Correct typos

2019-Mascots.org

@@ -135,7 +135,7 @@ component, formatting, style, styling, insert
    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 Figure \ref{fig:sensorsPos} show that sensors position have a very low impact
-   on the energy consumption and on the application delay. It has an impact of course but it is very
+   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.
 
@@ -150,12 +150,13 @@ component, formatting, style, styling, insert
 
     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 how case, application accuracy is driven by the sensing frequency and thus the transmit
+    in our case, application accuracy is driven by the sensing interval and thus, the transmit
-    frequency of the sensors. In this way, we vary the transmission frequency of the sensors from 1s
+    frequency of the sensors. Therefore, we varied the transmission interval of the sensors from 1s
-    to 60s. Figure \ref{fig:frequency} present the effects of the sensors transmission frequency on
+    to 60s. Figure \ref{fig:frequency} present the effects of the sensors transmission interval on
     the IoT/Network part energy consumption. In case of small and sporadic network traffic, these
-    results show that with a reasonable transmission frequency the energy consumption of the
+    results show that with a reasonable transmission interval the energy consumption of the
-    IoT/Network if almost not affected by the variation of this frequency.
+    IoT/Network if 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.
 
     #+BEGIN_EXPORT latex
     \begin{figure}
@@ -167,13 +168,14 @@ component, formatting, style, styling, insert
     #+END_EXPORT
     
 
-   The number of sensors is the dominant factor that leverage the energy consumption of the
+   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 consumption of each simulated
+   impact. The Figure \ref{fig:sensorsNumber} represents the energy consumed by each simulated part
-   part. It is clear that the energy consume by the network is the dominant part. However, since the
+   according the the number of sensors. It is clear that the energy consumed by the network is the
-   number of sensors is increasing the energy consume by the network will become negligible face to
+   dominant part. However, since the number of sensors is increasing the energy consumed by the
-   the energy consume by the sensors. In fact, deploying new sensors in the cell do not introduce
+   network will become negligible face to the energy consume by the sensors. In fact, deploying new
-   much network load. To this end, sensors energy consumption is dominant. 
+   sensors in the cell do not introduce much network load. To this end, sensors energy consumption
+   is dominant.
    
     #+BEGIN_EXPORT latex
     \begin{figure}
@@ -193,11 +195,12 @@ component, formatting, style, styling, insert
 
    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 red horizontal line represent
+   allocated memory for 20 sensors sending data every 10s. Note that horizontal red line represent
-   the average energy consumption for sample of energy value. We can see that at each sensing
+   the average energy consumption for the considered sample of energy values. We can see that at
-   interval, server face to peaks of energy consumption. However, VM allocated memory do not
+   each sensing interval, server face to peaks of energy consumption. However, VM allocated memory
-   influence energy consumption. In fact, simple database requests do not need any particular huge
+   do not influence energy consumption. In fact, simple database requests do not need any particular
-   memory access. Thus, remaining experiments are based on VM allocated memory of 1024MB.
+   huge memory access and processing time. Thus, remaining experiments are based on VM with 1024MB
+   of allocated memory.
 
    #+BEGIN_EXPORT latex
    \begin{figure}
@@ -208,17 +211,16 @@ component, formatting, style, styling, insert
    \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
-   number of sensors. These shows a clear linear relation between the number of sensors and the
+   number of sensors. These results show a clear linear relation between the number of sensors and
-   server energy consumption. Moreover, we can see that the more sensors we have per server, the
+   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
-   energy efficient to maximize the number of sensors per server. As showed on Figure
+   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
+   \ref{fig:sensorsNumber-WPS}, a significant amount of energy can be save when passing from 20 to
-   sensors to 300 per server.
+   300 sensors per server.
 
    #+BEGIN_EXPORT latex
    \begin{figure}
@@ -237,12 +239,12 @@ component, formatting, style, styling, insert
    \end{figure}
    #+END_EXPORT
 
-   A last parameter can leverage server energy consumption namely sensors send frequency. In
+   A last parameter can leverage server energy consumption namely sensors send interval. In addition
-   addition to increasing the application accuracy, sensors send frequency increase network traffic
+   to increasing the application accuracy, sensors send interval increase network traffic and
-   and database accesses. Figure \ref{fig:sensorsFrequency} present the impact on the server energy
+   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
-   more sensors send interval is low, the more server energy consumption peaks occurs. Therefore, it
+   lower sensors send interval is, the more server energy consumption peaks occurs. Therefore, it
-   leads to an increasing in the server energy consumption.
+   leads to an increase of the server energy consumption.
 
    #+BEGIN_EXPORT latex
    \begin{figure}
@@ -256,15 +258,14 @@ component, formatting, style, styling, insert
 ** End-To-End Consumption
    
    To have an overview of the energy consume by the system, it is important to consider the
-   end-to-end energy consumption. The Figure \ref{fig:end-to-end} represent the end-to-end system
+   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
    sensors. However, since we are using a single server, large-scale sensors deployment lead to an
-   increasing consumption of energy in the sensors side. On the other side, network energy
+   increasing consumption of energy in the IoT part. On the other side, network energy consumption
-   consumption is stable regarding to the number of sensors that are deployed since network the
+   is stable regarding to the number of sensors since the system use case do not required large data
-   system use case do not required large data transfer. Thus, it is important to remember that, to
+   transfert. Thus, it is important to remember that, to save energy, we should maximize the number
-   save energy, we should maximize the number of sensors handle by each cloud server while keeping a
+   of sensors handle by each cloud server while keeping reasonable sensors request intervals.
-   reasonable sensors requests intervals.
 
    #+BEGIN_EXPORT latex
    \begin{figure}