This dissertation mainly focuses on the area coverage problem, where the ultimate goal is to choose the minimum number of sensor nodes to cover the whole sensing field.
%We have focused mainly on the area coverage problem. Therefore, we represent the sensing area of each sensor node in the sensing field as a set of primary points and then achieving full area coverage by covering all the points in the sensing field. The ultimate goal of the area coverage problem is to choose the minimum number of sensor nodes to cover the whole sensing region and prolonging the lifetime of the WSN.
-Many centralized and distributed coverage algorithms for activity scheduling have been proposed in the literature, based on different assumptions and objectives. In centralized algorithms, a central controller makes all decisions and distributes the results to sensor nodes. The centralized algorithms have the advantage of requiring very low processing power from the sensor nodes which have usually limited processing capabilities. On the contrary, the exchange of packets in large WSNs may consume a considerable amount of energy in a centralized approach compared to a distributed one. Moreover, centralized approaches usually suffer from the scalability and reliability problems, making them less competitive than the network size increases.
+Many centralized and distributed coverage algorithms for activity scheduling have been proposed in the literature, based on different assumptions and objectives. In centralized algorithms, a central controller (base station) makes all decisions and distributes the results to sensor nodes. The centralized algorithms have the advantage of requiring very low processing power from the sensor nodes (except for the base station) which have usually limited processing capabilities. On the contrary, the exchange of packets in large WSNs may consume a considerable amount of energy in a centralized approach compared to a distributed one. The exchange of packets is between the sensor nodes and the base station. The centralized algorithms ensure nearly or close to optimal solution . They provide less redundant active sensor nodes during monitoring the sensing field. Moreover, centralized approaches usually suffer from the scalability and reliability problems, making them less competitive than the network size increases.
-In distributed algorithms, on the other hand, the decision process is localized in each individual sensor node, and only informations 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 the energy consumption for computation is increased. Overall, distributed algorithms are more suitable for large-scale networks, but it can not give an optimal (or near-optimal) solution based only on local informations. Moreover, a recent study conducted in \cite{ref226} concludes that there is a threshold in terms of network size to switch from a distributed to a centralized algorithm. Table~\ref{Table0:ch2} shows a comparison between centralized coverage algorithms and distributed coverage algorithms.
+In distributed algorithms, on the other hand, the decision process is localized in each individual sensor node, and only informations from neighboring nodes are used for the activity decision. Overall, distributed algorithms are more suitable for large-scale networks, but it can not give an optimal (or near-optimal) solution based only on local informations. They provide more redundant active sensor nodes during monitoring the sensing field. The exchange of packets is between the sensor nodes and their neighbors. Distributed algorithms are more robust against sensor failure. Moreover, a recent study conducted in \cite{ref226} concludes that there is a threshold in terms of network size to switch from a distributed to a centralized algorithm.
+
+%%Table~\ref{Table0:ch2} shows a comparison between centralized coverage algorithms and distributed coverage algorithms.
+
+
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+\iffalse
\begin{table}[h!]
\caption{Centralized Coverage Algorithms vs Distributed Coverage Algorithms}
\label{Table0:ch2}
\end{table}
+\fi
-In this dissertation, the sensing field is divided into smaller subregions using divide-and-conquer method. The division continues until the distance between two sensors inside the subregion is 3 or 2 hops maximum. This division makes our protocols more scalable for large networks, less energy consumer for communication and processing, and more reliable against network failure. Our proposed protocols are distributed on the sensor nodes of the subregions. The protocols in each subregion work in independent and simultaneous way with the protocols in the other subregions. If the network is disconnected in one subregion, it will not affect the other subregions of the sensing field. There is no fixed sensor node in the subregion for executing the optimization algorithm. Each period of the network lifetime, the sensor nodes in the subregion cooperate in order to select a sensor node to execute the optimization algorithm according to predefined priority metrics. The resulting local optimal schedule from optimization algorithm is applied within the subregion. The elected sensor node sends a packet to every sensor within the subregion to inform him to stay active or sleep during this period. Each optimization algorithm in a subregion provides locally an optimal solution, so the solution for the whole sensing field is near-optimal.
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
+
+In this dissertation, the sensing field is divided into smaller subregions using a divide-and-conquer method. The division continues until the distance between two sensors inside the subregion is 3 or 2 hops maximum. This division makes our protocols more scalable for large networks, less energy consumer for communication and processing, and more reliable against network failure. Our proposed protocols are distributed on the sensor nodes of the subregions. The protocols in each subregion work in independent and simultaneous way with the protocols in the other subregions. If the network is disconnected in one subregion, it will not affect the other subregions of the sensing field. There is no fixed sensor node in the subregion for executing the optimization algorithm. Each period of the network lifetime, the sensor nodes in the subregion cooperate in order to select a sensor node to execute the optimization algorithm according to predefined priority metrics. The resulting local optimal schedule from optimization algorithm is applied within the subregion. The elected sensor node sends a packet to every sensor within the subregion to inform him to stay active or sleep during this period. Each optimization algorithm in a subregion provides locally an optimal solution, so the solution for the whole sensing field is near-optimal.
Several algorithms to maintain the coverage and maximize the network lifetime were proposed in~\cite{ref113,ref101,ref103,ref105}.
Table \ref{x11} summarizes the main characteristics of some coverage approaches in previous literatures.
-\begin{table}[h!]
-
-\begin{flushleft}
-\centering
-\caption{Main characteristics of some coverage approaches in literature.}
-\label{x11}
- \begin{tabular}{@{} cl*{13}c @{}}
- & & \\
- & & \multicolumn{10}{c}{Characteristics} \\[2ex]
- \multicolumn{2}{c}{\footnotesize Coverage Approach} & \mcrot{1}{l}{50}{\footnotesize Distributed} & \mcrot{1}{l}{50}{\footnotesize Centralized} & \mcrot{1}{l}{50}{ \footnotesize Area coverage} & \mcrot{1}{l}{50}{\footnotesize Target coverage} & \mcrot{1}{l}{50}{\footnotesize k-coverage} & \mcrot{1}{l}{50}{\footnotesize Heterogeneous nodes}& \mcrot{1}{l}{50}{\footnotesize Homogeneous nodes} & \mcrot{1}{l}{50}{\footnotesize Disjoint sets} & \mcrot{1}{l}{50}{\footnotesize Non-Disjoint sets} & \mcrot{1}{l}{50}{\footnotesize SET K-COVER } & \mcrot{1}{l}{50}{\footnotesize Work in Rounds or Periods} & \mcrot{1}{l}{50}{\footnotesize Adjustable Radius} \\
- \cmidrule[1pt]{2-14}
-
-
-& \tiny Z. Abrams et al. (2004)~\cite{ref114} & \OK &\OK & \OK & & & &\OK & \OK & & \OK & & &\\
-
-& \tiny M. Cardei and D. Du (2005)~\cite{ref115} & & \OK & & \OK & & & \OK & \OK & & \OK & & &\\
-
-& \tiny S. Slijepcevic and M. Potkonjak (2001)~\cite{ref116} & & \OK & \OK & & & & \OK & \OK & & \OK & & &\\
-
-& \tiny Manjun and A. K. Pujari (2011)~\cite{ref117} & & \OK & & \OK & & & \OK & & \OK & & & &\\
-
-& \tiny M. Yang and J. Liu (2014)~\cite{ref118} & & \OK & \OK & & & & \OK & & \OK & & & & \\
-
-& \tiny S. Wang et al. (2010)~\cite{ref144} & & \OK & \OK & & & & \OK & & \OK & & \OK & & \\
-
-& \tiny C. Lin et al. (2010)~\cite{ref147} & & \OK & \OK & & & & \OK & & \OK & & & & \\
-
-& \tiny S. A. R. Zaidi et al. (2009)~\cite{ref148} & & \OK & \OK & & & & \OK & & \OK & & & & \\
-
-& \tiny Y. Li et al. (2011)~\cite{ref142} & & \OK & \OK & & & \OK & \OK & \OK & & \OK & & \OK &\\
-
-& \tiny H. M. Ammari and S. K. Das (2012)~\cite{ref152} & \OK & \OK & \OK & & \OK & & \OK & & \OK & & \OK & &\\
-
-& \tiny L. Liu et al. (2010)~\cite{ref150} & & \OK & & \OK & & \OK & & \OK & & \OK & & &\\
-
-& \tiny H. Cheng et al. (2014)~\cite{ref119} & & \OK & \OK & & & & \OK & & \OK & & & &\\
-
-& \tiny M. Rebai et al. (2014)~\cite{ref141} & & \OK & \OK & & & & \OK & & \OK & & & &\\
-
-& \tiny L. Aslanyan et al. (2013)~\cite{ref151} & & \OK & \OK & & & & \OK & & \OK & \OK & \OK & &\\
-
-& \tiny X. Liu et al. (2014)~\cite{ref143} & & \OK & \OK & & & & \OK & & \OK & \OK & \OK & &\\
-
-& \tiny F. Castano et al. (2013)~\cite{ref120} & & \OK & & \OK & & & \OK & & \OK & \OK & & &\\
-
-& \tiny A. Rossi et al. (2012)~\cite{ref121} & & \OK & & \OK & & \OK & \OK & & \OK & \OK & & \OK &\\
-
-& \tiny K. Deschinkel et al. (2012)~\cite{ref122} & & \OK & & \OK & & & \OK & & \OK & \OK & & &\\
-
-
-
-& \tiny A. Gallais et al. (2008)~\cite{ref123} & \OK & & \OK & & & \OK & \OK & & \OK & & \OK & \OK &\\
-
-& \tiny D. Tian and N. D. Georganas (2002)~\cite{ref124} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
-
-& \tiny F. Ye et al. (2003)~\cite{ref125} & \OK & & \OK & & & & \OK & & \OK & & & &\\
-
-& \tiny H. Zhang and J. C. Hou (2005)~\cite{ref126} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
-
-& \tiny W. B. Heinzelman et al. (2002)~\cite{ref109} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
-
-& \tiny T. Yardibi and E. Karasan (2010)~\cite{ref127} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
-
-& \tiny S. K. Prasad and A. Dhawan (2007)~\cite{ref128} & \OK & & & \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 & &\\
-
-& \tiny J. Lu and T. Suda (2003)~\cite{ref131} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
-
-
-
-& \tiny J. Cho et al. (2007)~\cite{ref145} & \OK & & \OK & & & & \OK & & \OK & & & &\\
-
-& \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}}
-
-& \tiny D. Dong et al. (2012)~\cite{ref149} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
-
-& \tiny B. Wang et al. (2012)~\cite{ref134} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
-
-& \tiny Z. Liu et al. (2012)~\cite{ref135} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
-
-& \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 Y. Xu et al. (2001)~\cite{GAF} & \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}} & &\textbf{\textcolor{red}{\OK}} & & \\
-
-&\textbf{\textcolor{red}{ \tiny MuDiLCO Protocol (2014)}} & \textbf{\textcolor{red}{\OK}} & & \textbf{\textcolor{red}{\OK}} & & & \textbf{\textcolor{red}{\OK}} & \textbf{\textcolor{red}{\OK}} & & \textbf{\textcolor{red}{\OK}} & \textbf{\textcolor{red}{\OK}} &\textbf{\textcolor{red}{\OK}} & & \\
-
-&\textbf{\textcolor{red}{ \tiny PeCO Protocol (2015)}} & \textbf{\textcolor{red}{\OK}} & & \textbf{\textcolor{red}{\OK}} & & & \textbf{\textcolor{red}{\OK}} & \textbf{\textcolor{red}{\OK}} & & \textbf{\textcolor{red}{\OK}} & &\textbf{\textcolor{red}{\OK}} & & \\
-
- \cmidrule[1pt]{2-14}
- \end{tabular}
- \end{flushleft}
-
-
-
-\end{table}
%Their solutions can be translated to distributed protocols to solve the coverage problem.
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}.
-Cho et al.~\cite{ref145} propose a distributed node scheduling protocol, which can retain sensing coverage needed by applications and increases network lifetime via putting in sleep mode some redundant nodes. In this work, the Effective Sensing Area (ESA) concept of a sensor node is used, which refers to the sensing area that is not overlapping with another sensor's sensing area. A sensor node can determine whether it will be active or turned off by computing its ESA. The suggested work permits sensor nodes to be in sleep mode opportunistically whilst fulfilling the needed sensing coverage. The authors in~\cite{ref146} define a Maximum Sensing Coverage Region problem (MSCR) in WSNs and then propose a distributed algorithm to solve it. The maximum observed area is fully covered by a minimum active sensors. In this work, the major property is to get rid of the redundant sensors in high-density WSNs and putting them in sleep mode. In addition, a smaller number of active sensors is chosen so as to ensure the full area is k-covered, and all events appearing in that area can be precisely and timely detected. This algorithm minimizes the total energy consumption and increases the network lifetime. 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 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.
+Cho et al.~\cite{ref145} propose a distributed node scheduling protocol, which can retain sensing coverage needed by applications and increases network lifetime via putting in sleep mode some redundant nodes. In this work, the Effective Sensing Area (ESA) concept of a sensor node is used, which refers to the sensing area that is not overlapping with another sensor's sensing area. A sensor node can determine whether it will be active or turned off by computing its ESA. The suggested work permits sensor nodes to be in sleep mode opportunistically whilst fulfilling the needed sensing coverage.
+Bahi et al. \cite{ref236,ref237} propose a distributed localisation algorithm and a scheduling method to maintain the coverage and improve the network lifetime. They suggest a mobile beacon to divide the area of interest into unit squares using Hilbert space filling curve method. They exploit the localization phase to construct sets of active nodes. They provide a local activity scheduling approach for the sensor nodes in the region to ensure the area coverage and to prolong the network lifetime. The experiment results show an improvement in the network lifetime due to reducing the energy consumed by the localisation and coverage algorithms.
+The authors in~\cite{ref146} define a Maximum Sensing Coverage Region problem (MSCR) in WSNs and then propose a distributed algorithm to solve it. The maximum observed area is fully covered by a minimum active sensors. In this work, the major property is to get rid of the redundant sensors in high-density WSNs and putting them in sleep mode. In addition, a smaller number of active sensors is chosen so as to ensure the full area is k-covered, and all events appearing in that area can be precisely and timely detected. This algorithm minimizes the total energy consumption and increases the network lifetime. 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 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.
A graph theoretical framework for connectivity-based area coverage with configurable coverage granularity is proposed~\cite{ref149}. A new coverage criterion and scheduling approach is proposed based on cycle partition. This method is able to build a sparse coverage set in distributed way by means of only connectivity information. This work only considers that the communication range of the sensor is two times smaller than the sensing one. Shibo et al.~\cite{ref137} express the area coverage problem as a minimum weight submodular set cover problem and propose 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. The authors in \cite{ref160} design an energy-efficient approach to area coverage problems by transforming the area coverage problem to the target coverage problem taking into account the intersection points among disks of sensors nodes or between disc of sensor nodes and boundaries. They propose two mechanisms for the converted target coverage problems to produce cover sets covering the sensing
field completely. Simulations results show that this approach can prolong the lifetime of the network compared with other works.
The works presented in~\cite{ref134,ref135,ref136} focus on coverage-aware, distributed energy-efficient, and distributed clustering methods respectively, which aim at extending the network lifetime, while the coverage is ensured.
-In this dissertation, we focus in more details on two distributed coverage algorithms: GAF and DESK, because we compared our proposed coverage optimization protocols with them during performance evaluation. GAF algorithm is chose for comparison as competitor because it is famous, fast, and easy to implement, as well as many authors referred to it in many publications. In addition, DESK algorithm is also selected as competitor in the comparison because it is a full distributed coverage approach.
+In this dissertation, we focus in more details on two distributed coverage algorithms: GAF and DESK, because we compared our proposed coverage optimization protocols with them during performance evaluation. GAF algorithm is chosen for comparison as a competitor because it is famous and easy to implement, as well as many authors referred to it in many publications. DESK algorithm is also selected as competitor in the comparison because it works into rounds fashion (network lifetime divides into rounds) similar to our approaches, as well as DESK is a full distributed coverage approach.
\subsection{Geographical Adaptive Fidelity (GAF)}
%The period the average
+\begin{table}[H]
+
+\begin{flushleft}
+\centering
+\caption{Main characteristics of some coverage approaches in literature.}
+\label{x11}
+ \begin{tabular}{@{} cl*{13}c @{}}
+ & & \\
+ & & \multicolumn{10}{c}{Characteristics} \\[2ex]
+ \multicolumn{2}{c}{\footnotesize Coverage Approach} & \mcrot{1}{l}{50}{\footnotesize Distributed} & \mcrot{1}{l}{50}{\footnotesize Centralized} & \mcrot{1}{l}{50}{ \footnotesize Area coverage} & \mcrot{1}{l}{50}{\footnotesize Target coverage} & \mcrot{1}{l}{50}{\footnotesize k-coverage} & \mcrot{1}{l}{50}{\footnotesize Heterogeneous nodes}& \mcrot{1}{l}{50}{\footnotesize Homogeneous nodes} & \mcrot{1}{l}{50}{\footnotesize Disjoint sets} & \mcrot{1}{l}{50}{\footnotesize Non-Disjoint sets} & \mcrot{1}{l}{50}{\footnotesize SET K-COVER } & \mcrot{1}{l}{50}{\footnotesize Work in Rounds or Periods} & \mcrot{1}{l}{50}{\footnotesize Adjustable Radius} \\
+ \cmidrule[1pt]{2-14}
+
+
+%& \tiny Z. Abrams et al. (2004)~\cite{ref114} & \OK &\OK & \OK & & & &\OK & \OK & & \OK & & &\\
+
+%& \tiny M. Cardei and D. Du (2005)~\cite{ref115} & & \OK & & \OK & & & \OK & \OK & & \OK & & &\\
+
+& \tiny S. Slijepcevic and M. Potkonjak (2001)~\cite{ref116} & & \OK & \OK & & & & \OK & \OK & & \OK & & &\\
+
+& \tiny Manjun and A. K. Pujari (2011)~\cite{ref117} & & \OK & & \OK & & & \OK & & \OK & & & &\\
+
+& \tiny M. Yang and J. Liu (2014)~\cite{ref118} & & \OK & \OK & & & & \OK & & \OK & & & & \\
+
+& \tiny S. Wang et al. (2010)~\cite{ref144} & & \OK & \OK & & & & \OK & & \OK & & \OK & & \\
+
+& \tiny C. Lin et al. (2010)~\cite{ref147} & & \OK & \OK & & & & \OK & & \OK & & & & \\
+
+& \tiny S. A. R. Zaidi et al. (2009)~\cite{ref148} & & \OK & \OK & & & & \OK & & \OK & & & & \\
+
+& \tiny Y. Li et al. (2011)~\cite{ref142} & & \OK & \OK & & & \OK & \OK & \OK & & \OK & & \OK &\\
+
+& \tiny H. M. Ammari and S. K. Das (2012)~\cite{ref152} & \OK & \OK & \OK & & \OK & & \OK & & \OK & & \OK & &\\
+
+& \tiny L. Liu et al. (2010)~\cite{ref150} & & \OK & & \OK & & \OK & & \OK & & \OK & & &\\
+
+& \tiny H. Cheng et al. (2014)~\cite{ref119} & & \OK & \OK & & & & \OK & & \OK & & & &\\
+
+& \tiny M. Rebai et al. (2014)~\cite{ref141} & & \OK & \OK & & & & \OK & & \OK & & & &\\
+
+& \tiny L. Aslanyan et al. (2013)~\cite{ref151} & & \OK & \OK & & & & \OK & & \OK & \OK & \OK & &\\
+
+& \tiny X. Liu et al. (2014)~\cite{ref143} & & \OK & \OK & & & & \OK & & \OK & \OK & \OK & &\\
+
+& \tiny F. Castano et al. (2013)~\cite{ref120} & & \OK & & \OK & & & \OK & & \OK & \OK & & &\\
+
+& \tiny A. Rossi et al. (2012)~\cite{ref121} & & \OK & & \OK & & \OK & \OK & & \OK & \OK & & \OK &\\
+
+& \tiny K. Deschinkel et al. (2012)~\cite{ref122} & & \OK & & \OK & & & \OK & & \OK & \OK & & &\\
+
+
+
+& \tiny A. Gallais et al. (2008)~\cite{ref123} & \OK & & \OK & & & \OK & \OK & & \OK & & \OK & \OK &\\
+
+& \tiny D. Tian and N. D. Georganas (2002)~\cite{ref124} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
+
+& \tiny F. Ye et al. (2003)~\cite{ref125} & \OK & & \OK & & & & \OK & & \OK & & & &\\
+
+& \tiny H. Zhang and J. C. Hou (2005)~\cite{ref126} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
+
+& \tiny W. B. Heinzelman et al. (2002)~\cite{ref109} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
+
+& \tiny T. Yardibi and E. Karasan (2010)~\cite{ref127} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
+
+& \tiny S. K. Prasad and A. Dhawan (2007)~\cite{ref128} & \OK & & & \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 & &\\
+
+& \tiny J. Lu and T. Suda (2003)~\cite{ref131} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
+
+
+
+& \tiny J. Cho et al. (2007)~\cite{ref145} & \OK & & \OK & & & & \OK & & \OK & & & &\\
+
+& \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}}
+
+& \tiny D. Dong et al. (2012)~\cite{ref149} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
+
+& \tiny B. Wang et al. (2012)~\cite{ref134} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
+
+& \tiny Z. Liu et al. (2012)~\cite{ref135} & \OK & & \OK & & & & \OK & & \OK & & \OK & &\\
+
+& \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 Y. Xu et al. (2001)~\cite{GAF} & \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}} & &\textbf{\textcolor{red}{\OK}} & & \\
+
+&\textbf{\textcolor{red}{ \tiny MuDiLCO Protocol (2014)}} & \textbf{\textcolor{red}{\OK}} & & \textbf{\textcolor{red}{\OK}} & & & \textbf{\textcolor{red}{\OK}} & \textbf{\textcolor{red}{\OK}} & & \textbf{\textcolor{red}{\OK}} & \textbf{\textcolor{red}{\OK}} &\textbf{\textcolor{red}{\OK}} & & \\
+
+&\textbf{\textcolor{red}{ \tiny PeCO Protocol (2015)}} & \textbf{\textcolor{red}{\OK}} & & \textbf{\textcolor{red}{\OK}} & & & \textbf{\textcolor{red}{\OK}} & \textbf{\textcolor{red}{\OK}} & & \textbf{\textcolor{red}{\OK}} & &\textbf{\textcolor{red}{\OK}} & & \\
+
+ \cmidrule[1pt]{2-14}
+ \end{tabular}
+ \end{flushleft}
+
+
+
+\end{table}
\label{ch2:sec:05}
This chapter describes some coverage problems in the literature, with their assumptions and proposed solutions.
The coverage is considered as an essential requirement for many applications in WSNs because the better the coverage of an area of interest is, the better the sensing measurements of the physical phenomenon also is. Therefore, many extensive researches have been focused on that problem. These researches aim at designing mechanisms that efficiently manage or schedule the sensors after deployment, since sensor nodes have a limited battery life.
-Many coverage algorithms for maintaining the coverage and improving the network lifetime in WSNs were proposed. On the one hand, the full centralized coverage algorithms provide optimal or near optimal solution with low computation power but they deplete the battery power due to the communication overhead, as well as they are not scalable for large size networks. On the other hand, the distributed coverage algorithms provide a lower quality solution in comparison with centralized approaches and consume more power for computation but they are reliable, scalable, and provide low communication overhead.
-As shown in table \ref{Table0:ch2}, each of the two coverage approaches has advantages and disadvantages. Therefore, each approach can be used based on the application requirements. We conclude from this chapter that it is desirable to introduce an hybrid approach to take into account the advantages of both centralized and distributed coverage approaches. Such an hybrid approach can provide a good quality coverage and prolong the network lifetime.
+Many coverage algorithms for maintaining the coverage and improving the network lifetime in WSNs were proposed. On the one hand, the full centralized coverage algorithms provide optimal or near optimal solution with low computation power for the sensors (except for the base station) but they deplete the battery power due to the communication overhead, so they are not scalable for large size networks. On the other hand, the distributed coverage algorithms provide a lower quality solution in comparison with centralized approaches and the communication between neighbors may be large especially for dense networks. Distributed coverage algorithms are reliable and scalable. The two coverage approaches has advantages and disadvantages. Therefore, each approach can be used based on the application requirements. We conclude from this chapter that it is desirable to introduce an hybrid approach to take into account the advantages of both centralized and distributed coverage approaches. Such an hybrid approach can provide a good quality coverage and prolong the network lifetime.
WSN is an ad hoc wireless networks, which consists of a large number of wireless cheap devices called sensors. A sensor node can perform communication, sensing, processing, and storage tasks with a limited capability. It can be used by human to monitor physical phenomena remotely and without any outside intervention. Wireless sensor nodes are self-contained units equipped with a radio transceiver, a microcontroller, a small memory, and a power source, usually a battery. These sensor nodes are cooperating together autonomously to perform the assigned tasks. The distributed self-organization and self-configuration capabilities of wireless sensor nodes enable myriad applications for monitoring, sensing and controlling the physical world.
-The sensor nodes have several limitations, such as the power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to the physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions are taking these constraints into account have been proposed. Sensor nodes are battery-powered without means of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. %Since batteries are the most important limited resource inside sensor nodes, it is desirable that WSNs are deployed with high densities so as to exploit the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase to prolong the network lifetime.
+The sensor nodes have several limitations, such as the power source, processing capability, bandwidth, uncertainty of sensed data, and the vulnerability of sensor nodes to the physical world. These limitations have been tackled by many researchers during the last years, and consequently, many solutions taking these constraints into account have been proposed. Sensor nodes are battery-powered without means of recharging or replacing, usually due to environmental (hostile or unpractical environments) or cost reasons. %Since batteries are the most important limited resource inside sensor nodes, it is desirable that WSNs are deployed with high densities so as to exploit the overlapping sensing regions of some sensor nodes to save energy by turning off some of them during the sensing phase to prolong the network lifetime.
Since the network lifetime depends on sensor lifetime, the power depletion represents the most significant part when designing of the WSN protocols due to the limited capacity of the sensor batteries. The major goal is to extend the network lifetime, taking into consideration the energy source limitations. Several energy-efficient approaches have been suggested to minimize the energy consumption and extend the network lifetime during monitoring a certain area by a WSN. %For example, one of the ways is to turn off the redundant sensors and put them in sleep mode to maintain the energy, whilst the active sensors perform the sensing coverage task during their life.
Specifically, the energy-efficient protocols proposed in this dissertation focus on the area coverage problem in WSNs. The major goal of the area coverage problem is to ensure monitoring the entire sensing field for as long as possible. The area coverage problem is closely related to the performance of WSNs in many applications, such as monitoring a battlefield, target detection, tracking, personal protection, animal habit monitoring, and homeland security.
Although many works on energy-efficient coverage have been introduced, there is still need for a protocol which can schedule sensor nodes in an efficient way with: a minimum number of active sensors and less communication overhead so as to maintain the coverage and extend the network lifetime as long as possible. The main question is how to reduce the redundancy while maintaining a good coverage with minimum energy consumption?
-
+\iffalse
\section*{3. The Objective of this Dissertation}
\addcontentsline{toc}{section}{3. The Objective of this Dissertation}
The primary objective of this dissertation is to develop energy-efficient distributed optimization protocols in wireless sensor networks that optimize both coverage and network lifetime. The developed protocols should schedule node activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime.
election and sensor activity scheduling based optimization, where the challenges include how to select the most efficient leader and the best representative active nodes which take the mission of monitoring during the current period.
In addition, the developed optimization protocols should be able to perform a distributed optimization process, by subdividing into subregions the region of interest where the sensor nodes in each subregion collaborate to select the leader which execute the optimization algorithm.
-
+\fi
-\section*{4. Main Contributions of this Dissertation}
+\section*{3. Main Contributions of this Dissertation}
\addcontentsline{toc}{section}{4. Main Contributions of this Dissertation}
%The coverage problem in WSNs is becoming more and more important for many applications ranging from military applications such as battlefield surveillance to the civilian applications such as health-care surveillance and habitant monitoring.
The main contributions in this dissertation concentrate on designing distributed optimization protocols to extend the lifetime of WSNs. We summarize the main contributions of our research as follows:
%\item We devise a framework to schedule nodes to be activated alternatively such that the network lifetime is prolonged while ensuring that a certain level of coverage is preserved. A key idea in our framework is to exploit the spatial-temporal subdivision. On the one hand, the area of interest is divided into several smaller subregions and, on the other hand, the timeline is divided into periods of equal length. In each subregion, the sensor nodes will cooperatively choose a leader which will schedule nodes' activities, and this grouping of sensors is similar to typical cluster architecture.
-\item We have designed a third protocol, called Perimeter-based Coverage Optimization (PeCO).
+\item We design a third protocol, called Perimeter-based Coverage Optimization (PeCO).
%which schedules nodes’ activities (wake up and sleep stages) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. This protocol is applied in a distributed way in regular subregions obtained after partitioning the area of interest in a preliminary step. It works in periods and is based on the resolution of an integer program to select the subset of sensors operating in active status for each period.
We have proposed a new mathematical optimization model. Instead of trying to cover a set of specified points/targets as in my previous protocols and most of the methods proposed in the literature, we formulate an integer program based on perimeter coverage of each sensor. The model involves integer variables to capture the deviations between the actual level of coverage and the required level. The idea is that an optimal scheduling will be obtained by minimizing a weighted sum of these deviations. This contribution is demonstrated in chapter 6.
% \section{ Refereed Journal and Conference Publications}
-\section*{5. Dissertation Outline}
+\section*{4. Dissertation Outline}
\addcontentsline{toc}{section}{5. Dissertation Outline}
The dissertation is organized as follows: the next chapter presents a scientific background about wireless sensor networks. Chapter 2 states a review of the related literatures to the coverage problem in WSNs, prior works and current works. Evaluation tools and optimization solvers are investigated in chapter 3. Chapter 4 describes the proposed DiLCO protocol, while chapter 5 and 6 respectively present the MuDiLCO and PeCO protocols. Finally, we conclude our work in chapter 7.
