In the
@ -220,19 +271,17 @@ server models + VM sharing
#+END_COMMENT
We consider that the link between the AP and the Cloud is composed of
several network switches and routers using Ethernet as shown in Figure
\ref{fig:parts}. The number of routers on the path depends on the
location of the server, either in a Cloud data center or in a Fog site
at the edge of the network.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.6\linewidth]{./plots/home.png}
\caption{Overview of IoT devices.}
\label{fig:IoTdev}
\end{figure}
#+END_EXPORT
** Cloud Infrastructure
We assume that the server hosting the application data for the users
belongs to a shared cloud facility with classical service level
agreement (SLA). The facility provides redundant storage and computing
means as virtual machines (VMs). A server can host several VMs at the
same time.
#+BEGIN_EXPORT latex
\begin{figure}
@ -243,28 +292,40 @@ server models + VM sharing
\end{figure}
#+END_EXPORT
In the following, we describe the experimental setup, the results and
the end-to-end model. For all these steps, we decompose the overall
IoT architecture into three parts: the IoT device part, the networking
part and the cloud part, as displayed on Figure \ref{fig:parts}.
* Experimental setup
\hl{Ajouter \% de bande passante utilisé par les applis low-rate}
#+LaTeX: \label{sec:model}
Our system model is divided in three parts. First, the IoT and the network parts are modeled through
simulations. Then, the Cloud part is modeled using real servers connected to wattmeters. In this way,
it is possible to evaluate the end-to-end energy consumption of the system.
#+Latex: \label{sec:model}
In this section, we describe the experimental setup employed to
acquire energy measurements for each of the three parts of our
system model. The IoT and the network parts are modeled
through simulations. The Cloud part is modeled using real
servers connected to wattmeters. In this way, it is possible to
evaluate the end-to-end energy consumption of the system.
** IoT Part
In the first place, the IoT part is composed of several sensors connected to an Access Point (AP)
which form a cell. This cell is evaluated using the ns-3 network simulator. Consequently, we setup
which form a cell. This cell is studied using the ns3 network
simulator. In the experimental scenario, we setup
between 5 and 15 sensors connected to the AP using WiFi 5GHz 802.11n. The node are placed
randomly in a rectangle of $400m^2$ around the AP which corresponds to a typical real use case. All
the cell nodes are setup with the default WIFI energy model provided by ns-3. The different
energy values used by the energy model are provided on Table \ref{tab:wifi-energy}. These energy
were extracted from previous work\cite{halperin_demystifying_nodate,li_end--end_2018} on
802.11n. Besides, we suppose that the energy source of each nodes are unlimited and thus each of
them can communicate until the end of all the simulations.
randomly in a rectangle of $400m^2$ around the AP which corresponds
to a typical use case for a home environment. All
the cell nodes employ the default WIFI energy model provided by ns3. The different
energy values used by the energy model are provided on Table \ref{tab:wifi-energy}. These parameters
were extracted from previous work\cite{halperin_demystifying_nodate,li_end--end_2018} On
IEEE 802.11n. Besides, we suppose that the energy source of each
nodes is not limited during the experiments. Thus each node
can communicate until the end of all the simulations.
As a scenario, sensors send 192 bits packets to the AP composed of: \textbf{1)} A 128 bits
sensors id \textbf{2)} A 32 bits integer representing the temperature \textbf{3)} An integer
timestamp representing the temperature sensing time to store them as time series. The data are
transmitted immediately at each sensing interval $I$ varied from 1s to 60s. Finally, the AP is in
timestamp representing the temperature sensing date. They are stored as time series. The data are
transmitted immediately at each sensing interval $I$ that we vary from 1s to 60s. Finally, the AP is in
charge of relaying data to the cloud via the network part.
#+BEGIN_EXPORT latex
@ -295,96 +356,109 @@ server models + VM sharing
** Network Part
The network part represents the a network section starting from the AP to the Cloud excluding the
server. It is also model into ns-3. We consider the server to be 9 hops away from the AP with a
typical round-trip latency of 100ms from the AP to the server. Each node from the AP to the Cloud
is assume to be network switches with static and dynamic network energy consumption. The first 8
hop are edge switches and the last one is consider to be a core switch as mention in
server. It is also modeled into ns3. We consider the server to be 9 hops away from the AP with a
typical round-trip latency of 100ms from the AP to the server
\cite{li_end--end_2018}. Each node from the AP to the Cloud
is a network switch with static and dynamic network energy consumption. The first 8
hops are edge switches and the last one is consider to be a core router as mentioned in
\cite{jalali_fog_2016}. ECOFEN \cite{orgerie_ecofen:_2011} is used to model the energy
consumption of the network part. ECOFEN is a ns-3 network energy module dedicated to wired
network. It is based on an energy-per-bit model including static energy consumption by assuming a
linear relation between the amount of data sent to the network interface and its power
consumption. The different energy values used to instantiate the ECOFEN energy model for the
consumption of the network part. ECOFEN is an ns3 network energy module dedicated to wired
networks. It is based on an energy-per-bit and energy-per-packet
model for the dynamic energy consumption
\cite{sivaraman_profiling_2011,Serrano2015}, and it includes also a static energy consumption.
The different values used to instantiate the ECOFEN energy model for the
network part are shown in Table \ref{tab:net-energy} and come from previous work
\cite{cornea_studying_2014-1}.
** Cloud Part
Finally, to measure the energy consumed by the server, we used real server from the large-scale
test-beds Grid5000 (G5K). In fact, G5K has a cluster called Nova composed of several nodes which
are connected to watt-meters. In this way, we can benefit from real energy measurements. The
server used in the experiment include an Intel Xeon E5-2620 processor with 64 GB of RAM and 600GB
Finally, to measure the energy consumed by the Cloud part, we use a real server from the large-scale
test-bed Grid'5000. Grid'5000 provides clusters composed of several nodes which
are connected to wattmeters. The wattmeters provide 50
instantaneous power measurements per second and per server. This
way, we can benefit from real energy measurements. The server used
in the experiment embeds two Intel Xeon E5-2620 v4 processors with
64 GB of RAM and 600GB
of disk space on a Linux based operating system. This server is configured to use KVM as
virtualization mechanism. We deploy a classical Linux x86_64 distribution on the Virtual Machine
(VM) along with a MySQL database. We used different amount of allocated memory for the VM namely
1024MB/2048MB/4096MB to highlight its effects on the server energy consumption.
The sensors requests are simulated using another server. This server is in charge to send hundred
of requests to the VM in order to fill the database. Consequently, it is easy to vary the
different requests characteristics namely: \textbf{1)} The number request, to virtually
add/remove sensors \textbf{2)} The requests interval. Figure \ref{fig:g5kExp} present this simulation
setup.
virtualization mechanism. We deploy a classical Debian x86_64 distribution on the Virtual Machine
(VM) along with a MySQL database. We use different amounts of allocated memory for the VM namely
1024MB/2048MB/4096MB to highlight its effects on the server energy
consumption. The server only hosts this VM in order to easily isolate its
power consumption.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.4\linewidth]{./plots/g5k-xp.png}
\caption{Grid5000 experimentation setup.}
\includegraphics[width=0.5\linewidth]{./plots/g5k-xp.png}
\caption{Grid'5000 experimental setup.}
\label{fig:g5kExp}
\end{figure}
#+END_EXPORT
The data sent by the IoT devices are simulated using another
server from the same cluster. This server is in charge of sending
the data packets to the VM hosting the application in order to fill
its database. In the following, each data packet coming from an IoT
device and addressed to the application VM is called a request. Consequently, it is easy to vary the
different application characteristics namely: \textbf{1)} The number
of requests, to virtually
add/remove sensors \textbf{2)} The requests interval, to study the
impact of the transmitting frequency. Figure \ref{fig:g5kExp} presents this simulation
setup.
* Evaluation
#+LaTeX: \label{sec:eval}
** IoT/Network Consumption
In a first place, we start by studying the impact of the sensors position on their energy
consumption. To this end, we run several simulations in ns-3 with different sensors position. The
results provided by Table \ref{tab:sensorsSendIntervalEffects} show that sensors position have a very low impact
In this section, we analyze the experimental results.
** IoT and Network Power Consumption
In a first place, we start by studying the impact of the sensors'
transmission frequency on their energy
consumption. To this end, we run several simulations in ns3 with different frequencies. The
results provided by Table \ref{tab:sensorsSendIntervalEffects} show
that the transmission frequency has a very low impact
on the energy consumption and on the application delay. It has an impact of course, but it is very
limited. This due to the fact that in such a scenario with very small number of communications
spread over the time, sensors don't have to contend for accessing to the Wifi channel.
\hl{TODO: définir le 'application delay' et le nombre de capteurs utilisés pour l'expérience de la table}
#+BEGIN_EXPORT latex
% Please add the following required packages to your document preamble:
% \usepackage{booktabs}
\begin{table*}[]
\centering
\caption{Sensors send interval effects}
\caption{Sensors transmission interval effects}
\label{tab:sensorsSendIntervalEffects}
\begin{tabular}{@{}lrrrrr@{}}
\toprule
Sensors Send Interval & 10s & 30s & 50s & 70s & 90s \\ \midrule
Sensors Power Consumption & 13.517\hl{94}W & 13.517\hl{67}W & 13.51767W & 13.51767W & 13.517\hl{61}W \\
Network Power Consumption & 10.441\hl{78}W & 10.441\hl{67}W & 10.44161W & 10.44161W & 10.441\hl{61}W \\
Average Appplication Delay & 17.81360s & 5.91265s & 3.53509s & 2.55086s & 1.93848s \\ \bottomrule
Average Application Delay & 17.81360s & 5.91265s & 3.53509s & 2.55086s & 1.93848s \\ \bottomrule
\end{tabular}
\end{table*}
#+END_EXPORT
Previous work \cite{li_end--end_2018} on similar scenario shows that increasing application
accuracy impact strongly the energy consumption in the context of data stream analysis. However,
in our case, application accuracy is driven by the sensing interval and thus, the transmit
frequency of the sensors. Therefore, we varied the transmission interval of the sensors from 1s
to 60s. Some of these results are proposed on Table \ref{tab:sensorsSendIntervalEffects}. In
case of small and sporadic network traffic, these results show that with a reasonable
transmission interval the energy consumption of the IoT/Network if almost not affected by the
Previous work \cite{li_end--end_2018} on a similar scenario shows that increasing application
accuracy impacts strongly the energy consumption in the context of data stream analysis. However,
in our case, application accuracy is driven by the sensing interval and thus, the transmission
frequency of the sensors.
In our case with small and sporadic network traffic, these results show that with a reasonable
transmission interval, the energy consumption of the IoT and the
network parts are almost not affected by the
variation of this transmission interval. In fact, transmitted data are not large enough to
leverage the energy consumed by the network.
The number of sensors is a dominant factor that leverage the energy consumption of the
IoT/Network part. Therefore, we varied the number of sensors in the Wifi cell to analyze its
impact. The Figure \ref{fig:sensorsNumber} represents the energy consumed by each simulated part
according the the number of sensors. It is clear that the energy consumed by the network is the
dominant part. However, since the number of sensors is increasing the energy consumed by the
network will become negligible face to the energy consume by the sensors. In fact, deploying new
We then vary the number of sensors in the Wifi cell.
The Figure \ref{fig:sensorsNumber} represents the energy consumed by each simulated part
according to the number of sensors. It is clear that the energy consumed by the network is the
dominant part. However, if the number of sensors is increasing, the energy consumed by the
network can become smaller than the sensors part. In fact, deploying new
sensors in the cell do not introduce much network load. To this end, sensors energy consumption
is dominant.
can become dominant.
#+BEGIN_EXPORT latex
\begin{figure}
@ -397,39 +471,51 @@ server models + VM sharing
** Cloud Energy Consumption
In this End-To-End energy consumption study, cloud account for a huge part of the overall energy
In this end-to-end energy consumption study, cloud accounts for a huge part of the overall energy
consumption. According a report \cite{shehabi_united_2016-1} on United States data center energy
usage, the average Power Usage Effectiveness (PUE) of an hyper-scale data center is 1.2. Thus, in
our analysis, all energy measurement on cloud server will account for this PUE.
our analysis, all energy measurement on cloud server will account
for this PUE. It means that the power consumption of the server is multiplied by
the PUE to include the external energy costs like server cooling
and data center facilities \cite{Ehsan}.
In a first place, we analyze the impact of the VM allocated memory on the server energy
consumption. Figure \ref{fig:vmSize} depict the server energy consumption according to the VM
allocated memory for 20 sensors sending data every 10s. Note that horizontal red line represent
Firstly, we analyze the impact of the VM allocated memory on the server energy
consumption. Figure \ref{fig:vmSize} depicts the server energy consumption according to the VM
allocated memory for 20 sensors sending data every 10s. Note that
the horizontal red line represents
the average energy consumption for the considered sample of energy values. We can see that at
each sensing interval, server face to peaks of energy consumption. However, VM allocated memory
do not influence energy consumption. In fact, simple database requests do not need any particular
huge memory access and processing time. Thus, remaining experiments are based on VM with 1024MB
each transmission interval, the server faces spikes of energy
consumption. However, the amount of allocated memory to the VM
does not significantly influence the server energy consumption. In
fact, simple database requests do not need any particular
heavy memory accesses and processing time. Thus, remaining experiments are based on VM with 1024MB
of allocated memory.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[width=0.8\linewidth]{./plots/vmSize-cloud.png}
\caption{VM size impact on the server energy consumption using 20 sensors sending data every 10s}
\caption{Server power consumption using 20 sensors sending data every 10s for various VM memory sizes}
\label{fig:vmSize}
\end{figure}
#+END_EXPORT
Next, we study the effects of increasing the number of sensors on the server energy consumption.
Figure \ref{fig:sensorsNumber-server} present the results of the average server energy
consumption when varying the number of sensors from 20 to 500 while Figure
\ref{fig:sensorsNumber-WPS} present the average server energy cost per sensors according to the
Figure \ref{fig:sensorsNumber-server} presents the results of the average server energy
consumption when varying the number of sensors from 20 to 500, while Figure
\ref{fig:sensorsNumber-WPS} presents the average server energy cost per sensor according to the
number of sensors. These results show a clear linear relation between the number of sensors and
the server energy consumption. Moreover, we can see that the more sensors we have per server, the
more energy we can save. In fact, since the idle server energy consumption is high, it is more
the server energy consumption. Moreover, we can see that the more sensors we have per VM, the
more energy we can save. In fact, since the server's idle power
consumption is high (around 97 Watts), it is more
energy efficient to maximize the number of sensors per server. As shown on Figure
\ref{fig:sensorsNumber-WPS}, a significant amount of energy can be save when passing from 20 to
300 sensors per server.
300 sensors per VM. Note that these measurements are the row
measurements taken from the wattmeters: they do not include the PUE
and are not shared among all the VMs that could be hosted on this
server.
\hl{Figure 5 n'inclut pas le PUE non? le Pidle est bien à 97 Watts environ?}
#+BEGIN_EXPORT latex
\begin{figure}
@ -448,32 +534,35 @@ server models + VM sharing
\end{figure}
#+END_EXPORT
A last parameter can leverage server energy consumption namely sensors send interval. In addition
to increasing the application accuracy, sensors send interval increase network traffic and
database accesses. Figure \ref{fig:sensorsFrequency} present the impact on the server energy
consumption of changing the send interval of 50 sensors to 1s, 10s and 30s. We can see that, the
lower sensors send interval is, the more server energy consumption peaks occurs. Therefore, it
leads to an increase of the server energy consumption.
A last parameter can leverage server energy consumption, namely
sensors transmission interval. In addition
to increasing the application accuracy, sensors transmission frequency increases network traffic and
database accesses. Figure \ref{fig:sensorsFrequency} presents the impact on the server energy
consumption when changing the transmission interval of 50 sensors
to 1s, 10s and 30s. We can see that, the lower sensors transmission
interval is, the more server energy consumption peaks
occur. Therefore, it leads to an increase of the server energy consumption.
#+BEGIN_EXPORT latex
\begin{figure}
\centering
\includegraphics[scale=0.5]{plots/sendInterval-cloud.png}
\caption{Server energy consumption for 50 sensors sending requests at different transmission interval.}
\label{fig:sensorsFrequency}
\caption{Server energy consumption for 50 sensors sending request at different interval.}
\end{figure}
#+END_EXPORT
** End-To-End Consumption
* End-To-End Consumption Model
#+LaTeX: \label{sec:discuss}
To have an overview of the energy consume by the system, it is important to consider the
To have an overview of the energy consumed by the overall system, it is important to consider the
end-to-end energy consumption. The Figure \ref{fig:end-to-end} represents the end-to-end system
energy consumption while varying the number of sensors. It is important to see that, for
small-scale systems, the server energy consumption is dominant face to the energy consumed by the
energy consumption while varying the number of sensors. Note that, for
small-scale systems, the server energy consumption is dominant compared to the energy consumed by the
sensors. However, since we are using a single server, large-scale sensors deployment lead to an
increasing consumption of energy in the IoT part. On the other side, network energy consumption
is stable regarding to the number of sensors since the system use case do not required large data
transfer. Thus, it is important to remember that, to save energy, we should maximize the number
is stable regarding the number of sensors since the system use case does not required large data
transfers. Thus, to save energy, we should maximize the number
of sensors handle by each cloud server while keeping reasonable sensors request intervals.
#+BEGIN_EXPORT latex
@ -488,12 +577,35 @@ server models + VM sharing
* Discussion
#+LaTeX: \label{sec:discuss}
* Conclusion
#+LaTeX: \label{sec:cl}
Information and Communication Technology takes a growing part in the
worldwide energy consumption. One of the root causes of this increase
lies in the multiplication of connected devices. Each object of the
Internet-of-Things often does not consume much energy by itself. Yet,
their number and the infrastructures they require to properly work
have leverage.
In this paper, we combine simulations and real
measurements to study the energy impact of IoT devices. In particular,
we analyze the energy consumption of Cloud and telecommunication
infrastructures induced by the utilization of connected devices.
Through the fine-grain analysis of a given low-bandwidth IoT device
periodically sending data to a Cloud server using WiFi,
we propose an end-to-end energy consumption model.
This model provides insights on the hidden part of the iceberg: the
impact of IoT devices on the energy consumption of Cloud and network
infrastructures. On our use-case, we show that for a given sensor, its
larger energy consumption is on the Cloud part. Consequently, with the
IoT exploding growth, it becomes necessary to improve the energy
efficiency of applications hosted on Cloud infrastructures.
Our future work includes studying other types of IoT wireless
transmission techniques and IoT applications in order to increase the
applicability of our model.
\bibliographystyle{IEEEtran}
\bibliography{references}

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@ -2489,3 +2489,28 @@ year = {2017},
howpublished = {\url{https://nest.com/-downloads/press/documents/nest-thermostat-fact-sheet_2017.pdf}},
author = {Google}
}
@INPROCEEDINGS{Hassidim2013,
author={A. {Hassidim} and D. {Raz} and M. {Segalov} and A. {Shaqed}},
booktitle={IEEE INFOCOM},
title={{Network utilization: The flow view}},
year={2013},
pages={1429-1437},
}
@ARTICLE{Ehsan,
author={E. {Ahvar} and A.-C. {Orgerie} and A. {Lebre}},
journal={IEEE Transactions on Sustainable Computing},
title={Estimating Energy Consumption of Cloud, Fog and Edge Computing Infrastructures},
year={2019},
}
@ARTICLE{Serrano2015,
author={P. {Serrano} and A. {Garcia-Saavedra} and G. {Bianchi} and A. {Banchs} and A. {Azcorra}},
journal={IEEE/ACM Transactions on Networking},
title={{Per-Frame Energy Consumption in 802.11 Devices and Its Implication on Modeling and Design}},
year={2015},
volume={23},
number={4},
pages={1243-1256},
}