X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/Sensornets15.git/blobdiff_plain/189bab8b6edfb58777eb8e606ef191cc6e431676..c85ec6ad6701f39ea2cf8f521d146f09d45f7cfe:/Example.tex diff --git a/Example.tex b/Example.tex index 702d24b..a7e85c3 100644 --- a/Example.tex +++ b/Example.tex @@ -124,14 +124,14 @@ and barrier coverage \cite{Kumar:2005}\cite{kim2013maximum} to prevent intrude The major approach to extend network lifetime while preserving coverage is to divide/organize the sensors into a suitable number of set covers (disjoint or -non-disjoint) where each set completely covers a region of interest and to +non-disjoint), where each set completely covers a region of interest, and to activate these set covers successively. The network activity can be planned in advance and scheduled for the entire network lifetime or organized in periods, and the set of active sensor nodes is decided at the beginning of each period \cite{ling2009energy}. Active node selection is determined based on the problem requirements (e.g. area monitoring, connectivity, power efficiency). For instance, Jaggi et al. \cite{jaggi2006} -adress the problem of maximizing network lifetime by dividing sensors into the maximum number of disjoint subsets such that each subset can ensure both coverage and connectivity. A greedy algorithm is applied once to solve this problem and the computed sets are activated in succession to achieve the desired network lifetime. -Vu \cite{chin2007}, Padmatvathy et al \cite{pc10}, propose algorithms working in a periodic fashion where a cover set is computed at the beginning of each period. +address the problem of maximizing network lifetime by dividing sensors into the maximum number of disjoint subsets such that each subset can ensure both coverage and connectivity. A greedy algorithm is applied once to solve this problem and the computed sets are activated in succession to achieve the desired network lifetime. +Vu \cite{chin2007}, Padmatvathy et al. \cite{pc10}, propose algorithms working in a periodic fashion where a cover set is computed at the beginning of each period. {\it Motivated by these works, DiLCO protocol works in periods, where each period contains a preliminary phase for information exchange and decisions, followed by a sensing phase where one cover set is in charge of the sensing task.} @@ -146,11 +146,11 @@ cooperatively by communicating with their neighbors which of them will remain in sleep mode for a certain period of time. The centralized algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high} always provide nearly or close to optimal solution since the algorithm has global view -of the whole network, but such a method has the disadvantage of requiring high +of the whole network. But such a method has the disadvantage of requiring high communication costs, since the node (located at the base station) making the decision needs information from all the sensor nodes in the area and the amount of information can be huge. {\it In order to be suitable for large-scale network, in the DiLCO protocol, the area coverage is divided into several smaller - subregions, and in each of which, a node called the leader is on charge for + subregions, and in each of one, a node called the leader is in charge for selecting the active sensors for the current period.} A large variety of coverage scheduling algorithms have been developed. Many of @@ -412,7 +412,7 @@ $X_{13}=( p_x + R_s * (0), p_y + R_s * (\frac{-\sqrt{2}}{2})) $. \fi -\subsection{The main idea} +\subsection{Main idea} \label{main_idea} \noindent We start by applying a divide-and-conquer algorithm to partition the @@ -428,7 +428,7 @@ executed simultaneously in each subregion. As shown in Figure~\ref{fig2}, the proposed DiLCO protocol is a periodic protocol where each period is decomposed into 4~phases: Information Exchange, -Leader Election , Decision, and Sensing. For each period there will be exactly +Leader Election, Decision, and Sensing. For each period there will be exactly one cover set in charge of the sensing task. A periodic scheduling is interesting because it enhances the robustness of the network against node failures. First, a node that has not enough energy to complete a period, or @@ -567,7 +567,7 @@ sensor in the subregion and then describe it in more detail. \fi \end{algorithm} \iffalse -The DiLCO protocol work in rounds and executed at each sensor node in the network , each sensor node can still sense data while being in +The DiLCO protocol work in rounds and executed at each sensor node in the network, each sensor node can still sense data while being in LISTENING mode. Thus, by entering the LISTENING mode at the beginning of each round, sensor nodes still executing sensing task while participating in the leader election and decision phases. More specifically, The DiLCO protocol algorithm works as follow: Initially, the sensor node check it's remaining energy in order to participate in the current round. Each sensor node determines it's position and it's subregion based Embedded GPS or Location Discovery Algorithm. After that, All the sensors collect position coordinates, current remaining energy, sensor node id, and the number of its one-hop live neighbors during the information exchange. It stores this information into a list L. @@ -583,7 +583,7 @@ objective is to find a maximum number of disjoint cover sets. To accomplish this goal, the authors proposed an integer program which forces undercoverage and overcoverage of targets to become minimal at the same time. They use binary variables $x_{jl}$ to indicate if sensor $j$ belongs to cover set $l$. In our -model, we consider binary variable $X_{j}$ which determine the activation of +model, we consider that the binary variable $X_{j}$ determines the activation of sensor $j$ in the sensing phase. We also consider primary points as targets. The set of primary points is denoted by $P$ and the set of sensors by $J$. @@ -640,7 +640,7 @@ U_{p} = \left \{ %\label{c1} %\sum_{t \in T} X_{j,t} \leq \frac{RE_j}{e_t} &\forall j \in J \\ %\label{c2} -\Theta_{p}\in \mathbb{N} , &\forall p \in P\\ +\Theta_{p}\in \mathbb{N}, &\forall p \in P\\ U_{p} \in \{0,1\}, &\forall p \in P \\ X_{j} \in \{0,1\}, &\forall j \in J \end{array} @@ -775,7 +775,7 @@ symmetric communication costs), and we set their respective size to 112 and is equal to 0.2575 mW. Each node has an initial energy level, in Joules, which is randomly drawn in the -interval $[500-700]$. If it's energy provision reaches a value below 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 @@ -842,7 +842,7 @@ Where: $A_r^t$ is the number of active sensors in the subregion $r$ during round where $M$ corresponds to the number of periods. The total energy consumed by the sensors (EC) comes through taking into consideration four main energy -factors. The first one , denoted $E^{\scriptsize \mbox{com}}_m$, represent the +factors. The first one, denoted $E^{\scriptsize \mbox{com}}_m$, represent the energy consumption spent by all the nodes for wireless communications during period $m$. $E^{\scriptsize \mbox{list}}_m$, the next factor, corresponds to the energy consumed by the sensors in LISTENING status before receiving the @@ -1075,9 +1075,10 @@ difficult, but will reduce the communication overhead. \fi \section*{\uppercase{Acknowledgements}} -\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 the financial support and -Campus France for the received support. +Campus France for the received support. This paper is also partially funded by +the Labex ACTION program (contract ANR-11-LABX-01-01). %\vfill \bibliographystyle{apalike}