X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/585c6ba024142990d0e8889792b49ca87292c3ee..dc6cf8e426e52e11890b51d8cfbe8193285bea12:/CHAPITRE_06.tex diff --git a/CHAPITRE_06.tex b/CHAPITRE_06.tex index 121ae35..f72a7ba 100644 --- a/CHAPITRE_06.tex +++ b/CHAPITRE_06.tex @@ -3,24 +3,21 @@ %% 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{Introduction} \label{ch6:sec:01} -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 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. +%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. +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 @@ -151,8 +148,9 @@ In the PeCO protocol, the scheduling of the sensor nodes' activities is formul \end{figure} - - +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% This section deleted %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +\iffalse \subsection{The Main Idea} \label{ch6:sec:02:02} @@ -173,8 +171,9 @@ are energy consuming, even for nodes that will not join the set cover to monitor \label{fig2} \end{figure} - - +\fi +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% +%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%% \subsection{PeCO Protocol Algorithm} \label{ch6:sec:02:03} @@ -182,7 +181,7 @@ are energy consuming, even for nodes that will not join the set cover to monitor \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} @@ -194,7 +193,7 @@ protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN. \emph{$s_k.status$ = 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}\; @@ -204,14 +203,14 @@ protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN. % \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_k.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{$\left\{\left(X_{1},\dots,X_{l},\dots,X_{A}\right)\right\}$ = Execute Integer Program Algorithm($A$)}\; + \emph{A.PreviousSize = A.CurrentSize}\; } \emph{$s_k.status$ = COMMUNICATION}\; @@ -229,7 +228,7 @@ protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN. \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 must be greater than a threshold $E_{th}$ in order to participate in the current @@ -255,8 +254,8 @@ section. 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} @@ -303,11 +302,11 @@ Our coverage optimization problem can then be mathematically expressed as follow \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\\ @@ -376,15 +375,8 @@ To obtain experimental results which are relevant, simulations with five 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 @@ -392,10 +384,9 @@ 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 $\beta^j_i$) 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}