\label{ch6}
-\section{Summary}
+\section{Introduction}
\label{ch6:sec:01}
-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
-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
-region of interest is first subdivided into subregions and our protocol is then
-distributed among sensor nodes in each subregion.
-The novelty of our approach lies essentially in the formulation of a new
-mathematical optimization model based on the perimeter coverage level to schedule
-sensors' activities. Extensive simulation experiments have been performed using
-OMNeT++, the discrete event simulator, to demonstrate that PeCO can
-offer longer lifetime coverage for WSNs in comparison with some other protocols.
+The continuous progress in Micro Electro-Mechanical Systems (MEMS) and
+wireless communication hardware has given rise to the opportunity to use large
+networks of tiny sensors, called Wireless Sensor Networks (WSN)~\cite{ref1,ref223}, to fulfill monitoring tasks. The features of a WSN made it suitable for a wide
+range of application in areas such as business, environment, health, industry,
+military, and so on~\cite{ref4}. These large number of applications have led to different design, management, and operational challenges in WSNs. The challenges become harder with considering into account the main limited capabilities of the sensor nodes such memory, processing, battery life, bandwidth, and short radio ranges. One important feature that distinguish the WSN from the other types of wireless networks is the provision of the sensing capability for the sensor nodes \cite{ref224}.
+
+The sensor node consumes some energy both in performing the sensing task and in transmitting the sensed data to the sink. Therefore, it is required to activate as less number as possible of sensor nodes that can monitor the whole area of interest so as to reduce the data volume and extend the network lifetime. The sensing coverage is the most important task of the WSNs since sensing unit of the sensor node is responsible for measuring physical, chemical, or biological phenomena in the sensing field. The main challenge of any sensing coverage problem is to discover the redundant sensor node and turn off those nodes in WSN \cite{ref225}. The redundant sensor node is a node whose sensing area is covered by its active neighbors. In previous works, several approaches are used to find out the redundant node such as Voronoi diagram method, sponsored sector, crossing coverage, and perimeter coverage.
+
+In this chapter, we propose such an approach called Perimeter-based Coverage Optimization
+protocol (PeCO). The PeCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages. An energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. This optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period.
+
+
+The rest of the chapter is organized as follows. The next section is devoted to the PeCO protocol description and section~\ref{ch6:sec:03} focuses on the
+coverage model formulation which is used to schedule the activation of sensor
+nodes based on perimeter coverage model. Section~\ref{ch6:sec:04} presents simulations
+results and discusses the comparison with other approaches. Finally, concluding
+remarks are drawn in section~\ref{ch6:sec:05}.
+
+
\section{The PeCO Protocol Description}
\label{ch6:sec:02}
Every couple of intersection points is placed on the angular interval $[0,2\pi]$
in a counterclockwise manner, leading to a partitioning of the interval.
Figure~\ref{pcm2sensors}(a) illustrates the arcs for the nine neighbors of
-sensor $0$ and Figure~\ref{expcm} gives the position of the corresponding arcs
+sensor $0$ and Figure~\ref{expcm} gives the position of the corresponding arcs
in the interval $[0,2\pi]$. More precisely, we can see that the points are
ordered according to the measures of the angles defined by their respective
positions. The intersection points are then visited one after another, starting
The values of $\alpha^j_i$ and $\beta^j_i$ have been chosen to ensure a good
network coverage and a longer WSN lifetime. We have given a higher priority to
-the undercoverage (by setting the $\alpha^j_i$ with a larger value than
+the undercoverage (by setting the $\alpha^j_i$ with a larger value than
$\beta^j_i$) so as to prevent the non-coverage for the interval~$i$ of the
sensor~$j$. On the other hand, we have assigned to
$\beta^j_i$ a value which is slightly lower so as to minimize the number of active sensor nodes which contribute
\section{Conclusion}
-\label{ch6:sec:04}
+\label{ch6:sec:05}
In this chapter, we have studied the problem of Perimeter-based Coverage Optimization in
WSNs. We have designed a new protocol, called Perimeter-based Coverage Optimization, which
