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+% Intended LaTeX compiler: pdflatex
+\documentclass[conference]{IEEEtran}
+ \usepackage{hyperref}
+\usepackage{booktabs}
+\usepackage{subfigure}
+\usepackage{graphicx}
+\usepackage{xcolor}
+\usepackage{multirow}
+
+\author{\IEEEauthorblockN{Loic Guegan and
+Anne-C\'ecile Orgerie}
+\IEEEauthorblockA{Univ Rennes, Inria, CNRS, IRISA, Rennes, France\\
+Email: \{loic.guegan, anne-cecile.orgerie\}@irisa.fr}
+}
+
+
+\hypersetup{
+ pdfauthor={Anne-Cecile Orgerie},
+ pdftitle={Estimating the end-to-end energy consumption of low-bandwidth IoT applications for WiFi devices},
+ pdfkeywords={},
+ pdfsubject={},
+ pdflang={English}}
+
+
+
+\title{Estimating the end-to-end energy consumption of low-bandwidth IoT applications for WiFi devices}
+\begin{document}
+
+\maketitle
+\newcommand{\hl}[1]{\textcolor{red}{#1}}
+
+\begin{abstract}
+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, And 
+we propose an end-to-end energy consumption model for these devices. 
+\end{abstract}
+
+\begin{IEEEkeywords}
+IoT devices, energy consumption, clouds, end-to-end model
+\end{IEEEkeywords}
+
+\IEEEpeerreviewmaketitle
+
+
+\section{Introduction}
+In 2018, Information and Communication Technology (ICT) was estimated
+to absorb around 3\% of the global energy consumption~\cite{ShiftProject}. 
+This consumption is estimated to grow at a rate
+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 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
+of IoT also comprises indirect costs, as it relies on computing and 
+networking infrastructures that consume energy to enable smart
+services. Indeed, IoT devices communicate with Cloud computing
+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 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.  
+
+The energy consumption of IoT devices themselves is only the top of
+the iceberg: their use induce energy costs in communication and cloud
+infrastructures. In this paper, we estimate the overall energy
+consumption of an IoT device environment by combining simulations and
+real measurements. We focus on a given application with low bandwidth
+requirement and we evaluate its overall energy consumption: from the
+device, through telecommunication networks, and up to the Cloud data
+center hosting the application. From this analysis, we derive an
+end-to-end energy consumption model that can be used to assess the
+consumption of other IoT devices.
+
+While some IoT devices produce a lot of data, like smart vehicles for
+instance, many others generate only a small amount of data, like smart
+meters. However, the scale matters here: many small devices can end up
+producing big data volumes. As an example, according to a report
+published by Sandvine in October 2018, the Google Nest Thermostat is
+the most significant IoT device in terms of worldwide connections: it
+represents 0.16\% of all connections, ranging 55th on the list of
+connections~\cite{Sandvine2018}. As a comparison, the voice assistants
+Alexa and Siri are respectively 97th and 102nd with 0.05\% of all
+connections~\cite{Sandvine2018}. This example highlights the growing
+importance of low-bandwidth IoT applications on Internet
+infrastructures, and consequently on their energy consumption. 
+
+In this paper, we focus on IoT devices for low-bandwidth applications
+such as smart meters or smart sensors. These devices send few
+data periodically to cloud servers, either to store them or to get
+computing power and take decisions. This is a first step towards a
+comprehensive characterization of the global IoT energy
+footprint. While few studies address the energy consumption of
+high-bandwidth IoT applications~\cite{li_end--end_2018}, to the best
+of our knowledge, none of them targets low-bandwidth applications,
+despite their growing importance on the Internet infrastructures.
+
+Low-bandwidth IoT applications, such as the Nest Thermostat, often
+relies on sensors powered by batteries. For such sensors, reducing
+their energy consumption is a critical target. Yet, we argue that
+end-to-end energy models are required to estimate the overall impact
+of IoT devices, and to understand how to reduce their complete energy
+consumption. Without such models, one could optimize the consumption
+of on-battery devices at a heavier cost for cloud servers and
+networking infrastructures, resulting on an higher overall energy 
+consumption. Using end-to-end models could prevent these unwanted
+effects. 
+
+Our contributions include:
+\begin{itemize}
+\item a characterization of low-bandwidth IoT applications;
+\item an analysis of the energy consumption of a low-bandwidth IoT
+application including the energy consumption of the WiFi IoT device
+and the consumption induced by its utilization on the Cloud and
+telecommunication infrastructures;
+\item an end-to-end energy model for low-bandwidth IoT applications
+relying on WiFi devices.
+\end{itemize}
+
+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 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. 
+
+
+
+\section{Related Work}
+\label{sec:orge831050}
+\label{sec:sota}
+\subsection{Energy consumption of IoT devices}
+\label{sec:org77c2591}
+The multiplication of smart devices and smart applications pushes the
+limits of Internet: IoT is now used everywhere for home automation,
+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~\cite{Wang2016}.
+Various network technologies are employed by IoT
+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.
+
+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}. 
+
+
+\subsection{Energy consumption of network and cloud infrastructures}
+\label{sec:orga15491a}
+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. 
+
+
+
+\section{Characterization of low-bandwidth IoT applications}
+\label{sec:org1da7386}
+\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.
+
+\begin{figure}[htbp]
+  \centering
+  \includegraphics[width=0.7\linewidth]{./plots/home.png}
+  \caption{Overview of IoT devices.}
+  \label{fig:IoTdev}
+\end{figure}
+
+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}. 
+
+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.
+
+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{figure}[htbp]
+  \centering
+  \includegraphics[width=1.\linewidth]{./plots/parts2.png}
+  \caption{Overview of the IoT architecture.}
+  \label{fig:parts}
+\end{figure}
+
+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}. 
+
+
+\section{Experimental setup}
+\label{sec:orgb5f6554}
+\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. 
+
+\subsection{IoT Part}
+\label{sec:orgeb67dd0}
+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 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 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 in Table~\ref{tab:params}. 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 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{table}[htbp]
+  \centering
+  \caption{Simulations Energy Parameters}
+  \label{tab:wifi-energy}
+  \subtable[IoT part]{
+    \begin{tabular}{@{}lr@{}}
+      Parameter      & Value  \\ \midrule
+      Supply Voltage & 3.3V   \\
+      Tx             & 0.38A  \\
+      Rx             & 0.313A \\
+      Idle           & 0.273A \\ \bottomrule
+  \end{tabular}}
+  \hspace{0.3cm}
+  \subtable[Network part]{
+    \label{tab:net-energy}
+    \begin{tabular}{@{}lr@{}}
+      Parameter  & Value \\ \midrule
+      Idle       & 0.00001W           \\ 
+      Bytes (Tx/Rx)  & 3.4nJ           \\ 
+      Pkt (Tx/Rx)    & 192.0nJ         \\ \bottomrule
+    \end{tabular}
+  }
+  \label{tab:params}
+\end{table}
+
+\subsection{Network Part}
+\label{sec:orgaeb55ca}
+The network part represents the a network section starting from the AP to the Cloud excluding the
+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 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 left part of Table~\ref{tab:params} and come from previous work~\cite{cornea_studying_2014-1}.
+
+\subsection{Cloud Part}
+\label{sec:orgfc9ea54}
+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 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{figure}[htbp]
+  \centering
+  \includegraphics[width=0.7\linewidth]{./plots/g5k-xp.png}
+  \caption{Grid'5000 experimental setup.}
+  \label{fig:g5kExp}
+\end{figure}
+
+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.
+
+
+
+
+\section{Evaluation}
+\label{sec:org8201f68}
+\label{sec:eval}
+
+\subsection{IoT and Network Power Consumption}
+\label{sec:org1d05c1b}
+In this section, we analyze the experimental results.
+   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 15 sensors using
+   different transmission 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 average end-to-end 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.
+
+% Please add the following required packages to your document preamble:
+% \usepackage{booktabs}
+\begin{table*}[htbp]
+\centering
+\caption{Sensors transmission interval effects with 15 sensors}
+\label{tab:sensorsSendIntervalEffects}
+\begin{tabular}{@{}lrrrrr@{}}
+\toprule
+Transmission Interval        & 10s            & 30s            & 50s       & 70s       & 90s      \\ \midrule
+Sensor Power     & 13.517\hl{94}W & 13.517\hl{67}W & 13.51767W & 13.51767W & 13.517\hl{61}W \\
+Network Power     & 0.441\hl{88}W & 0.441\hl{77}W & 0.44171W & 0.44171W & 0.441\hl{71}W \\
+Application Delay & 0.09951s       & 0.10021s       & 0.10100s  & 0.10203s  & 0.10202s  \\ \bottomrule
+\end{tabular}
+\end{table*}
+
+
+   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.
+
+We then vary the number of sensors in the Wifi cell.
+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
+can become dominant.
+
+\begin{figure}[htbp]
+  \centering
+  \includegraphics[width=0.9\linewidth]{./plots/numberSensors-WIFINET.png}
+  \caption{Analysis of the variation of the number of sensors on the IoT/Network part energy consumption for a transmission interval of 10s.}
+  \label{fig:sensorsNumber}
+\end{figure}
+
+
+\subsection{Cloud Energy Consumption}
+\label{sec:org9daa066}
+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. 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}.
+
+\begin{figure*}[htbp]
+  \centering
+  \includegraphics[width=.7\linewidth]{./plots/vmSize-cloud.png}
+  \caption{Server power consumption multiplied by the PUE (= 1.2) using 20 sensors sending data every 10s for various VM memory sizes}
+  \label{fig:vmSize}
+\end{figure*}
+
+
+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 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.
+
+Next, we study the effects of increasing the number of sensors on the server energy consumption.
+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 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 VM. Note that these measurements are not the row
+measurements taken from the wattmeters: they include the PUE
+but they are not shared among all the VMs that could be hosted on this
+server. So, for the studied server, its static power consumption
+(also called idle consumption) is around 83.2 Watts and we consider
+a PUE of 1.2, this value is taken from~\cite{shehabi_united_2016-1}\}.
+
+\begin{figure}[htbp]
+  \centering
+    \includegraphics[width=0.7\linewidth]{./plots/sensorsNumberLine-cloud.png}
+    \caption{Average server power consumption multiplied by the PUE (= 1.2) for sensors sending data every 10s}
+    \label{fig:sensorsNumber-server}
+\end{figure}
+  
+
+\begin{figure}[htbp]
+  \centering
+    \includegraphics[width=0.7\linewidth]{./plots/WPS-cloud.png}
+    \caption{Average sensors power cost on the server hosting only our VM with PUE (= 1.2) for sensors sending data every 10s}
+    \label{fig:sensorsNumber-WPS}
+\end{figure}
+
+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{figure*}[htbp]
+  \centering
+  \includegraphics[width=0.7\linewidth]{plots/sendInterval-cloud.png}
+  \caption{Server energy consumption multiplied by the PUE (= 1.2) for 50 sensors sending requests at different transmission interval.}
+  \label{fig:sensorsFrequency}
+\end{figure*}
+
+\section{End-To-End Consumption Model}
+\label{sec:orgfd3b6ae}
+\label{sec:discuss}
+
+To have an overview of the energy consumed by the overall system, it is important to consider the
+end-to-end energy consumption.
+We detail here the model used to attribute the energy
+consumption of our application for each part of the
+architecture. For a given IoT device, we have:
+\begin{enumerate}
+\item For the IoT part, the entire consumption of the IoT device
+belongs to the system's accounted consumption.
+\item For the network part, the data packets generated by the IoT
+device travel through network switches, routers and ports that
+are shared with other traffic.
+\item For the cloud part, the VM hosting the data is shared with
+other IoT devices belonging to the same application and the
+server hosting the VM also hosts other VMs. Furthermore, the
+server belongs to a data center and takes part in the overall
+energy drawn to cool the server room.
+\end{enumerate}
+
+Concerning the IoT part, we include the entire IoT device power
+consumption. Indeed, in our targeted low-bandwidth IoT application,
+the sensor is dedicated to this application. From Table~\ref{tab:params}, one can
+derive that the static power 
+consumption of one IoT sensor is around 0.9 Watts. Its dynamic part
+depends on the transmission frequency.
+
+Concerning the sharing of the network costs, for each router, we
+consider its aggregate bandwidth (on all the ports), its average
+link utilization and the share taken by our IoT application. For a
+given network device, we compute our share of the static energy
+part as follows: 
+
+\begin{footnotesize}
+\[P_{static}^{netdevice} = \frac{P_{static}^{device} \times Bandwidth^{application}}{AggregateBandwidth^{device}
+\times LinkUtilization^{device}}\]
+\end{footnotesize}
+
+where \(P_{static}^{device}\) is the static power consumption of the
+network device (switch fabrics for instance or gateway),
+\(Bandwidth^{application }\) Is the bandwidth used by our IoT application,
+\(AggregateBandwidth^{device }\) is the overall aggregated bandwidth of the
+network device on all its ports, and \(LinkUtilization^{device}\) is the
+effective link utilization percentage. The \(Bandwidth^{application }\)
+depends on the transmission frequency in our use-case.
+The formula includes the
+link utilization in order to charge for the effective energy cost
+per trafic and not for the theoretical upper bound which is the
+link bandwidth. Indeed, using such an upper bound leads to greatly
+underestimate our energy part, since link utilization typically
+varies between 5 to 40\%~\cite{Hassidim2013,Zhang2016}.
+
+Similarly, for each network port, we take the share attributable to
+our application: the ratio of our bandwidth utilization over the
+port bandwidth multiplied by the link utilization and the overall
+static power consumption of the port. Table~\ref{tab:netbidules}
+summarizes the parameters used in our model, they are taken from~\cite{mahadevan_power_2009,Hassidim2013}. These are the parameters
+used in our formula to compute the values that we used in the
+simulations and that are presented in left part of Table~\ref{tab:params}. 
+
+
+
+\begin{table}[htbp]
+  \centering
+  \caption{Network Devices Parameters}
+  \label{tab:netbidules}
+    \begin{tabular}{l|l}
+      Device      & ~Parameters  \\ \midrule
+      \multirow{2}{*}{Gateway} & Static power = 8.3 Watts\\
+       & Bandwidth = 54Mbps\\
+       & Utilization = 10\%   \\ \hline
+      \multirow{2}{*}{Core router} & Static power = 555 Watts\\
+       & 48 ports of 1 Gbps\\
+       & Utilization = 25\% \\ \hline
+      \multirow{2}{*}{Edge switch} & Static power = 150 Watts\\
+       & 48 ports of 1 Gbps\\
+       & Utilization = 25\% \\
+      \bottomrule
+  \end{tabular}
+\end{table}
+
+
+
+For the sharing of the Cloud costs, we take into account the number
+of VMs that a server can host, the CPU utilization of a VM and the
+PUE. For a given Cloud server hosting our IoT application, we
+compute our share of the static energy part as follows: 
+
+\[P_{static}^{Cloudserver} = \frac{P_{static}^{server} \times PUE^{DataCenter}}{HostedVMs^{server}}\]
+
+Where \(P_{static}^{server}\) is the static power consumption of the
+server, \(PUE^{DataCenter}\) is the data center PUE, and
+\(HostedVMs^{server}\) is the number of VMs a server can host. This last
+parameter should be adjusted in the case of VMs with multiple
+virtual CPUs. We do not
+consider here over-commitment of Cloud servers. Yet, the dynamic
+energy part is computed with the real dynamic measurements, so it
+accounts for VM over-provisioning and resource under-utilization.
+
+In our case, the Cloud server has 14 cores, which corresponds to
+the potential hosting of 14 small VMs with one virtual CPU each,
+and each vCPU is pinned to a server core. We consider that for
+fault-tolerance purpose, the IoT application has a replication
+factor of 2, meaning that two cloud servers store its database.
+
+The Figure~\ref{fig:end-to-end} represents the end-to-end system
+energy consumption using the model described above while varying
+the number of sensors for a transmission interval of 10
+seconds. The values are extracted from the experiments presented in
+the previous section. 
+
+\begin{figure}[htbp]
+  \centering
+  \hspace{1cm}
+  \includegraphics[width=1.\linewidth]{plots/final.png}
+  \label{fig:end-to-end}
+  \caption{End-to-end network energy consumption using sensors interval of 10s}
+\end{figure}
+
+
+Note that, for small-scale systems, with WiFi IoT devices, the IoT
+sensor part is dominant in the overall energy consumption. Indeed,
+the IoT application induces a very small cost on Cloud and network
+infrastructures compared to the IoT device cost. But, our model
+assumes that a single VM is handling multiple users (up to 300
+sensors), thus being energy-efficient. Conclusions would be
+different with one VM per user in the case of no over-commitment on
+the Cloud side. For the network infrastructure, in our case of
+low-bandwidth utilization (one data packet every 10 seconds), the
+impact is almost negligible.
+
+Another way of looking at these results is to observe that only for
+a high number of sensors (more than 300), the power consumption of Cloud and
+network parts start to be negligible (few percent). It means that,
+if IoT applications handle clients one by one (i.e. one VM per
+client), the impact is high on cloud and network part if they have
+only few sensors. The energy efficiency is really poor for only few
+devices: with 20 IoT sensors, the overall energy cost to handle these
+devices is almost doubled compared to the energy consumption of the IoT devices
+themselves. Instead of increasing the number of sensors, which
+would result in a higher overall energy consumption, one should
+focus on reducing the energy consumption of IoT devices, especially
+WiFi devices which are common due to WiFi availability
+everywhere. One could also focus on improving the energy cost of
+Cloud and network infrastructure for low-bandwidth applications and
+few devices.
+
+
+
+\section{Conclusion}
+\label{sec:org76c5125}
+\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 sensor part. But the impact on the
+Cloud and network part is huge when using only few sensors with
+low-bandwidth applications.
+Consequently, with the
+IoT exploding growth, it becomes necessary to improve the energy
+efficiency of applications hosted on Cloud infrastructures and of IoT devices.
+Our future work includes studying other types of IoT wireless
+transmission techniques that would be more energy-efficient. We also
+plan to study other
+IoT applications in order to increase the applicability of our model
+and provide advice for increasing the energy-efficiency of IoT infrastructures.
+
+
+
+\bibliographystyle{IEEEtran}
+\bibliography{references}
+\end{document}