%% CHAPTER 06 %%
%% %%
%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
-
-\chapter{Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}
+ \chapter{ Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}
\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 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.
+The framework is similar to the one described in chapter 4, section \ref{ch4:sec:02:03}, but in this approach, the 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}
\subsection{Assumptions and Models}
\label{ch6:sec:02:01}
-PeCO protocol uses the same assumptions and network model that presented in chapter 4, section \ref{ch4:sec:02:01}.
+The PeCO protocol uses the same assumptions and network model that presented in chapter 4, section \ref{ch4:sec:02:01}.
The PeCO protocol uses the same perimeter-coverage model as Huang and
Tseng in~\cite{ref133}. It can be expressed as follows: a sensor is
-said to be perimeter covered if all the points on its perimeter are covered by
+said to be a perimeter covered if all the points on its perimeter are covered by
at least one sensor other than itself. They proved that a network area is
$k$-covered if and only if each sensor in the network is $k$-perimeter-covered (perimeter covered by at least $k$ sensors).
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
\end{table}
-In the PeCO protocol, the scheduling of the sensor nodes' activities is formulated with an
-integer program based on coverage intervals. The formulation of the coverage
-optimization problem is detailed in~section~\ref{ch6:sec:03}. Note that when a sensor
-node has a part of its sensing range outside the WSN sensing field, as in
-Figure~\ref{ex4pcm}, the maximum coverage level for this arc is set to $\infty$
-and the corresponding interval will not be taken into account by the
-optimization algorithm.
+In the PeCO protocol, the scheduling of the sensor nodes' activities is formulated as an integer program based on coverage intervals. The formulation of the coverage optimization problem is detailed in~section~\ref{ch6:sec:03}. Note that when a sensor node has a part of its sensing range outside the WSN sensing field, as in Figure~\ref{ex4pcm}, the maximum coverage level for this arc is set to $\infty$ and the corresponding interval will not be taken into account by the optimization algorithm.
\begin{figure}[h!]
\end{figure}
-
-
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This section deleted %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+\iffalse
\subsection{The Main Idea}
\label{ch6:sec:02:02}
our protocol will be executed in a distributed way in each subregion
simultaneously to schedule nodes' activities for one sensing period.
-As shown in Figure~\ref{fig2}, node activity scheduling is produced by our
-protocol in a periodic manner. Each period is divided into 4 stages: Information
-(INFO) Exchange, Leader Election, Decision (the result of an optimization
-problem), and Sensing. For each period there is exactly one set cover
-responsible for the sensing task. Protocols based on a periodic scheme, like
-PeCO, are more robust against an unexpected node failure. On the one hand, if
-a node failure is discovered before taking the decision, the corresponding sensor
-node will not be considered by the optimization algorithm. On the other
-hand, if the sensor failure happens after the decision, the sensing task of the
-network will be temporarily affected: only during the period of sensing until a
-new period starts, since a new set cover will take charge of the sensing task in
-the next period. The energy consumption and some other constraints can easily be
-taken into account since the sensors can update and then exchange their
-information (including their residual energy) at the beginning of each period.
-However, the pre-sensing phases (INFO Exchange, Leader Election, and Decision)
-are energy consuming, even for nodes that will not join the set cover to monitor
-the area.
+As shown in Figure~\ref{fig2}, node activity scheduling is produced by our protocol in a periodic manner. Each period is divided into 4 stages: Information (INFO) Exchange, Leader Election, Decision (the result of an optimization problem), and Sensing. For each period, there is exactly one set cover responsible for the sensing task. Protocols based on a periodic scheme, like PeCO, are more robust against an unexpected node failure. On the one hand, if a node failure is discovered before taking the decision, the corresponding sensor
+node will not be considered by the optimization algorithm. On the other hand, if the sensor failure happens after the decision, the sensing task of the network will be temporarily affected: only during the period of sensing until a new period starts, since a new set cover will take charge of the sensing task in the next period. The energy consumption and some other constraints can easily be taken into account since the sensors can update and then exchange their information (including their residual energy) at the beginning of each period. However, the pre-sensing phases (INFO Exchange, Leader Election, and Decision)
+are energy consuming, even for nodes that will not join the set cover to monitor the area.
\begin{figure}[t!]
\centering
\label{fig2}
\end{figure}
-
-
+\fi
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
\subsection{PeCO Protocol Algorithm}
\label{ch6:sec:02:03}
\noindent The pseudocode implementing the protocol on a node is given below.
More precisely, Algorithm~\ref{alg:PeCO} gives a brief description of the
-protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN.
+protocol applied by a sensor node $s_j$ where $j$ is the node index in the WSN.
\begin{algorithm}[h!]
% \KwIn{all the parameters related to information exchange}
%\emph{Initialize the sensor node and determine it's position and subregion} \;
\If{ $RE_k \geq E_{th}$ }{
- \emph{$s_k.status$ = COMMUNICATION}\;
+ \emph{$s_j.status$ = COMMUNICATION}\;
\emph{Send $INFO()$ packet to other nodes in subregion}\;
\emph{Wait $INFO()$ packet from other nodes in subregion}\;
- \emph{Update K.CurrentSize}\;
+ \emph{Update A.CurrentSize}\;
\emph{LeaderID = Leader election}\;
- \If{$ s_k.ID = LeaderID $}{
- \emph{$s_k.status$ = COMPUTATION}\;
+ \If{$ s_j.ID = LeaderID $}{
+ \emph{$s_j.status$ = COMPUTATION}\;
- \If{$ s_k.ID $ is Not previously selected as a Leader }{
+ \If{$ s_j.ID $ is Not previously selected as a Leader }{
\emph{ Execute the perimeter coverage model}\;
% \emph{ Determine the segment points using perimeter coverage model}\;
}
- \If{$ (s_k.ID $ is the same Previous Leader) And (K.CurrentSize = K.PreviousSize)}{
+ \If{$ (s_j.ID $ is the same Previous Leader) And (A.CurrentSize = A.PreviousSize)}{
\emph{ Use the same previous cover set for current sensing stage}\;
}
\Else{
\emph{Update $a^j_{ik}$; prepare data for IP~Algorithm}\;
- \emph{$\left\{\left(X_{1},\dots,X_{l},\dots,X_{K}\right)\right\}$ = Execute Integer Program Algorithm($K$)}\;
- \emph{K.PreviousSize = K.CurrentSize}\;
+ \emph{$\left\{\left(X_{1},\dots,X_{k},\dots,X_{A}\right)\right\}$ = Execute Integer Program Algorithm($A$)}\;
+ \emph{A.PreviousSize = A.CurrentSize}\;
}
- \emph{$s_k.status$ = COMMUNICATION}\;
- \emph{Send $ActiveSleep()$ to each node $l$ in subregion}\;
- \emph{Update $RE_k $}\;
+ \emph{$s_j.status$ = COMMUNICATION}\;
+ \emph{Send $ActiveSleep()$ to each node $k$ in subregion}\;
+ \emph{Update $RE_j $}\;
}
\Else{
- \emph{$s_k.status$ = LISTENING}\;
+ \emph{$s_j.status$ = LISTENING}\;
\emph{Wait $ActiveSleep()$ packet from the Leader}\;
- \emph{Update $RE_k $}\;
+ \emph{Update $RE_j $}\;
}
}
- \Else { Exclude $s_k$ from entering in the current sensing stage}
-\caption{PeCO($s_k$)}
+ \Else { Exclude $s_j$ from entering in the current sensing stage}
+\caption{PeCO($s_j$)}
\label{alg:PeCO}
\end{algorithm}
-In this algorithm, K.CurrentSize and K.PreviousSize respectively represent the
+In this algorithm, A.CurrentSize and A.PreviousSize respectively represent the
current number and the previous number of living nodes in the subnetwork of the
-subregion. Initially, the sensor node checks its remaining energy $RE_k$, which
+subregion. Initially, the sensor node checks its remaining energy $RE_j$, which
must be greater than a threshold $E_{th}$ in order to participate in the current
period. Each sensor node determines its position and its subregion using an
embedded GPS or a location discovery algorithm. After that, all the sensors
collect position coordinates, remaining energy, sensor node ID, and the number
of their one-hop live neighbors during the information exchange. The sensors
inside a same region cooperate to elect a leader. The selection criteria for the
-leader, in order of priority, are: larger numbers of neighbors, larger remaining
+leader, in order of priority, are larger numbers of neighbors, larger remaining
energy, and then in case of equality, larger index. Once chosen, the leader
collects information to formulate and solve the integer program which allows to
construct the set of active sensors in the sensing stage.
First, we have the following sets:
\begin{itemize}
-\item $S$ represents the set of WSN sensor nodes;
-\item $A \subseteq S $ is the subset of alive sensors;
+\item $J$ represents the set of WSN sensor nodes;
+\item $A \subseteq J $ is the subset of alive sensors;
\item $I_j$ designates the set of coverage intervals (CI) obtained for
sensor~$j$.
\end{itemize}
lifetime, the objective is to activate a minimal number of sensors in each
period to ensure the desired coverage level. As the number of alive sensors
decreases, it becomes impossible to reach the desired level of coverage for all
-coverage intervals. Therefore we use variables $M^j_i$ and $V^j_i$ as a measure
+coverage intervals. Therefore, we use variables $M^j_i$ and $V^j_i$ as a measure
of the deviation between the desired number of active sensors in a coverage
interval and the effective number. And we try to minimize these deviations,
first to force the activation of a minimal number of sensors to ensure the
desired coverage level, and if the desired level cannot be completely satisfied,
to reach a coverage level as close as possible to the desired one.
-
Our coverage optimization problem can then be mathematically expressed as follows:
%Objective:
\begin{equation} %\label{eq:ip2r}
\left \{
\begin{array}{ll}
-\min \sum_{j \in S} \sum_{i \in I_j} (\alpha^j_i ~ M^j_i + \beta^j_i ~ V^j_i )&\\
+\min \sum_{j \in J} \sum_{i \in I_j} (\alpha^j_i ~ M^j_i + \beta^j_i ~ V^j_i )&\\
\textrm{subject to :}&\\
-\sum_{k \in A} ( a^j_{ik} ~ X_{k}) + M^j_i \geq l \quad \forall i \in I_j, \forall j \in S\\
+\sum_{k \in A} ( a^j_{ik} ~ X_{k}) + M^j_i \geq l \quad \forall i \in I_j, \forall j \in J\\
%\label{c1}
-\sum_{k \in A} ( a^j_{ik} ~ X_{k}) - V^j_i \leq l \quad \forall i \in I_j, \forall j \in S\\
+\sum_{k \in A} ( a^j_{ik} ~ X_{k}) - V^j_i \leq l \quad \forall i \in I_j, \forall j \in J\\
% \label{c2}
% \Theta_{p}\in \mathbb{N}, &\forall p \in P\\
% U_{p} \in \{0,1\}, &\forall p \in P\\
relative importance of satisfying the associated level of coverage. For example,
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
+region. This kind of an integer program is inspired from the model developed for
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
different node densities going from 100 to 300~nodes were performed considering
each time 25~randomly generated networks. The nodes are deployed on a field of
interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a
-high coverage ratio. Each node has an initial energy level, in Joules, which is
-randomly drawn in the interval $[500-700]$. If its energy provision reaches a
-value below the threshold $E_{th}=36$~Joules, the minimum energy needed for a
-node to stay active during one period, it will no more participate in the
-coverage task. This value corresponds to the energy needed by the sensing phase,
-obtained by multiplying the energy consumed in active state (9.72 mW) with the
-time in seconds for one period (3600 seconds), and adding the energy for the
-pre-sensing phases. According to the interval of initial energy, a sensor may
-be active during at most 20 periods.
+high coverage ratio.
+%Each node has an initial energy level, in Joules, which is randomly drawn in the interval $[500-700]$. If its energy provision reaches a value below the threshold $E_{th}=36$~Joules, the minimum energy needed for a node to stay active during one period, it will no more participate in the coverage task. This value corresponds to the energy needed by the sensing phase, obtained by multiplying the energy consumed in active state (9.72 mW) with the time in seconds for one period (3600 seconds), and adding the energy for the pre-sensing phases. According to the interval of initial energy, a sensor may be active during at most 20 periods.
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
-in covering the interval.
+$\beta^j_i$ a value which is slightly lower so as to minimize the number of active sensor nodes which contribute in covering the interval.
-We applied the performance metrics, which are described in chapter 4, section \ref{ch4:sec:04:04} in order to evaluate the efficiency of our approach. We used the modeling language and the optimization solver which are mentioned in chapter 4, section \ref{ch4:sec:04:02}. In addition, we employed an energy consumption model, which is presented in chapter 4, section \ref{ch4:sec:04:03}.
+With the performance metrics, described in chapter 4, section \ref{ch4:sec:04:04}, we evaluate the efficiency of our approach. We use the modeling language and the optimization solver which are mentioned in chapter 4, section \ref{ch4:sec:04:02}. In addition, we use the same energy consumption model, presented in chapter 4, section \ref{ch4:sec:04:03}.
\subsection{Simulation Results}
difference is more obvious in Figure~\ref{fig3LT}(b) than in
Figure~\ref{fig3LT}(a) because the gain induced by our protocols increases with
time, and the lifetime with a coverage of 50\% is far longer than with
-95\%.
+95\%.
-\begin{figure}[h!]
+\begin{figure} [p]
\centering
\begin{tabular}{@{}cr@{}}
\includegraphics[scale=0.8]{Figures/ch6/R/LT95.eps} & \raisebox{4cm}{(a)} \\
size. DiLCO is better for coverage ratios near 100\%, but in that case PeCO is
not ineffective for the smallest network sizes.
-\begin{figure}[h!]
+\begin{figure} [p]
\centering \includegraphics[scale=0.8]{Figures/ch6/R/LTa.eps}
\caption{Network lifetime for different coverage ratios.}
\label{figLTALL}
-\end{figure}
+\end{figure}
-
-\section{Conclusion}
-\label{ch6:sec:04}
+ %\FloatBarrier
+\section{Conclusion}
+\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
