X-Git-Url: https://bilbo.iut-bm.univ-fcomte.fr/and/gitweb/ThesisAli.git/blobdiff_plain/672f1f775e091f7120b3ad28a38dc90f11163105..e0f801634bcae6d3ea76eafe17926f59bd4127af:/CHAPITRE_02.tex?ds=sidebyside

diff --git a/CHAPITRE_02.tex b/CHAPITRE_02.tex
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@@ -38,7 +38,7 @@ in WSNs. Cardei and Wu~\cite{ref113} provide a taxonomy for coverage algorithms
 The choice of non-disjoint or disjoint cover sets (sensors participate or not in many cover sets), coverage type ( area, target, or barrier), coverage ratio, coverage degree (how many sensors are required to cover a target or an area) can be added to the above list.
 
 
-Once a sensor nodes are deployed, a coverage algorithm is run to schedule the sensor nodes into cover sets so as to maintain sufficient coverage in the area of interest and extend the network lifetime. The WSN applications require (complete or partial) area coverage and complete target coverage. Many centralized algorithms and distributed algorithms for activity scheduling have been proposed in the literature, and based on different assumptions and objectives. In centralized algorithms, a central controller makes all decisions and distributes the results to sensor nodes. A distributed algorithms, on the other hand, the decision process is localized in each individual sensor node, and only information from neighboring nodes are used for the activity decision. Compared to centralized algorithms, distributed algorithms reduce the energy consumption required for radio communication and detection accuracy whilst increase the energy consumption for computation. Overall, distributed algorithms are more suitable for large-scale networks, but it can not give optimal (or near optimal) solution based only on local information. Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{ref113,ref153,ref103,ref105}. Table~\ref{Table1:ch2} summarized the main characteristics of some coverage approaches in previous literatures.
+Once a sensor nodes are deployed, a coverage algorithm is run to schedule the sensor nodes into cover sets so as to maintain sufficient coverage in the area of interest and extend the network lifetime. The WSN applications require (complete or partial) area coverage and complete target coverage. Many centralized algorithms and distributed algorithms for activity scheduling have been proposed in the literature, and based on different assumptions and objectives. In centralized algorithms, a central controller makes all decisions and distributes the results to sensor nodes. A distributed algorithms, on the other hand, the decision process is localized in each individual sensor node, and only information from neighboring nodes are used for the activity decision. Compared to centralized algorithms, distributed algorithms reduce the energy consumption required for radio communication and detection accuracy whilst increase the energy consumption for computation. Overall, distributed algorithms are more suitable for large-scale networks, but it can not give optimal (or near optimal) solution based only on local information. Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{ref113,ref101,ref103,ref105}. Table~\ref{Table1:ch2} summarized the main characteristics of some coverage approaches in previous literatures.
 
 
 \subsection{Centralized Algorithms}
@@ -86,7 +86,7 @@ Various centralized  methods  based on column generation approaches have also be
 \label{ch2:sec:04}
 
 In distributed and localized coverage  algorithms, the required  computation to schedule the  activity of  sensor nodes  will be done  by the  cooperation among neighboring nodes. These  algorithms may require more computation  power for the processing by the cooperating sensor nodes, but they are more scalable for large WSNs. Localized and distributed algorithms generally result in non-disjoint set covers.
-Many distributed algorithms have been  developed to perform the scheduling so as to preserve coverage, see for example \cite{ref123,ref124,ref125,ref126,ref109,ref127,ref128,ref129}. 
+Many distributed algorithms have been  developed to perform the scheduling so as to preserve coverage, see for example \cite{ref123,ref124,ref125,ref126,ref109,ref127,ref128,ref97}. 
 
 Distributed  algorithms typically operate in rounds for a predetermined duration. At the beginning of each  round, a sensor exchanges information with its neighbors and makes a  decision to either remain turned on or  to go to sleep for  the round. This decision is  basically made on simple greedy criteria like the largest uncovered area \cite{ref130} or maximum uncovered targets \cite{ref131}. The Distributed  Adaptive Sleep  Scheduling  Algorithm (DASSA) \cite{ref127} does not require location information of sensors while  maintaining connectivity and  satisfying a user  defined coverage target. In DASSA, nodes use the residual energy levels and  feedback from the sink for  scheduling the activity  of their neighbors. This feedback mechanism reduces the randomness  in scheduling  that would otherwise occur due to the absence of location information.  
 
@@ -102,8 +102,16 @@ The works presented in~\cite{ref134,ref135,ref136} focus on coverage-aware, dist
 
 Shibo et al.~\cite{ref137} have expressed the coverage problem as a  minimum  weight submodular set cover problem  and proposed a Distributed Truncated Greedy Algorithm (DTGA) to solve it. They take  advantage from both temporal and spatial correlations between  data sensed by different sensors, and leverage prediction, to improve  the lifetime. 
 
+In \cite{ref160}  authors  transform the  area  coverage  problem to  the  target
+coverage one taking into account the  intersection points among disks of sensors
+nodes or between disk of sensor nodes and boundaries.
 
-In \cite{ref84}, Xu et al. have described an algorithm, called Geographical Adaptive Fidelity (GAF), which uses geographic location information to divide the area of interest into fixed square grids. Within each grid, it keeps only one node staying awake to take the responsibility of sensing and communication. Figure~\ref{gaf1} gives an example of fixed square grid in GAF.
+
+In \cite{ref133} authors prove  that  if  the perimeters  of sensors are sufficiently  covered it will be  the case for the  whole area. They provide an algorithm in $O(nd~log~d)$  time to compute the perimeter-coverage of
+each  sensor,  where  $d$  denotes  the  maximum  number  of  sensors  that  are neighboring  to  a  sensor and  $n$  is  the  total  number of  sensors  in  the network.
+
+
+In \cite{GAF}, Xu et al. have described an algorithm, called Geographical Adaptive Fidelity (GAF), which uses geographic location information to divide the area of interest into fixed square grids. Within each grid, it keeps only one node staying awake to take the responsibility of sensing and communication. Figure~\ref{gaf1} gives an example of fixed square grid in GAF.
 
 \begin{figure}[h!]
 \centering
@@ -134,7 +142,7 @@ The sensor nodes in GAF can be in one of three states: active, sleep, or discove
 The sensor node sets a timer to $T_d$ seconds after entering in discovery state. As soon as the timer fires, the sensor node broadcast its discovery message and enters in active state. The active sensor node sets a timeout value $T_a$ to define how long it can stay in active state. After $T_a$, the sensor node will return to the discovery state. Whilst, during its active state, it re-broadcasts its discovery message at intervals $T_d$ periodically. The sensor node with discovery or active state can change its state to sleep when it detects that some other equivalent node will handle routing inside the grid. The sensor nodes in the sleeping state wake up after a sleeping time $T_s$ and go back to the discovery state. In GAF, load balancing is performed by means of periodic election of the leader (i.e., the active node that handle the routing inside the fixed grid). A rank-based election algorithm has been used to elect the leader. It is based on the remaining energy of sensor nodes inside the fixed square grid so as to extend the network lifetime In proportionally with network density.
 
 
-In~\cite{ref132}, the author have designed a novel distributed heuristic, called Distributed Energy-efficient Scheduling for k-coverage (DESK), which  ensures that the energy consumption  among the sensors is balanced  and the lifetime  maximized  while  the coverage  requirement  is maintained. This heuristic  works in  rounds, requires  only  one-hop neighbor information, and each  sensor decides its status (active or  sleep) based on the perimeter coverage model from~\cite{ref133}. Figure~\ref{desk} shows the DESK network time line.
+In~\cite{DESK}, the author have designed a novel distributed heuristic, called Distributed Energy-efficient Scheduling for k-coverage (DESK), which  ensures that the energy consumption  among the sensors is balanced  and the lifetime  maximized  while  the coverage  requirement  is maintained. This heuristic  works in  rounds, requires  only  one-hop neighbor information, and each  sensor decides its status (active or  sleep) based on the perimeter coverage model from~\cite{ref133}. Figure~\ref{desk} shows the DESK network time line.
 
 \begin{figure}[h!]
 \centering
@@ -226,7 +234,7 @@ check if its $n_i$ is decreased to 0 or not. If $n_i$ of a sensor node is 0 (i.e
 
 & \tiny  S. K. Prasad and A. Dhawan (2007)~\cite{ref128} & \OK & &  & \OK & & & \OK &  & \OK &  & \OK &  &\\
 
-& \tiny  S. Misra et al. (2011)~\cite{ref129} & \OK &   & \OK &  &  &  & \OK &  & \OK &  &  &  &\\
+& \tiny  S. Misra et al. (2011)~\cite{ref97} & \OK &   & \OK &  &  &  & \OK &  & \OK &  &  &  &\\
 
 & \tiny  P. Berman et al. (2005)~\cite{ref130}  & \OK & \OK & \OK &  &  &  & \OK &  & \OK & \OK &  &\\
 
@@ -238,7 +246,7 @@ check if its $n_i$ is decreased to 0 or not. If $n_i$ of a sensor node is 0 (i.e
 
 & \tiny  V. T. Quang and T. Miyoshi (2008)~\cite{ref146}  & \OK &   & \OK &  & \OK &  & \OK &  & \OK &  & \OK &  &\\
 
-\rot{\rlap{Some Proposed Coverage Protocols in previous literatures}}
+\rot{\rlap{Some Proposed Coverage Protocols in previous literatures}} 
 
 & \tiny  D. Dong et al. (2012)~\cite{ref149}  & \OK &  & \OK &  &  &  & \OK &  & \OK &  & \OK &  &\\
 
@@ -248,11 +256,15 @@ check if its $n_i$ is decreased to 0 or not. If $n_i$ of a sensor node is 0 (i.e
 
 & \tiny  L. Zhang et al. (2013)~\cite{ref136} & \OK &   & \OK &   &  & \OK & \OK &  & \OK &  & \OK &  &\\
 
-& \tiny   S. He et al. (2012)~\cite{ref137}   & \OK & \OK  & \OK  &   &  &  & \OK &  & \OK &  &  &  &\\
+& \tiny  S. He et al. (2012)~\cite{ref137}   & \OK & \OK  & \OK  &   &  &  & \OK &  & \OK &  &  &  &\\
 
-& \tiny  Y. Xu et al. (2001)~\cite{ref84}   & \OK &   & \OK  &   &  &  & \OK &  & \OK &  &  &  &\\
+& \tiny  Y. Xu et al. (2001)~\cite{GAF}   & \OK &   & \OK  &   &  &  & \OK &  & \OK &  &  &  &\\
 
-& \tiny  C. Vu et al. (2006)~\cite{ref132}  & \OK &   & \OK &  & \OK &  & \OK &  & \OK &  & \OK &  &\\
+& \tiny  C. Vu et al. (2006)~\cite{DESK}  & \OK &   & \OK &  & \OK &  & \OK &  & \OK &  & \OK &  &\\
+
+& \tiny  X. Deng et al. (2012)~\cite{ref160}  & \OK &   & \OK &  &  &  & \OK &  & \OK &  &  &  &\\
+
+& \tiny  X. Deng et al. (2005)~\cite{ref133}  & \OK &   & \OK &  & \OK &  & \OK &  & \OK &  &  &  &\\
 
 &\textbf{\textcolor{red}{ \tiny DiLCO Protocol (2014)}}                  &  \textbf{\textcolor{red}{\OK}}   &   & \textbf{\textcolor{red}{\OK}}   &   &   & \textbf{\textcolor{red}{\OK}}  & \textbf{\textcolor{red}{\OK}}   &   &  &   &\textbf{\textcolor{red}{\OK}}  &    &  \\
 
@@ -271,6 +283,7 @@ check if its $n_i$ is decreased to 0 or not. If $n_i$ of a sensor node is 0 (i.e
 
 
 
+
 \section{Conclusion}
 \label{ch2:sec:05}
 This chapter has been described some coverage problems proposed in the literature, and their assumptions and proposed solutions.
