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25 %\title{Authors' Instructions \subtitle{Preparation of Camera-Ready Contributions to SCITEPRESS Proceedings} }
27 \title{Distributed Lifetime Coverage Optimization Protocol \\in Wireless Sensor Networks}
29 \author{\authorname{Ali Kadhum Idrees, Karine Deschinkel, Michel Salomon, and Rapha\"el Couturier}
30 \affiliation{FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comte, Belfort, France}
31 %\affiliation{\sup{2}Department of Computing, Main University, MySecondTown, MyCountry}
32 \email{ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel, michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}
33 %\email{\{f\_author, s\_author\}@ips.xyz.edu, t\_author@dc.mu.edu}
36 \keywords{Wireless Sensor Networks, Area Coverage, Network lifetime,
37 Optimization, Scheduling.}
39 \abstract{One of the fundamental challenges in Wireless Sensor Networks (WSNs) is the
40 coverage preservation and the extension of the network lifetime continuously and
41 effectively when monitoring a certain area (or region) of interest. In this
42 paper, a Distributed Lifetime Coverage Optimization protocol (DiLCO) to maintain
43 the coverage and to improve the lifetime in wireless sensor networks is
44 proposed. The area of interest is first divided into subregions using a
45 divide-and-conquer method and then the DiLCO protocol is distributed on the
46 sensor nodes in each subregion. The DiLCO combines two efficient techniques:
47 leader election for each subregion, followed by an optimization-based activity scheduling for each subregion. The proposed DiLCO works
48 into periods during which a small number of nodes, remaining active for sensing,
49 is selected to ensure coverage so as to maximize the lifetime of wireless sensor
50 network. Each period consists of four phases: (i)~Information Exchange,
51 (ii)~Leader Election, (iii)~Decision, and (iv)~Sensing. The decision process is
52 carried out by a leader node, which solves an integer program. Compared with
53 some existing protocols, simulation results show that the proposed protocol can
54 prolong the network lifetime and improve the coverage performance effectively.}
56 \onecolumn \maketitle \normalsize \vfill
58 \section{\uppercase{Introduction}}
59 \label{sec:introduction}
61 Energy efficiency is very important issue in WSNs since sensors are powered by batteries. Therefore, reducing energy consumption and extending network lifetime are the main challenges in the design of WSNs. One of the major scientific research challenges in WSNs, which has been addressed by a large amount of literature during the last few years, is the design of energy efficient approaches for coverage and connectivity~\cite{conti2014mobile}.
62 Coverage reflects how well a sensor field is monitored. The most discussed coverage problems in literature can be classified
63 into three types \cite{li2013survey}: area coverage (where every
64 point inside an area is to be monitored), target coverage (where the main objective is to cover only a finite number of discrete
65 points called targets), and barrier coverage (the problem of preventing an intruder from entering a region of interest is referred to as the barrier coverage).
66 It is required to monitor the area of interest efficiently~\cite{Nayak04}, but in the same time the power consumption should be minimized. Sensor nodes runs on batteries with limited capacities~\cite{Sudip03} and it is impossible, difficult or expensive to recharge and/or replace batteries in remote, hostile, or unpractical environments. Therefore, it is desired that the 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.
68 In this paper we concentrate on the area coverage problem with the objective of
69 maximizing the network lifetime by using DiLCO protocol to maintain the coverage and to improve the lifetime in WSNs. The area of interest is divided into subregions using divide-and-conquer method and an activity scheduling for sensor nodes is planned by the elected leader in each subregion. In fact, the nodes in a subregion can be seen as a cluster where each node sends sensing data to the cluster head or the sink node. Furthermore, the activities in a subregion/cluster can continue even if another cluster stops due to too many node failures. Our DiLCO protocol considers periods, where a period starts with a discovery phase to exchange information between sensors of the subregion, in order to choose in a suitable manner a sensor node (the leader) to carry out the coverage strategy. Our DiLCO protocol involves solving an integer program, which provides the activation of the sensors for the sensing phase of the current period.
71 The remainder of the paper is organized as follows. The next section reviews the related work in the field. Section~\ref{sec:The DiLCO Protocol Description} is devoted to the DiLCO protocol Description. Section~\ref{cp} gives the coverage model
72 formulation which is used to schedule the activation of sensors.
73 Section~\ref{sec:Simulation Results and Analysis} shows the simulation results. Finally, we give concluding remarks and some suggestions for
74 future works in Section~\ref{sec:Conclusion and Future Works}.
76 \section{\uppercase{Literature Review}}
77 \label{sec:Literature Review}
78 \noindent In this section, we summarize some related works regarding coverage lifetime maximization and scheduling, and distinguish our DiLCO protocol from the works presented in the literature. Some algorithms have been developed in ~\cite{yang2014energy,ChinhVu,vashistha2007energy,deschinkel2012column,shi2009,qu2013distributed,ling2009energy,xin2009area,cheng2014achieving,ling2009energy} to solve the area coverage problem so as to preserve coverage and prolong the network lifetime.
81 Yang et al.~\cite{yang2014energy} investigated full area coverage problem
82 under the probabilistic sensing model in the sensor networks. They have studied the relationship between the
83 coverage of two adjacent points mathematically and then convert the problem of full area coverage into point coverage problem. They proposed $\varepsilon$-full area coverage optimization (FCO) algorithm to select a subset
84 of sensors to provide probabilistic area coverage dynamically so as to extend the network lifetime.
87 Vu et al.~\cite{ChinhVu} proposed a localized and distributed greedy algorithm named DESK for generating non-disjoint cover sets which provide the k-area coverage for the whole network.
90 Qu et al.~\cite{qu2013distributed} developed a distributed algorithm using adjustable sensing sensors
91 for maintaining the full coverage of such sensor networks. The
92 algorithm contains two major parts: the first part aims at
93 providing $100\%$ coverage and the second part aims at saving
94 energy by decreasing the sensing radius.
96 Shi et al.~\cite{shi2009} modeled the Area Coverage Problem (ACP), which will be changed into a set coverage
97 problem. By using this model, they are proposed an Energy-Efficient central-Scheduling greedy algorithm, which can reduces energy consumption and increases network lifetime, by selecting a appropriate subset of sensor nodes to support the networks periodically.
99 The work in~\cite{cheng2014achieving} presented a unified sensing architecture for duty cycled sensor networks, called uSense, which comprises three ideas: Asymmetric Architecture, Generic Switching and Global Scheduling. The objective is to provide a flexible and efficient coverage in sensor networks.
101 In~\cite{ling2009energy}, the lifetime of
102 a sensor node is divided into epochs. At each epoch, the
103 base station deduces the current sensing coverage requirement
104 from application or user request. It then applies the heuristic algorithm in order to produce the set of active nodes which take the mission of sensing during the current epoch. After that, the produced schedule is sent to the sensor nodes in the network.
109 The work in ~\cite{vu2009delaunay} considered the area coverage problem for variable sensing radii in WSNs by improving the energy balancing heuristic proposed in ~\cite{wang2007energy} so that the area of interest can be full covered using Delaunay triangulation structure.
111 Diongue and Thiare~\cite{diongue2013alarm} proposed an energy aware sleep scheduling algorithm for lifetime maximization in wireless sensor networks (ALARM). The proposed approach permits to schedule redundant nodes according to the weibull distribution. This work did not analyze the ALARM scheme under the coverage problem.
114 In~\cite{xin2009area}, the authors proposed a circle intersection localized coverage algorithm
115 to maintain connectivity based on loose connectivity critical condition
116 . By using the connected coverage node set, it can maintain network
117 connection in the case which loose condition is not meet.
118 The authors in ~\cite{vashistha2007energy} addressed the full area coverage problem using information
119 coverage. They are proposed a low-complexity heuristic algorithm to obtain full area information covers (FAIC), which they refer to as Grid Based FAIC (GB-FAIC) algorithm. Using these FAICs, they are obtained the optimal schedule for applying the sensing activity of sensor nodes in order to
120 achieve increased sensing lifetime of the network.
127 In \cite{xu2001geography}, Xu et al. proposed a Geographical Adaptive Fidelity (GAF) algorithm, 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.
129 The main contributions of our DiLCO Protocol can be summarized as follows:
130 (1) The distributed optimization over the subregions in the area of interest,
131 (2) The distributed dynamic leader election at each round by each sensor node in the subregion,
132 (3) The primary point coverage model to represent each sensor node in the network,
133 (4) The activity scheduling based optimization on the subregion, which are based on the primary point coverage model to activate as less number as possible of sensor nodes to take the mission of the coverage in each subregion, and (5) The improved energy consumption model.
136 The work presented in~\cite{luo2014parameterized,tian2014distributed} tries to solve the target coverage problem so as to extend the network lifetime since it is easy to verify the coverage status of discreet target.
137 %Je ne comprends pas la phrase ci-dessus
138 The work proposed in~\cite{kim2013maximum} considers the barrier-coverage problem in WSNs. The final goal is to maximize the network lifetime such that any penetration of the intruder is detected.
139 %inutile de parler de ce papier car il concerne barrier coverage
140 In \cite{ChinhVu}, the authors propose a localized and distributed greedy algorithm named DESK for generating non-disjoint cover sets which provide the k-coverage for the whole network.
141 Our Work in~\cite{idrees2014coverage} proposes a coverage optimization protocol to improve the lifetime in heterogeneous energy wireless sensor networks. In this work, the coverage protocol distributed in each sensor node in the subregion but the optimization take place over the the whole subregion. We are considered only distributing the coverage protocol over two subregions.
143 The work presented in ~\cite{Zhang} focuses on a distributed clustering method, which aims to extend the network lifetime, while the coverage is ensured.
145 The work proposed by \cite{qu2013distributed} considers the coverage problem in WSNs where each sensor has variable sensing radius. The final objective is to maximize the network coverage lifetime in WSNs.
149 Casta{\~n}o et al.~\cite{castano2013column} proposed a multilevel approach based on column generation (CG) to extend the network lifetime with connectivity and coverage constraints. They are included two heuristic methods within the CG framework so as to accelerate the solution process.
150 In \cite{diongue2013alarm}, diongue is proposed an energy Aware sLeep scheduling AlgoRithm for lifetime maximization in WSNs (ALARM) algorithm for coverage lifetime maximization in wireless sensor networks. ALARM is sensor node scheduling approach for lifetime maximization in WSNs in which it schedule redundant nodes according to the weibull distribution taking into consideration frequent nodes failure.
151 Yu et al.~\cite{yu2013cwsc} presented a connected k-coverage working sets construction
152 approach (CWSC) to maintain k-coverage and connectivity. This approach try to select the minimum number of connected sensor nodes that can provide k-coverage ($k \geq 1$).
153 In~\cite{cheng2014achieving}, the authors are presented a unified sensing architecture for duty cycled sensor networks, called uSense, which comprises three ideas: Asymmetric Architecture, Generic Switching and Global Scheduling. The objective is to provide a flexible and efficient coverage in sensor networks.
155 In~\cite{yang2013energy}, the authors are investigated full area coverage problem
156 under the probabilistic sensing model in the sensor networks. %They are designed $\varepsilon-$full area coverage optimization (FCO) algorithm to select a subset of sensors to provide probabilistic area coverage dynamically so as to extend the network lifetime.
157 In \cite{xu2001geography}, Xu et al. proposed a Geographical Adaptive Fidelity (GAF) algorithm, 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.
159 The main contributions of our DiLCO Protocol can be summarized as follows:
160 (1) The distributed optimization over the subregions in the area of interest,
161 (2) The distributed dynamic leader election at each round by each sensor node in the subregion,
162 (3) The primary point coverage model to represent each sensor node in the network,
163 (4) The activity scheduling based optimization on the subregion, which are based on the primary point coverage model to activate as less number as possible of sensor nodes to take the mission of the coverage in each subregion,
164 (5) The improved energy consumption model.
168 \section{ The DiLCO Protocol Description}
169 \label{sec:The DiLCO Protocol Description}
171 \noindent In this section, we introduce a Distributed Lifetime Coverage Optimization protocol, which is called DiLCO. It is distributed on each subregion in the area of interest. It is based on two efficient techniques: network leader election and sensor activity scheduling for coverage preservation and energy conservation continuously and efficiently to maximize the lifetime in the network.
172 \iffalse The main features of our DiLCO protocol:
173 i)It divides the area of interest into subregions by using divide-and-conquer concept, ii)It requires only the information of the nodes within the subregion, iii) it divides the network lifetime into rounds, iv)It based on the autonomous distributed decision by the nodes in the subregion to elect the Leader, v)It apply the activity scheduling based optimization on the subregion, vi) it achieves an energy consumption balancing among the nodes in the subregion by selecting different nodes as a leader during the network lifetime, vii) It uses the optimization to select the best representative set of sensors in the subregion by optimize the coverage and the lifetime over the area of interest, viii)It uses our proposed primary point coverage model, which represent the sensing range of the sensor as a set of points, which are used by the our optimization algorithm, ix) It uses a simple energy model that takes communication, sensing and computation energy consumptions into account to evaluate the performance of our protocol.
175 \subsection{ Assumptions and Models}
176 \noindent We consider a randomly and uniformly deployed network consisting of
177 static wireless sensors. The wireless sensors are deployed in high
178 density to ensure initially a high coverage ratio of the interested area. We
179 assume that all nodes are homogeneous in terms of communication and
180 processing capabilities and heterogeneous in term of energy provision.
181 The location information is available to the sensor node either
182 through hardware such as embedded GPS or through location discovery
183 algorithms. We consider a boolean disk coverage model which is the most
184 widely used sensor coverage model in the literature. Each sensor has a
185 constant sensing range $R_s$. All space points within a disk centered
186 at the sensor with the radius of the sensing range is said to be
187 covered by this sensor. We also assume that the communication range $R_c \geq 2R_s$.
188 In fact, Zhang and Zhou~\cite{Zhang05} proved that if the transmission range fulfills the
189 previous hypothesis, a complete coverage of a convex area implies
190 connectivity among the working nodes in the active mode.
192 \indent Instead of working with the coverage area, we consider for each
193 sensor a set of points called primary points~\cite{idrees2014coverage}. We also assume that the
194 sensing disk defined by a sensor is covered if all the primary points of
195 this sensor are covered.
198 By knowing the position (point center: ($p_x,p_y$)) of a wireless
199 sensor node and its $R_s$, we calculate the primary points directly
200 based on the proposed model. We use these primary points (that can be
201 increased or decreased if necessary) as references to ensure that the
202 monitored region of interest is covered by the selected set of
203 sensors, instead of using all the points in the area.
205 \indent We can calculate the positions of the selected primary
206 points in the circle disk of the sensing range of a wireless sensor
207 node (see figure~\ref{fig1}) as follows:\\
208 $(p_x,p_y)$ = point center of wireless sensor node\\
210 $X_2=( p_x + R_s * (1), p_y + R_s * (0) )$\\
211 $X_3=( p_x + R_s * (-1), p_y + R_s * (0)) $\\
212 $X_4=( p_x + R_s * (0), p_y + R_s * (1) )$\\
213 $X_5=( p_x + R_s * (0), p_y + R_s * (-1 )) $\\
214 $X_6= ( p_x + R_s * (\frac{-\sqrt{2}}{2}), p_y + R_s * (0)) $\\
215 $X_7=( p_x + R_s * (\frac{\sqrt{2}}{2}), p_y + R_s * (0))$\\
216 $X_8=( p_x + R_s * (\frac{-\sqrt{2}}{2}), p_y + R_s * (\frac{-\sqrt{2}}{2})) $\\
217 $X_9=( p_x + R_s * (\frac{\sqrt{2}}{2}), p_y + R_s * (\frac{-\sqrt{2}}{2})) $\\
218 $X_{10}=( p_x + R_s * (\frac{-\sqrt{2}}{2}), p_y + R_s * (\frac{\sqrt{2}}{2})) $\\
219 $X_{11}=( p_x + R_s * (\frac{\sqrt{2}}{2}), p_y + R_s * (\frac{\sqrt{2}}{2})) $\\
220 $X_{12}=( p_x + R_s * (0), p_y + R_s * (\frac{\sqrt{2}}{2})) $\\
221 $X_{13}=( p_x + R_s * (0), p_y + R_s * (\frac{-\sqrt{2}}{2})) $.
227 %\includegraphics[scale=0.20]{fig21.pdf}\\~ ~ ~ ~ ~(a)
228 %\includegraphics[scale=0.20]{fig22.pdf}\\~ ~ ~ ~ ~(b)
229 \includegraphics[scale=0.25]{principles13.pdf}%\\~ ~ ~ ~ ~(c)
230 %\includegraphics[scale=0.10]{fig25.pdf}\\~ ~ ~(d)
231 %\includegraphics[scale=0.10]{fig26.pdf}\\~ ~ ~(e)
232 %\includegraphics[scale=0.10]{fig27.pdf}\\~ ~ ~(f)
234 \caption{Wireless Sensor Node represented by 13 primary points}
235 %\caption{Wireless Sensor Node represented by (a)5, (b)9 and (c)13 primary points respectively}
241 \subsection{The Main Idea}
242 \noindent The area of interest can be divided using the
243 divide-and-conquer strategy into smaller areas called subregions and
244 then our coverage protocol will be implemented in each subregion
245 simultaneously. Our DiLCO protocol works in periods fashion as shown in figure~\ref{fig2}.
248 \includegraphics[width=75mm]{FirstModel.pdf} % 70mm
249 \caption{DiLCO protocol}
253 %Modifier la figure pour faire apparaitre des periodes et dans le schema en bleu, indiquer sensing round au lieu de sensing tout seul.
255 Each period is divided into 4 phases : Information (INFO) Exchange,
256 Leader Election, Decision, and Sensing. For each period there is
257 exactly one set cover responsible for the sensing task. This protocol is
258 more reliable against an unexpected node failure because it works
259 in periods. On the one hand, if a node failure is detected before
260 making the decision, the node will not participate to this phase, and,
261 on the other hand, if the node failure occurs after the decision, the
262 sensing task of the network will be temporarily affected: only during
263 the period of sensing until a new period starts, since a new set cover
264 will take charge of the sensing task in the next period. The energy
265 consumption and some other constraints can easily be taken into
266 account since the sensors can update and then exchange their
267 information (including their residual energy) at the beginning of each
268 period. However, the pre-sensing phases (INFO Exchange, Leader
269 Election, Decision) are energy consuming for some nodes, even when
270 they do not join the network to monitor the area.
271 We define two types of packets to be used by our DiLCO protocol.
272 %\begin{enumerate}[(a)]
274 \item INFO packet: sent by each sensor node to all the nodes inside a same subregion for information exchange.
275 \item ActiveSleep packet: sent by the leader to all the nodes in its subregion to inform them to be Active or Sleep during the sensing phase.
279 There are five status for each sensor node in the network :
280 %\begin{enumerate}[(a)]
282 \item LISTENING: Sensor is waiting for a decision (to be active or not)
283 \item COMPUTATION: Sensor applies the optimization process as leader
284 \item ACTIVE: Sensor is active
285 \item SLEEP: Sensor is turned off
286 \item COMMUNICATION: Sensor is transmitting or receiving packet
289 %Below, we describe each phase in more details.
290 Algorithm 1 gives a brief description of the protocol applied by each sensor node (denoted by $s_j$ for a sensor node indexed by $j$).
291 Initially, the sensor node checks its remaining energy in order to participate in the current period. After that, all the sensors collect position coordinates, remaining energy $RE_j$, sensor node id, and the number of its one-hop live neighbors during the information exchange.
292 Then all the sensor nodes in the same subregion will select the leader based on the received informations. The selection criteria for the leader in order of priority are: larger number of neighbours, larger remaining energy, and then in case of equality, larger index. After that, if the sensor node is leader, it will execute the integer program algorithm (see section~\ref{cp}) which provides a set of sensors planned to be active in the sensing round. As leader, it will send an Active-Sleep packet to each sensor in the same subregion to indicate it if it has to be active or not. On the contrary, if the sensor is not the leader, it will wait for the Active-Sleep packet to know its state for the sensing round.
297 \subsubsection{Information Exchange Phase}
299 Each sensor node $j$ sends its position, remaining energy $RE_j$, and
300 the number of neighbours $NBR_j$ to all wireless sensor nodes in
301 its subregion by using an INFO packet and then listens to the packets
302 sent from other nodes. After that, each node will have information
303 about all the sensor nodes in the subregion. In our model, the
304 remaining energy corresponds to the time that a sensor can live in the
307 \subsubsection{Leader Election Phase}
308 This step includes choosing the Wireless Sensor Node Leader (WSNL),
309 which will be responsible for executing the coverage algorithm. Each
310 subregion in the area of interest will select its own WSNL
311 independently for each round. All the sensor nodes cooperate to
312 select WSNL. The nodes in the same subregion will select the leader
313 based on the received information from all other nodes in the same
314 subregion. The selection criteria in order of priority are: larger
315 number of neighbours, larger remaining energy, and then in case of
316 equality, larger index.
318 \subsubsection{Decision phase}
319 The WSNL will solve an integer program (see section~\ref{cp}) to
320 select which sensors will be activated in the following sensing phase
321 to cover the subregion. WSNL will send Active-Sleep packet to each
322 sensor in the subregion based on the algorithm's results.
325 \subsubsection{Sensing phase}
326 Active sensors in the round will execute their sensing task to
327 preserve maximal coverage in the region of interest. We will assume
328 that the cost of keeping a node awake (or asleep) for sensing task is
329 the same for all wireless sensor nodes in the network. Each sensor
330 will receive an Active-Sleep packet from WSNL informing it to stay
331 awake or to go to sleep for a time equal to the period of sensing until
332 starting a new round. Algorithm 1, which
333 will be executed by each node at the beginning of a round, explains how the
334 Active-Sleep packet is obtained.
340 \subsection{DiLCO protocol Algorithm}
341 we first show the pseudo-code of DiLCO protocol, which is executed by each sensor in the subregion and then describe it in more detail.
344 \begin{algorithm}[h!]
345 % \KwIn{all the parameters related to information exchange}
346 % \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}
348 %\emph{Initialize the sensor node and determine it's position and subregion} \;
350 \If{ $RE_j \geq E_{th}$ }{
351 \emph{$s_j.status$ = COMMUNICATION}\;
352 \emph{Send $INFO()$ packet to other nodes in the subregion}\;
353 \emph{Wait $INFO()$ packet from other nodes in the subregion}\;
354 %\emph{UPDATE $RE_j$ for every sent or received INFO Packet}\;
355 %\emph{ Collect information and construct the list L for all nodes in the subregion}\;
357 %\If{ the received INFO Packet = No. of nodes in it's subregion -1 }{
358 \emph{LeaderID = Leader election}\;
359 \If{$ s_j.ID = LeaderID $}{
360 \emph{$s_j.status$ = COMPUTATION}\;
361 \emph{$\left\{\left(X_{1},\dots,X_{k},\dots,X_{J}\right)\right\}$ =
362 Execute Integer Program Algorithm($J$)}\;
363 \emph{$s_j.status$ = COMMUNICATION}\;
364 \emph{Send $ActiveSleep()$ to each node $k$ in subregion} \;
365 \emph{Update $RE_j $}\;
368 \emph{$s_j.status$ = LISTENING}\;
369 \emph{Wait $ActiveSleep()$ packet from the Leader}\;
370 % \emph{After receiving Packet, Retrieve the schedule and the $T$ rounds}\;
371 \emph{Update $RE_j $}\;
375 \Else { Exclude $s_j$ from entering in the current sensing phase}
378 \caption{DiLCO($s_j$)}
384 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
385 LISTENING mode. Thus, by entering the LISTENING mode at the beginning of each round,
386 sensor nodes still executing sensing task while participating in the leader election and decision phases. More specifically, The DiLCO protocol algorithm works as follow:
387 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.
388 The sensor node enter in listening mode waiting to receive ActiveSleep packet from the leader to take the decision. Each sensor node will execute the Algorithm~1 to know who is the leader. After that, if the sensor node is leader, It will execute the integer program algorithm ( see section~\ref{cp}) to optimize the coverage and the lifetime in it's subregion. After the decision, the optimization approach will select the set of sensor nodes to take the mission of coverage during the sensing phase. The leader will send ActiveSleep packet to each sensor node in the subregion to inform him to it's status during the period of sensing, either Active or sleep until the starting of next round. Based on the decision, the leader as other nodes in subregion, either go to be active or go to be sleep during current sensing phase. the other nodes in the same subregion will stay in listening mode waiting the ActiveSleep packet from the leader. After finishing the time period for sensing, all the sensor nodes in the same subregion will start new round by executing the DiLCO protocol and the lifetime in the subregion will continue until all the sensor nodes are died or the network becomes disconnected in the subregion.
392 \section{Coverage problem formulation}
395 \indent Our model is based on the model proposed by
396 \cite{pedraza2006} where the objective is to find a maximum number of
397 disjoint cover sets. To accomplish this goal, authors proposed an
398 integer program, which forces undercoverage and overcoverage of targets
399 to become minimal at the same time. They use binary variables
400 $x_{jl}$ to indicate if sensor $j$ belongs to cover set $l$. In our
401 model, we consider binary variables $X_{j}$, which determine the
402 activation of sensor $j$ in the sensing round. We also
403 consider primary points as targets. The set of primary points is
404 denoted by $P$ and the set of sensors by $J$.
406 \noindent For a primary point $p$, let $\alpha_{jp}$ denote the
407 indicator function of whether the point $p$ is covered, that is:
409 \alpha_{jp} = \left \{
411 1 & \mbox{if the primary point $p$ is covered} \\
412 & \mbox{by sensor node $j$}, \\
413 0 & \mbox{otherwise.}\\
417 The number of active sensors that cover the primary point $p$ is equal
418 to $\sum_{j \in J} \alpha_{jp} * X_{j}$ where:
422 1& \mbox{if sensor $j$ is active,} \\
423 0 & \mbox{otherwise.}\\
427 We define the Overcoverage variable $\Theta_{p}$ as:
429 \Theta_{p} = \left \{
431 0 & \mbox{if the primary point}\\
432 & \mbox{$p$ is not covered,}\\
433 \left( \sum_{j \in J} \alpha_{jp} * X_{j} \right)- 1 & \mbox{otherwise.}\\
437 \noindent More precisely, $\Theta_{p}$ represents the number of active
438 sensor nodes minus one that cover the primary point $p$.\\
439 The Undercoverage variable $U_{p}$ of the primary point $p$ is defined
444 1 &\mbox{if the primary point $p$ is not covered,} \\
445 0 & \mbox{otherwise.}\\
450 \noindent Our coverage optimization problem can then be formulated as follows
451 \begin{equation} \label{eq:ip2r}
454 \min \sum_{p \in P} (w_{\theta} \Theta_{p} + w_{U} U_{p})&\\
455 \textrm{subject to :}&\\
456 \sum_{j \in J} \alpha_{jp} X_{j} - \Theta_{p}+ U_{p} =1, &\forall p \in P\\
458 %\sum_{t \in T} X_{j,t} \leq \frac{RE_j}{e_t} &\forall j \in J \\
460 \Theta_{p}\in \mathbb{N} , &\forall p \in P\\
461 U_{p} \in \{0,1\}, &\forall p \in P \\
462 X_{j} \in \{0,1\}, &\forall j \in J
470 \item $X_{j}$ : indicates whether or not the sensor $j$ is actively
471 sensing in the round (1 if yes and 0 if not);
472 \item $\Theta_{p}$ : {\it overcoverage}, the number of sensors minus
473 one that are covering the primary point $p$;
474 \item $U_{p}$ : {\it undercoverage}, indicates whether or not the primary point
475 $p$ is being covered (1 if not covered and 0 if covered).
478 The first group of constraints indicates that some primary point $p$
479 should be covered by at least one sensor and, if it is not always the
480 case, overcoverage and undercoverage variables help balancing the
481 restriction equations by taking positive values. There are two main
482 objectives. First, we limit the overcoverage of primary points in order to
483 activate a minimum number of sensors. Second we prevent the absence of monitoring on
484 some parts of the subregion by minimizing the undercoverage. The
485 weights $w_\theta$ and $w_U$ must be properly chosen so as to
486 guarantee that the maximum number of points are covered during each
492 \section{\uppercase{Simulation Results and Analysis}}
493 \label{sec:Simulation Results and Analysis}
494 \noindent \subsection{Simulation Framework}
495 In this subsection, we conducted a series of simulations to evaluate the
496 efficiency and the relevance of our DiLCO protocol, using the discrete event
497 simulator OMNeT++ \cite{varga}. The simulation parameters are summarized in
501 \caption{Relevant parameters for network initializing.}
504 % used for centering table
506 % centered columns (4 columns)
508 %inserts double horizontal lines
509 Parameter & Value \\ [0.5ex]
511 %Case & Strategy (with Two Leaders) & Strategy (with One Leader) & Simple Heuristic \\ [0.5ex]
515 % inserts single horizontal line
516 Sensing Field & $(50 \times 25)~m^2 $ \\
517 % inserting body of the table
519 Nodes Number & 50, 100, 150, 200 and 250~nodes \\
521 Initial Energy & 500-700~joules \\
523 Sensing Period & 60 Minutes \\
524 $E_{th}$ & 36 Joules\\
528 % [1ex] adds vertical space
534 % is used to refer this table in the text
537 We performed simulations for five different densities varying from 50 to 250~nodes. Experimental results are the average obtained from 25 randomly generated networks (25 for each network density) in which nodes are deployed over a $(50 \times 25)~m^2 $ sensing field. More precisely, the deployment is controlled at a coarse scale in order to ensure that the deployed nodes can cover the sensing field with a high coverage ratio.\\
539 We first concentrate on the required number of subregions making effective our protocol. Thus our DiLCO protocol is declined into five versions: DiLCO-2, DiLCO-4, DiLCO-8, DiLCO-16, and DiLCO-32, corresponding to $2$, $4$, $8$, $16$ or $32$ subregions (leaders).
541 We use an energy consumption model proposed by~\cite{ChinhVu} and based on ~\cite{raghunathan2002energy} with slight modifications.
542 The energy consumption for sending/receiving the packets is added whereas the part related to the sensing range is removed because we consider a fixed sensing range.
543 % We are took into account the energy consumption needed for the high computation during executing the algorithm on the sensor node.
544 %The new energy consumption model will take into account the energy consumption for communication (packet transmission/reception), the radio of the sensor node, data sensing, computational energy of Micro-Controller Unit (MCU) and high computation energy of MCU.
547 For our energy consumption model, we refer to the sensor node Medusa II which uses Atmels AVR ATmega103L microcontroller~\cite{raghunathan2002energy}. The typical architecture of a sensor is composed of four subsystems : the MCU subsystem which is capable of computation, communication subsystem (radio) which is responsible for
548 transmitting/receiving messages, sensing subsystem that collects data, and the power supply which powers the complete sensor node ~\cite{raghunathan2002energy}. Each of the first three subsystems can be turned on or off depending on the current status of the sensor. Energy consumption (expressed in milliWatt per second) for the different status of the sensor is summarized in Table~\ref{table4}.
551 \caption{The Energy Consumption Model}
554 % used for centering table
555 \begin{tabular}{|c|c|c|c|c|}
556 % centered columns (4 columns)
558 %inserts double horizontal lines
559 Sensor mode & MCU & Radio & Sensing & Power (mW) \\ [0.5ex]
561 % inserts single horizontal line
562 Listening & ON & ON & ON & 20.05 \\
563 % inserting body of the table
565 Active & ON & OFF & ON & 9.72 \\
567 Sleep & OFF & OFF & OFF & 0.02 \\
569 Computation & ON & ON & ON & 26.83 \\
571 %\multicolumn{4}{|c|}{Energy needed to send/receive a 1-bit} & 0.2575\\
576 % is used to refer this table in the text
579 For the sake of simplicity we ignore the energy needed to turn on the
580 radio, to start up the sensor node, the transition from one status to another, etc.
581 %We also do not consider the need of collecting sensing data. PAS COMPRIS
582 Thus, when a sensor becomes active (i.e., it already decides its status), it can turn its radio off to save battery. DiLCO protocol uses two types of packets for communication. The size of the INFO-Packet and Status-Packet are 112 bits and 24 bits respectively.
583 The value of energy spent to send a 1-bit-content message is obtained by using the equation in ~\cite{raghunathan2002energy} to calculate the energy cost for transmitting messages and we propose the same value for receiving the packets.
584 The energy needed to send or receive a 1-bit is equal to $0.2575 mW$.
586 The initial energy of each node is randomly set in the interval $[500-700]$. Each sensor node will not participate in the next round if its remaining energy is less than $E_{th}=36 Joules$, the minimum energy needed for the node to stay alive during one round. This value has been computed by multiplying the energy consumed in active state (9.72 mW) by the time in second for one round (3600 seconds). According to the interval of initial energy, a sensor may be alive during at most 20 rounds.\\
589 In the simulations, we introduce the following performance metrics to evaluate the efficiency of our approach:
591 %\begin{enumerate}[i)]
594 \item {{\bf Coverage Ratio (CR)}:} the coverage ratio measures how much the area of a sensor field is covered. In our case, we treated the sensing fields as a grid, and used each grid point as a sample point
595 for calculating the coverage. The coverage ratio can be calculated by:
598 \mbox{CR}(\%) = \frac{\mbox{$n$}}{\mbox{$N$}} \times 100.
600 where $n$ is the number of covered grid points by the active sensors of all subregions during the current sensing phase and $N$ is total number of grid points in the sensing field of the network. In our simulation $N = 51 \times 26 = 1326$ grid points.
601 %The accuracy of this method depends on the distance between grids. In our
602 %simulations, the sensing field has been divided into 50 by 25 grid points, which means
603 %there are $51 \times 26~ = ~ 1326$ points in total.
604 % Therefore, for our simulations, the error in the coverage calculation is less than ~ 1 $\% $.
608 \item{{\bf Number of Active Sensors Ratio(ASR)}:} It is important to have as few active nodes as possible in each round,
609 in order to minimize the communication overhead and maximize the
610 network lifetime. The Active Sensors Ratio is defined as follows:
613 \mbox{ASR}(\%) = \frac{\sum\limits_{r=1}^R \mbox{$A_r^t$}}{\mbox{$S$}} \times 100 .
615 Where: $A_r^t$ is the number of active sensors in the subregion $r$ during round $t$ in the current sensing phase, $S$ is the total number of sensors in the network, and $R$ is the total number of the subregions in the network.
619 \item {{\bf Network Lifetime}:} we define the network lifetime as the time until the coverage ratio drops below a predefined threshold. We denoted by $Lifetime95$ (respectively $Lifetime50$) as the amount of time during which the network can satisfy an area coverage greater than $95\%$ (repectively $50\%$). We assume that the network
620 is alive until all nodes have been drained of their energy or the
621 sensor network becomes disconnected . Network connectivity is important because an
622 active sensor node without connectivity towards a base station cannot
623 transmit information on an event in the area that it monitors.
626 \item {{\bf Energy Consumption}:}
628 Energy Consumption (EC) can be seen as the total energy consumed by the sensors during the $Lifetime95$ or $Lifetime50$ divided by the number of periods. The EC can be computed as follow: \\
631 \mbox{EC} = \frac{\sum\limits_{m=1}^{M} \left( E^{\mbox{com}}_m+E^{\mbox{list}}_m+E^{\mbox{comp}}_m + E^{a}+E^{s} \right)}{M_L},
636 %\mbox{EC} = \frac{\mbox{$\sum\limits_{d=1}^D E^c_d$}}{\mbox{$D$}} + \frac{\mbox{$\sum\limits_{d=1}^D %E^l_d$}}{\mbox{$D$}} + \frac{\mbox{$\sum\limits_{d=1}^D E^a_d$}}{\mbox{$D$}} + %\frac{\mbox{$\sum\limits_{d=1}^D E^s_d$}}{\mbox{$D$}}.
639 where $M$ corresponds to the number of periods. The total energy consumed by the sensors
640 (EC) comes through taking into consideration four main energy factors. The first
641 one , denoted $E^{\scriptsize \mbox{com}}_m$, represent the energy consumption
642 spent by all the nodes for wireless communications during period $m$.
643 $E^{\scriptsize \mbox{list}}_m$, the next factor, corresponds to the energy
644 consumed by the sensors in LISTENING status before receiving the decision to go
645 active or sleep in period $m$. $E^{\scriptsize \mbox{comp}}_m$ refers to the
646 energy needed by all the leader nodes to solve the integer program during a
647 period. Finally, $E^a_{m}$ and $E^s_{m}$ indicate the energy consumed by the whole network in the sensing round.
650 \item {{\bf Execution Time}:} a sensor node has limited energy resources and computing power,
651 therefore it is important that the proposed algorithm has the shortest
652 possible execution time. The energy of a sensor node must be mainly
653 used for the sensing phase, not for the pre-sensing ones.
655 \item {{\bf Stopped simulation runs}:} A simulation
656 ends when the sensor network becomes
657 disconnected (some nodes are dead and are not able to send information to the base station). We report the number of simulations that are stopped due to network disconnections and for which round it occurs.
665 %\subsection{Performance Analysis for differnet subregions}
666 \subsection{Performance Analysis}
668 In this subsection, we study the performance of our DiLCO protocol for different number of subregions (Leaders).
669 The DiLCO-1 protocol is a centralized approach on all the area of the interest, while DiLCO-2, DiLCO-4, DiLCO-8, DiLCO-16 and DiLCO-32 are distributed on two, four, eight, sixteen, and thirty-two subregions respectively. We do not take into account the DiLC0-1 protocol in our simulation results because it requires high execution time to solve the integer program and thus it is too costly in term of energy.
671 Our method is compared with other two approaches. The first approach, called DESK and proposed by ~\cite{ChinhVu} is a full distributed coverage algorithm. The second approach, called GAF ~\cite{xu2001geography}, consists in dividing the region into fixed squares. During the decision phase, in each square, one sensor is chosen to remain on during the sensing phase time.
674 \subsubsection{Coverage Ratio}
675 Figure~\ref{fig3} shows the average coverage ratio for 150 deployed nodes.
679 \includegraphics[scale=0.45] {R/CR.pdf}
680 \caption{The Coverage Ratio}
684 Figure~\ref{fig3} shows that DESK and GAF provide a
685 a little better coverage ratio compared to DiLCO in the first thirty periods. This is due to the fact that our DiLCO protocol versions put in sleep mode some sensors through optimization process (which slightly decreases the coverage ratio) while there are more active nodes with DESK or GAF. With DiLCO-2 (respectively DiLCO-4), the coverage ratio decreases rapidly to reach zero value in period ... (respectively in period ....) whereas other methods guarantee a coverage ratio greater than $50\%$ after this period. We believe that the results obtained with these two methods can be explained by a high consumption of energy
686 and we will check this assumption in the next paragraph. Concerning DiLCO-8, DiLCO-16 and DiLCO-32, these methods seem to be more efficient than DESK and GAF because they can provide the same level of coverage (except in the first periods, slightly lower) for a greater number of periods. Unlike other methods, their strategy enables to activate a restricted number of nodes, and thus extends the lifetime of the network.
687 %As shown in the figure ~\ref{fig3}, as the number of subregions increases, the coverage preservation for area of interest increases for a larger number of periods. Coverage ratio decreases when the number of periods increases due to dead nodes. Although some nodes are dead,
688 %thanks to DiLCO-8, DiLCO-16 and DiLCO-32 protocols, other nodes are preserved to ensure the coverage. Moreover, when we have a dense sensor network, it leads to maintain the coverage for a larger number of rounds. DiLCO-8, DiLCO-16 and DiLCO-32 protocols are
689 %slightly more efficient than other protocols, because they subdivides
690 %the area of interest into 8, 16 and 32~subregions if one of the subregions becomes disconnected, the coverage may be still ensured in the remaining subregions.%
694 \subsubsection{The Energy Consumption}
695 Based on previous results in figure~\ref{fig3}, we keep DiLCO-16 and DiLCO-32 and we compare their performances in terms of energy consumption with the two other approaches. We measure the energy consumed by the sensors during the communication, listening, computation, active, and sleep modes for different network densities. Figure~\ref{fig95} illustrates the energy consumption for different network sizes.
696 % for $Lifetime95$ and $Lifetime50$.
697 We denote by $DiLCO-/50$ (respectively $DiLCO-/95$) as the amount of energy consumed during which the network can satisfy an area coverage greater than $50\%$ (repectively $95\%$) and we refer to the same definition for the two other approaches.
700 \includegraphics[scale=0.45]{R/EC.pdf}
701 \caption{The Energy Consumption}
705 The results show that DiLCO-16/50, DiLCO-32/50, DiLCO-16/95 and DiLCO-32/95 protocols are the most competitive from the energy consumption point of view. The other approaches have a high energy consumption due to activating a larger number of redundant nodes.
708 %In fact, a distributed method on the subregions greatly reduces the number of communications and the time of listening so thanks to the partitioning of the initial network into several independent subnetworks.
709 %As shown in Figures~\ref{fig95} and ~\ref{fig50} , DiLCO-2 consumes more energy than the other versions of DiLCO, especially for large sizes of network. This is easy to understand since the bigger the number of sensors involved in the integer program, the larger the time computation to solve the optimization problem as well as the higher energy consumed during the communication.
712 \subsubsection{Execution Time}
713 We observe the impact of the network size and of the number of subregions on the computation time. We report the average execution times in seconds needed to solve the optimization problem for the different approaches and various numbers of sensors.
714 The original execution time is computed on a laptop DELL with intel Core i3 2370 M (2.4 GHz) processor (2 cores) and the MIPS (Million Instructions Per Second) rate equal to 35330. To be consistent with the use of a sensor node with Atmels AVR ATmega103L microcontroller (6 MHz) and a MIPS rate equal to 6 to run the optimization resolution, this time is multiplied by 2944.2 $\left( \frac{35330}{2} \times \frac{1}{6}\right)$ and reported on Figure~\ref{fig8}.
718 \includegraphics[scale=0.45]{R/T.pdf}
719 \caption{Execution Time (in seconds)}
724 Figure~\ref{fig8} shows that DiLCO-32 has very low execution times in comparison with other DiLCO versions, because the activity scheduling is tackled by a larger number of leaders and each leader solves an integer problem with a limited number of variables and constraints. Conversely, DiLCO-2 requires to solve an optimization problem with half of the network nodes and thus presents a high execution time. Nevertheless if we refer to figure~\ref{fig3}, we observe that DiLCO-32 is slightly less efficient than DilCO-16 to maintain as long as possible high coverage. Excessive subdivision of the area of interest prevents to ensure good coverage especially on the borders of the subregions.
726 %The DiLCO-32 has more suitable times in the same time it turn on redundent nodes more. We think that in distributed fashion the solving of the optimization problem in a subregion can be tackled by sensor nodes. Overall, to be able to deal with very large networks, a distributed method is clearly required.
729 \subsubsection{The Network Lifetime}
730 In figure~\ref{figLT95}, network lifetime is illustrated for different network sizes. The term $/50$ (respectively $/95$) next to the name of the method refers to the amount of time during which the network can satisfy an area coverage greater than $50\%$ ($Lifetime50$)(repectively $95\%$ ($Lifetime95$))
734 \includegraphics[scale=0.45]{R/LT.pdf}
735 \caption{The Network Lifetime}
740 As highlighted by figure~\ref{figLT95}, the network lifetime obviously
741 increases when the size of the network increases. For the same level of coverage, DiLCO outperforms DESK and GAF for the lifetime of the network. If we focus on level of coverage greater than $95\%$, The subdivision in $16$ subregions seems to be the most appropriate.
744 % with our DiLCO-16/50, DiLCO-32/50, DiLCO-16/95 and DiLCO-32/95 protocols
745 % that leads to the larger lifetime improvement in comparison with other approaches. By choosing the best
746 % suited nodes, for each round, to cover the area of interest and by
747 % letting the other ones sleep in order to be used later in next rounds. Comparison shows that our DiLCO-16/50, DiLCO-32/50, DiLCO-16/95 and DiLCO-32/95 protocols, which are used distributed optimization over the subregions, are the best one because it is robust to network disconnection during the network lifetime as well as it consume less energy in comparison with other approaches. It also means that distributing the protocol in each node and subdividing the sensing field into many subregions, which are managed
748 % independently and simultaneously, is the most relevant way to maximize the lifetime of a network.
753 \section{\uppercase{Conclusion and Future Works}}
754 \label{sec:Conclusion and Future Works}
755 In this paper, we have addressed the problem of the coverage and the lifetime
756 optimization in wireless sensor networks. This is a key issue as
757 sensor nodes have limited resources in terms of memory, energy and
758 computational power. To cope with this problem, the field of sensing
759 is divided into smaller subregions using the concept of divide-and-conquer method, and then a DiLCO protocol for optimizing the coverage and lifetime performances in each subregion.
760 The proposed protocol combines two efficient techniques: network
761 leader election and sensor activity scheduling, where the challenges
762 include how to select the most efficient leader in each subregion and
763 the best representative set of active nodes to ensure a high level of coverage.
764 We have compared this method with two other approaches using many metrics as coverage ratio, execution time, lifetime.
765 Some experiments have been performed to study the choice of the number of
766 subregions which subdivide the sensing field, considering different network
767 sizes. They show that as the number of subregions increases, so does the network
768 lifetime. Moreover, it makes the DiLCO protocol more robust against random
769 network disconnection due to node failures. However, too much subdivisions
770 reduces the advantage of the optimization. In fact, there is a balance between
771 the benefit from the optimization and the execution time needed to solve
772 it. Therefore, the subdivision in $16$ subregions seems to be the most appropriate.
774 \noindent In this paper, we have addressed the problem of the coverage and the lifetime
775 optimization in wireless sensor networks. This is a key issue as
776 sensor nodes have limited resources in terms of memory, energy and
777 computational power. To cope with this problem, the field of sensing
778 is divided into smaller subregions using the concept of divide-and-conquer method, and then a DiLCO protocol for optimizing the coverage and lifetime performances in each subregion.
779 The proposed protocol combines two efficient techniques: network
780 leader election and sensor activity scheduling, where the challenges
781 include how to select the most efficient leader in each subregion and
782 the best representative active nodes that will optimize the network lifetime
783 while taking the responsibility of covering the corresponding
784 subregion. The network lifetime in each subregion is divided into
785 rounds, each round consists of four phases: (i) Information Exchange,
786 (ii) Leader Election, (iii) an optimization-based Decision in order to
787 select the nodes remaining active for the last phase, and (iv)
788 Sensing. The simulations show the relevance of the proposed DiLCO
789 protocol in terms of lifetime, coverage ratio, active sensors ratio, energy consumption, execution time, and the number of stopped simulation runs due to network disconnection. Indeed, when
790 dealing with large and dense wireless sensor networks, a distributed
791 approach like the one we are proposed allows to reduce the difficulty of a
792 single global optimization problem by partitioning it in many smaller
793 problems, one per subregion, that can be solved more easily.
795 In future work, we plan to study and propose a coverage optimization protocol, which
796 computes all active sensor schedules in one time, using
797 optimization methods. \iffalse The round will still consist of 4 phases, but the
798 decision phase will compute the schedules for several sensing phases
799 which, aggregated together, define a kind of meta-sensing phase.
800 The computation of all cover sets in one time is far more
801 difficult, but will reduce the communication overhead. \fi
803 \section*{\uppercase{Acknowledgements}}
804 \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.
811 \bibliographystyle{apalike}
813 \bibliography{Example}}
