\begin{abstract}
The most important problem in a Wireless Sensor Network (WSN) is to optimize the
-use of its limited energy provision, so that it can fulfill its monitoring task
-as long as possible. Among known available approaches that can be used to
+use of its limited energy provision, so that it can fulfill its monitoring task
+as long as possible. Among known available approaches that can be used to
improve power management, lifetime coverage optimization provides activity
scheduling which ensures sensing coverage while minimizing the energy cost. In
this paper, we propose such an approach called Perimeter-based Coverage Optimization
-protocol (PeCO). It is a hybrid of centralized and distributed methods: the
+protocol (PeCO). It is a hybrid of centralized and distributed methods: the
region of interest is first subdivided into subregions and our protocol is then
distributed among sensor nodes in each subregion.
% A sensor node which runs LiCO protocol repeats periodically four stages:
\section{ The PeCO Protocol Description}
\label{sec:The PeCO Protocol Description}
-\noindent In this section, we describe in details our Lifetime Coverage
+\noindent In this section, we describe in details our Perimeter-based Coverage
Optimization protocol. First we present the assumptions we made and the models
we considered (in particular the perimeter coverage one), second we describe the
background idea of our protocol, and third we give the outline of the algorithm
% The leader has the responsibility of applying the integer program algorithm (see section~\ref{cp}), which provides a set of sensors planned to be active in the sensing stage. As leader, it will send an Active-Sleep packet to each sensor in the same subregion to inform it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing stage.
-\section{Lifetime Coverage problem formulation}
+\section{Perimeter-based Coverage Problem Formulation}
\label{cp}
\noindent In this section, the coverage model is mathematically formulated. We
-start with a description of the notations that will be used throughout the
+start with a description of the notations that will be used throughout the
section.
First, we have the following sets:
weights associated with coverage intervals of a specified part of a region may
be given by a relatively larger magnitude than weights associated with another
region. This kind of integer program is inspired from the model developed for
-brachytherapy treatment planning for optimizing dose distribution
+brachytherapy treatment planning for optimizing dose distribution
\cite{0031-9155-44-1-012}. The integer program must be solved by the leader in
each subregion at the beginning of each sensing phase, whenever the environment
has changed (new leader, death of some sensors). Note that the number of
\section{Conclusion and Future Works}
\label{sec:Conclusion and Future Works}
-In this paper we have studied the problem of lifetime coverage optimization in
-WSNs. We have designed a new protocol, called Lifetime Coverage Optimization, which
+In this paper we have studied the problem of Perimeter-based Coverage Optimization in
+WSNs. We have designed a new protocol, called Perimeter-based Coverage Optimization, which
schedules nodes' activities (wake up and sleep stages) with the objective of
maintaining a good coverage ratio while maximizing the network lifetime. This
protocol is applied in a distributed way in regular subregions obtained after
\section*{Acknowledgments}
-\noindent As a Ph.D. student, Ali Kadhum IDREES would like to gratefully
+\noindent As a Ph.D. student, Ali Kadhum IDREES would like to gratefully
acknowledge the University of Babylon - IRAQ for financial support and Campus
France for the received support. This work is also partially funded by the Labex ACTION program (contract ANR-11-LABX-01-01).
