%more interesting to divide the area into several subregions, given the
%computation complexity.
-\textcolor{blue}{ Compared to our previous paper~\cite{idrees2015distributed},
- in this one we study the possibility of dividing the sensing phase into
- multiple rounds. In fact, in this paper we make a multiround optimization,
- while it was a single round optimization in our previous work. The idea is to
+\textcolor{blue}{ Compared to our previous work~\cite{idrees2015distributed},
+ in this paper we study the possibility of dividing the sensing phase into
+ multiple rounds. We make a multiround optimization,
+ while previously it was a single round optimization. The idea is to
take advantage of the pre-sensing phase to plan the sensor's activity for
several rounds instead of one, thus saving energy. In addition, when the
optimization problem becomes more complex, its resolution is stopped after a
\subsection{Assumptions and primary points}
\label{pp}
-\textcolor{blue}{Assumptions and coverage model are identical to those presented
- in~\cite{idrees2015distributed}.}
-
-\iffalse
-We consider a randomly and uniformly deployed network consisting of static
-wireless sensors. The sensors are deployed in high density to ensure initially
-a high coverage ratio of the interested area. We assume that all nodes are
-homogeneous in terms of communication and processing capabilities, and
-heterogeneous from the point of view of energy provision. Each sensor is
-supposed to get information on its location either through hardware such as
-embedded GPS or through location discovery algorithms.
-
-To model a sensor node's coverage area, we consider the boolean disk coverage
-model which is the most widely used sensor coverage model in the
-literature. Thus, each sensor has a constant sensing range $R_s$ and all space
-points within the disk centered at the sensor with the radius of the sensing
-range is said to be covered by this sensor. We also assume that the
-communication range satisfies $R_c \geq 2R_s$. In fact, Zhang and
-Zhou~\cite{Zhang05} proved that if the transmission range fulfills the previous
-hypothesis, a complete coverage of a convex area implies connectivity among the
-active nodes.\fi
-
-\textcolor{blue}{We consider a scenario where sensors are deployed in high
- density to ensure initially a high coverage ratio of the interested area. Each
+\textcolor{blue}{The assumptions and the coverage model are identical to those presented
+ in~\cite{idrees2015distributed}. We consider a scenario in which sensors are deployed in high
+ density to initially ensure a high coverage ratio of the interested area. Each
sensor has a predefined sensing range $R_s$, an initial energy supply
(eventually different from each other) and is supposed to be equipped with
- module for locating its geographical positions. All space points within the
- disk centered at the sensor with the radius of the sensing range is said to be
+ a module to locate its geographical positions. All space points within the
+ disk centered at the sensor with the radius of the sensing range are said to be
covered by this sensor.}
\indent Instead of working with the coverage area, we consider for each sensor a
As can be seen in Figure~\ref{fig2}, our protocol works in periods fashion,
where each period is divided into 4~phases: Information~Exchange,
-Leader~Election, Decision, and Sensing. \textcolor{blue}{Compared to protocol
- DiLCO described in~\cite{idrees2015distributed},} each sensing phase is itself
+Leader~Election, Decision, and Sensing. \textcolor{blue}{Compared to
+ the DiLCO protocol described in~\cite{idrees2015distributed},} each sensing phase is itself
divided into $T$ rounds of equal duration and for each round a set of sensors (a
cover set) is responsible for the sensing task. In this way a multiround
optimization process is performed during each period after Information~Exchange
sensing phase. We also consider primary points as targets. The set of primary
points is denoted by $P$ and the set of sensors by $J$. Only sensors able to be
alive during at least one round are involved in the integer program.
-\textcolor{blue}{Note that the proposed integer program is an extension of that
- formulated in~\cite{idrees2015distributed}, variables are now indexed in
+\textcolor{blue}{Note that the proposed integer program is an
+ extension of the one formulated in~\cite{idrees2015distributed}, variables are now indexed in
addition with the number of round $t$.}
For a primary point $p$, let $\alpha_{j,p}$ denote the indicator function of
in~\cite{raghunathan2002energy}. It is based on the model proposed
by~\cite{ChinhVu}. We refer to the sensor node Medusa~II which uses an Atmels
AVR ATmega103L microcontroller~\cite{raghunathan2002energy} to use numerical
- values.} \textcolor{red}{Est-ce qu'il faut en ecrire plus et redonner le
- tableau de valeurs?}
+ values.}
\iffalse
\subsection{Energy model}
primary points to be used by a MuDiLCO protocol. In this comparison, MuDiLCO-1
protocol is used with five primary point models, each model corresponding to a
number of primary points, which are called Model-5 (it uses 5 primary points),
-Model-9, Model-13, Model-17, and Model-21. \textcolor{blue}{Note that results
+Model-9, Model-13, Model-17, and Model-21. \textcolor{blue}{Note
+ that the results
presented in~\cite{idrees2015distributed} correspond to Model-13 (13 primary
points)}.
