we concentrate on the area coverage problem, with the objective of maximizing
the network lifetime by using an optimized multiround scheduling.
-We study the problem of designing an energy-efficient optimization algorithm that divides the sensors in a WSN into multiple cover sets such that the area of interest is monitored as long as possible. Providing multiple cover sets can be used to improve the energy efficiency of WSNs. Therefore, in order to increase the longevity of the WSN and conserve the energy, it can be useful to provide multiple cover sets in one time after that schedule them for multiple rounds, so that the battery life of a sensor is not wasted due to the repeated execution of the coverage optimization algorithm, as well as the information exchange and leader election.
+We study the problem of designing an energy-efficient optimization algorithm that divides the sensor nodes in a WSN into multiple cover sets such that the area of interest is monitored as long as possible. Providing multiple cover sets can be used to improve the energy efficiency of WSNs. Therefore, in order to increase the longevity of the WSN and conserve the energy, it can be useful to provide multiple cover sets in one time after that schedule them for multiple rounds, so that the battery life of a sensor is not wasted due to the repeated execution of the coverage optimization algorithm, as well as the information exchange and leader election.
+
+The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization protocol) presented in this chapter is an extension of the approach introduced in chapter 4. Simulation results have shown that it was more interesting to divide the area into several subregions, given the computation complexity. Compared to our protocol in chapter 4, in this one we study the possibility of dividing the sensing phase into multiple rounds. In fact, in this chapter we make a multiround optimization while it was a single round optimization in our protocol in chapter 4.
-The MuDiLCO protocol (for Multiround Distributed Lifetime Coverage Optimization protocol) presented in this chapter is an extension of the approach introduced in chapter 4. Simulation results have shown that it was more interesting to divide the area into several subregions, given the computation complexity. Compared to our protocol in chapter 4, in this one we study the possibility of dividing the sensing phase into multiple rounds. In fact, in this chapter we make a multiround optimization, while it was a single round optimization in our protocol in chapter 4.
The remainder of the chapter continues with section \ref{ch5:sec:02} where a detail of MuDiLCO Protocol is presented. The next section describes the Primary Points based Multiround Coverage Problem formulation which is used to schedule the activation of sensors in T cover sets. Section \ref{ch5:sec:04} shows the simulation
results. The chapter ends with a conclusion and some suggestions for further work.
-
-
-
-
\section{MuDiLCO Protocol Description}
\label{ch5:sec:02}
\noindent In this section, we introduce the MuDiLCO protocol which is distributed on each subregion in the area of interest. It is based on two energy-efficient
\chapter{Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}
\label{ch6}
+\iffalse
\section{Summary}
\label{ch6:sec:01}
OMNeT++, the discrete event simulator, to demonstrate that PeCO can
offer longer lifetime coverage for WSNs in comparison with some other protocols.
+
+\fi
+
+
+\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 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 methods are used to find out the redundant node such as Voronoi diagram method, sponsored sector, crossing coverage, and perimeter coverage.
+
+
+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}
\section{Conclusion}
-\label{ch6:sec:04}
+\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
\contentsline {subsection}{\numberline {5.4.3}Results Analysis and Comparison }{99}{subsection.5.4.3}
\contentsline {section}{\numberline {5.5}Conclusion}{104}{section.5.5}
\contentsline {chapter}{\numberline {6}Perimeter-based Coverage Optimization to Improve Lifetime in Wireless Sensor Networks}{107}{chapter.6}
-\contentsline {section}{\numberline {6.1}Summary}{107}{section.6.1}
-\contentsline {section}{\numberline {6.2}The PeCO Protocol Description}{107}{section.6.2}
-\contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{107}{subsection.6.2.1}
-\contentsline {subsection}{\numberline {6.2.2}The Main Idea}{110}{subsection.6.2.2}
+\contentsline {section}{\numberline {6.1}Introduction}{107}{section.6.1}
+\contentsline {section}{\numberline {6.2}The PeCO Protocol Description}{108}{section.6.2}
+\contentsline {subsection}{\numberline {6.2.1}Assumptions and Models}{108}{subsection.6.2.1}
+\contentsline {subsection}{\numberline {6.2.2}The Main Idea}{109}{subsection.6.2.2}
\contentsline {subsection}{\numberline {6.2.3}PeCO Protocol Algorithm}{111}{subsection.6.2.3}
-\contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{113}{section.6.3}
+\contentsline {section}{\numberline {6.3}Perimeter-based Coverage Problem Formulation}{112}{section.6.3}
\contentsline {section}{\numberline {6.4}Performance Evaluation and Analysis}{114}{section.6.4}
\contentsline {subsection}{\numberline {6.4.1}Simulation Settings}{114}{subsection.6.4.1}
\contentsline {subsection}{\numberline {6.4.2}Simulation Results}{115}{subsection.6.4.2}
\contentsline {subsubsection}{\numberline {6.4.2.1}Coverage Ratio}{115}{subsubsection.6.4.2.1}
-\contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{115}{subsubsection.6.4.2.2}
-\contentsline {subsubsection}{\numberline {6.4.2.3}The Energy Consumption}{117}{subsubsection.6.4.2.3}
+\contentsline {subsubsection}{\numberline {6.4.2.2}Active Sensors Ratio}{116}{subsubsection.6.4.2.2}
+\contentsline {subsubsection}{\numberline {6.4.2.3}The Energy Consumption}{116}{subsubsection.6.4.2.3}
\contentsline {subsubsection}{\numberline {6.4.2.4}The Network Lifetime}{117}{subsubsection.6.4.2.4}
-\contentsline {section}{\numberline {6.5}Conclusion}{117}{section.6.5}
+\contentsline {section}{\numberline {6.5}Conclusion}{118}{section.6.5}
\contentsline {part}{III\hspace {1em}Conclusion and Perspectives}{121}{part.3}
\contentsline {chapter}{\numberline {7}Conclusion and Perspectives}{123}{chapter.7}
\contentsline {section}{\numberline {7.1}Conclusion}{123}{section.7.1}
title = {Guide to Wireless Sensor Networks},
publisher = {Springer-Verlag London Limited},
year = {2009},
-}
\ No newline at end of file
+}
+
+
+@article{ref223,
+ title={Wireless sensor networks: applications and challenges of ubiquitous sensing},
+ author={Puccinelli, Daniele and Haenggi, Martin},
+ journal={Circuits and Systems Magazine, IEEE},
+ volume={5},
+ number={3},
+ pages={19--31},
+ year={2005},
+ publisher={IEEE}
+}
+
+@inproceedings{ref224,
+ title={Probabilistic coverage in wireless sensor networks},
+ author={Ahmed, Nadeem and Kanhere, Salil S and Jha, Sanjay},
+ booktitle={Local Computer Networks, 2005. 30th Anniversary. The IEEE Conference on},
+ pages={8--pp},
+ year={2005},
+ organization={IEEE}
+}
+
+@inproceedings{ref225,
+ title={Incremental Model for Complete Area Coverage in Wireless Sensor Networks},
+ author={Sethi, Hemendra Kumar and Dash, Sasmita and Lenka, Manas Ranjan and Swain, Amulya Ratna},
+ booktitle={Computational Intelligence and Networks (CINE), 2015 International Conference on},
+ pages={92--97},
+ year={2015},
+ organization={IEEE}
+}
\ No newline at end of file
