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39 \title{Lifetime Coverage Optimization Protocol \\
40 in Wireless Sensor Networks} %LiCO Protocol
42 \author{Ali Kadhum Idrees,~\IEEEmembership{}
43 Karine Deschinkel,~\IEEEmembership{}
44 Michel Salomon,~\IEEEmembership{}
45 and~Rapha\"el Couturier ~\IEEEmembership{}
46 \thanks{The authors are with FEMTO-ST Institute, UMR 6174 CNRS, University of Franche-Comt\'e,
47 Belfort, France. Email: ali.idness@edu.univ-fcomte.fr, $\lbrace$karine.deschinkel,
48 michel.salomon, raphael.couturier$\rbrace$@univ-fcomte.fr}}
50 \markboth{IEEE Communications Letters,~Vol.~XX, No.~Y, January 2015}%
51 {Shell \MakeLowercase{\textit{et al.}}: Bare Demo of IEEEtran.cls for Journals}
56 The most important problem in Wireless Sensor Networks (WSNs) is to optimize the
57 use of its limited energy provision, so that it can fulfill its monitoring task
58 as long as possible. Among known available approaches that can be used to
59 improve power management, lifetime coverage optimization provides activity
60 scheduling which ensures sensing coverage while minimizing the energy cost. In
61 this paper, we propose a such approach called Lifetime Coverage Optimization
62 protocol (LiCO). It is a hybrid of centralized and distributed methods: the
63 region of interest is first subdivided into subregions and our protocol is then
64 distributed among sensor nodes in each subregion. A sensor node which runs LiCO
65 protocol repeats periodically four stages: information exchange, leader
66 election, optimization decision, and sensing. More precisely, the scheduling of
67 nodes' activities (sleep/wake up duty cycles) is achieved in each subregion by a
68 leader selected after cooperation between nodes within the same subregion. The
69 novelty of approach lies essentially in the formulation of a new mathematical
70 optimization model based on perimeter coverage level to schedule sensors'
71 activities. Extensive simulation experiments have been performed using OMNeT++,
72 the discrete event simulator, to demonstrate that LiCO is capable to offer
73 longer lifetime coverage for WSNs in comparison with some other protocols.
76 % Note that keywords are not normally used for peerreview papers.
78 Wireless Sensor Networks, Area Coverage, Network lifetime, Optimization, Scheduling.
81 \IEEEpeerreviewmaketitle
83 \section{Introduction}
84 \label{sec:introduction}
86 \noindent The continuous progress in Micro Electro-Mechanical Systems (MEMS) and
87 wireless communication hardware has given rise to the opportunity to use large
88 networks of tiny sensors, called Wireless Sensor Networks
89 (WSN)~\cite{akyildiz2002wireless,puccinelli2005wireless}, to fulfill monitoring
90 tasks. A WSN consists of small low-powered sensors working together by
91 communicating with one another through multi-hop radio communications. Each node
92 can send the data it collects in its environment, thanks to its sensor, to the
93 user by means of sink nodes. The features of a WSN made it suitable for a wide
94 range of application in areas such as business, environment, health, industry,
95 military, and son~\cite{yick2008wireless}. Typically, a sensor node contains
96 three main components~\cite{anastasi2009energy}: a sensing unit able to measure
97 physical, chemical, or biological phenomena observed in the environment; a
98 processing unit which will process and store the collected measurements; a radio
99 communication unit for data transmission and receiving.
101 The energy needed by an active sensor node to perform sensing, processing, and
102 communication is supplied by a power supply which is a battery. This battery has
103 a limited energy provision and it may be unsuitable or impossible to replace or
104 recharge it in most applications. Therefore it is necessary to deploy WSN with
105 high density in order to increase the reliability and to exploit node redundancy
106 thanks to energy-efficient activity scheduling approaches. Indeed, the overlap
107 of sensing areas can be exploited to schedule alternatively some sensors in a
108 low power sleep mode and thus save energy. Overall, the main question that must
109 be answered is: how to extend the lifetime coverage of a WSN as long as possible
110 while ensuring a high level of coverage? So, this last years many
111 energy-efficient mechanisms have been suggested to retain energy and extend the
112 lifetime of the WSNs~\cite{rault2014energy}.
114 %The sensor system ought to have a lifetime long enough to satisfy the application necessities. The lifetime coverage maximization is one of the fundamental requirements of any area coverage protocol in WSN implementation~\cite{nayak2010wireless}. In order to increase the reliability and prevent the possession of coverage holes (some parts are not covered in the area of interest) in the WSN, it is necessary to deploy the WSN with high density so as to increase the reliability and to exploit redundancy by using energy-efficient activity scheduling approaches.
116 %From a certain standpoint, the high coverage ratio is required by many applications such as military and health-care. Therefore, a suitable number of sensors are being chosen so as to cover the area of interest, is the first challenge. Meanwhile, the sensor nodes have a limited capabilities in terms of memory, processing, communication, and battery power being the most important and critical one. So, the main question is: how to extend the lifetime coverage of WSN as long time as possible?. There are many energy-efficient mechanisms have been suggested to retain energy and extend the lifetime of the WSNs~\cite{rault2014energy}.
118 %\uppercase{\textbf{Our contributions.}}
120 This paper makes the following contributions.
122 \item We devise a framework to schedule nodes to be activated alternatively such
123 that the network lifetime is prolonged while ensuring that a certain level of
124 coverage is preserved. A key idea in our framework is to exploit spatial an
125 temporal subdivision. On the one hand the area of interest if divided into
126 several smaller subregions and on the other hand the time line is divided into
127 periods of equal length. In each subregion the sensor nodes will cooperatively
128 choose a leader which will schedule nodes' activities, and this grouping of
129 sensors is similar to typical cluster architecture.
130 \item We propose a new mathematical optimization model. Instead of trying to
131 cover a set of specified points/targets as in most of the methods proposed in
132 the literature, we formulate an integer program based on perimeter coverage of
133 each sensor. The model involves integer variables to capture the deviations
134 between the actual level of coverage and the required level. So that an
135 optimal scheduling will be obtained by minimizing a weighted sum of these
137 \item We conducted extensive simulation experiments, using the discrete event
138 simulator OMNeT++, to demonstrate the efficiency of our protocol. We compared
139 our LiCO protocol to two approaches found in the literature:
140 DESK~\cite{ChinhVu} and GAF~\cite{xu2001geography}, and also to our previous
141 work published in~\cite{Idrees2} which is based on another optimization model
142 for sensor scheduling.
145 %Two combined integrated energy-efficient techniques have been used by LiCO protocol in order to maximize the lifetime coverage in WSN: the first, by dividing the area of interest into several smaller subregions based on divide-and-conquer method and then one leader elected for each subregion in an independent, distributed, and simultaneous way by the cooperation among the sensor nodes within each subregion, and this similar to cluster architecture;
146 % the second, activity scheduling based new optimization model has been used to provide the optimal cover set that will take the mission of sensing during current period. This optimization algorithm is based on a perimeter-coverage model so as to optimize the shared perimeter among the sensors in each subregion, and this represents as a energu-efficient control topology mechanism in WSN.
148 The rest of the paper is organized as follows. In the next section we review
149 some related work in the field. Section~\ref{sec:The LiCO Protocol Description}
150 is devoted to the LiCO protocol description and Section~\ref{cp} focuses on the
151 coverage model formulation which is used to schedule the activation of sensor
152 nodes. Section~\ref{sec:Simulation Results and Analysis} presents simulations
153 results and discusses the comparison with other approaches. Finally, concluding
154 remarks are drawn and some suggestions given for future works in
155 Section~\ref{sec:Conclusion and Future Works}.
157 % that show that our protocol outperforms others protocols.
158 \section{Related Literature}
159 \label{sec:Literature Review}
161 \noindent In this section, we summarize some related works regarding the
162 coverage problem and distinguish our LiCO protocol from the works presented in
165 The most discussed coverage problems in literature can be classified in three
166 categories~\cite{li2013survey} according to their respective monitoring
167 objective. Hence, area coverage \cite{Misra} means that every point inside a
168 fixed area must be monitored, while target coverage~\cite{yang2014novel} refer
169 to the objective of coverage for a finite number of discrete points called
170 targets, and barrier coverage~\cite{HeShibo}\cite{kim2013maximum} focuses on
171 preventing intruders from entering into the region of interest. In
172 \cite{Deng2012} authors transform the area coverage problem to the target
173 coverage one taking into account the intersection points among disks of sensors
174 nodes or between disk of sensor nodes and boundaries. In
175 \cite{Huang:2003:CPW:941350.941367} authors prove that if the perimeters of
176 sensors are sufficiently covered it will be the case for the whole area. They
177 provide an algorithm in $O(nd~log~d)$ time to compute the perimeter-coverage of
178 each sensor, where $d$ denotes the maximum number of sensors that are
179 neighboring to a sensor and $n$ is the total number of sensors in the
180 network. {\it In LiCO protocol, instead of determining the level of coverage of
181 a set of discrete points, our optimization model is based on checking the
182 perimeter-coverage of each sensor to activate a minimal number of sensors.}
184 The major approach to extend network lifetime while preserving coverage is to
185 divide/organize the sensors into a suitable number of set covers (disjoint or
186 non-disjoint), where each set completely covers a region of interest, and to
187 activate these set covers successively. The network activity can be planned in
188 advance and scheduled for the entire network lifetime or organized in periods,
189 and the set of active sensor nodes is decided at the beginning of each period
190 \cite{ling2009energy}. Active node selection is determined based on the problem
191 requirements (e.g. area monitoring, connectivity, or power efficiency). For
192 instance, Jaggi {\em et al.}~\cite{jaggi2006} address the problem of maximizing
193 the lifetime by dividing sensors into the maximum number of disjoint subsets
194 such that each subset can ensure both coverage and connectivity. A greedy
195 algorithm is applied once to solve this problem and the computed sets are
196 activated in succession to achieve the desired network lifetime. Vu
197 \cite{chin2007}, Padmatvathy {\em et al.}~\cite{pc10}, propose algorithms
198 working in a periodic fashion where a cover set is computed at the beginning of
199 each period. {\it Motivated by these works, LiCO protocol works in periods,
200 where each period contains a preliminary phase for information exchange and
201 decisions, followed by a sensing phase where one cover set is in charge of the
204 Various centralized and distributed approaches, or even a mixing of these two
205 concepts, have been proposed to extend the network lifetime. In distributed
206 algorithms~\cite{yangnovel,ChinhVu,qu2013distributed} each sensors decides of
207 its own activity scheduling after an information exchange with its neighbors.
208 The main interest of a such approach is to avoid long range communications and
209 thus to reduce the energy dedicated to the communications. Unfortunately, since
210 each node has only information on its immediate neighbors (usually the one-hop
211 ones) it may take a bad decision leading to a global suboptimal solution.
212 Conversely, centralized
213 algorithms~\cite{cardei2005improving,zorbas2010solving,pujari2011high} always
214 provide nearly or close to optimal solution since the algorithm has a global
215 view of the whole network. The disadvantage of a centralized method is obviously
216 its high cost in communications needed to transmit to a single node, the base
217 station which will globally schedule nodes' activities, data from all the other
218 sensor nodes in the area. The price in communications can be very huge since
219 long range communications will be needed. In fact the larger the WNS, the higher
220 the communication and thus energy cost. {\it In order to be suitable for
221 large-scale networks, in the LiCO protocol the area of interest is divided
222 into several smaller subregions, and in each one, a node called the leader is
223 in charge for selecting the active sensors for the current period. Thus our
224 protocol is scalable and a globally distributed method, whereas it is
225 centralized in each subregion.}
227 Various coverage scheduling algorithms have been developed this last years.
228 Many of them, dealing with the maximization of the number of cover sets, are
229 heuristics. These heuristics involve the construction of a cover set by
230 including in priority the sensor nodes which cover critical targets, that is to
231 say targets that are covered by the smallest number of sensors
232 \cite{berman04,zorbas2010solving}. Other approaches are based on mathematical
233 programming formulations~\cite{cardei2005energy,5714480,pujari2011high,Yang2014}
234 and dedicated techniques (solving with a branch-and-bound algorithm available in
235 optimization solver). The problem is formulated as an optimization problem
236 (maximization of the lifetime or number of cover sets) under target coverage and
237 energy constraints. Column generation techniques, well-known and widely
238 practiced techniques for solving linear programs with too many variables, have
240 used~\cite{castano2013column,rossi2012exact,deschinkel2012column}. {\it In LiCO
241 protocol, each leader, in charge of a subregion, solves an integer program
242 which has a twofold objective: minimize the overcoverage and the undercoverage
243 of the perimeter of each sensor.}
245 %\noindent Recently, the coverage problem has been received a high attention, which concentrates on how the physical space could be well monitored after the deployment. Coverage is one of the Quality of Service (QoS) parameters in WSNs, which is highly concerned with power depletion~\cite{zhu2012survey}. Most of the works about the coverage protocols have been suggested in the literature focused on three types of the coverage in WSNs~\cite{mulligan2010coverage}: the first, area coverage means that each point in the area of interest within the sensing range of at least one sensor node; the second, target coverage in which a fixed set of targets need to be monitored; the third, barrier coverage refers to detect the intruders crossing a boundary of WSN. The work in this paper emphasized on the area coverage, so, some area coverage protocols have been reviewed in this section, and the shortcomings of reviewed approaches are being summarized.
247 %The problem of k-coverage in WSNs was addressed~\cite{ammari2012centralized}. It mathematically formulated and the spacial sensor density for full k-coverage determined, where the relation between the communication range and the sensing range constructed by this work to retain the k-coverage and connectivity in WSN. After that, a four configuration protocols have proposed for treating the k-coverage in WSNs.
249 %In~\cite{rebai2014branch}, the problem of full grid coverage is formulated using two integer linear programming models: the first, a model that takes into account only the overall coverage constraint; the second, both the connectivity and the full grid coverage constraints have taken into consideration. This work did not take into account the energy constraint.
251 %Li et al.~\cite{li2011transforming} presented a framework to convert any complete coverage problem to a partial coverage one with any coverage ratio by means of executing a complete coverage algorithm to find a full coverage sets with virtual radii and transforming the coverage sets to a partial coverage sets by adjusting sensing radii. The properties of the original algorithms can be maintained by this framework and the transformation process has a low execution time.
253 %The authors in~\cite{liu2014generalized} explained that in some applications of WSNs such as structural health monitoring (SHM) and volcano monitoring, the traditional coverage model which is a geographic area defined for individual sensors is not always valid. For this reason, they define a generalized coverage model, which is not need to have the coverage area of individual nodes, but only based on a function to determine whether a set of
254 %sensor nodes is capable of satisfy the requested monitoring task for a certain area. They have proposed two approaches to divide the deployed nodes into suitable cover sets, which can be used to prolong the network lifetime.
256 %The work in~\cite{wang2010preserving} addressed the target area coverage problem by proposing a geometric-based activity scheduling scheme, named GAS, to fully cover the target area in WSNs. The authors deals with small area (target area coverage), which can be monitored by a single sensor instead of area coverage, which focuses on a large area that should be monitored by many sensors cooperatively. They explained that GAS is capable to monitor the target area by using a few sensors as possible and it can produce as many cover sets as possible.
258 %Cho et al.~\cite{cho2007distributed} proposed a distributed node scheduling protocol, which can retain sensing coverage needed by applications
259 %and increase 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 and by compute it's ESA can be determine whether it will be active or sleep. The suggested work permits to sensor nodes to be in sleep mode opportunistically whilst fulfill the needed sensing coverage.
261 %In~\cite{quang2008algorithm}, the authors defined a maximum sensing coverage region problem (MSCR) in WSNs and then proposed an algorithm to solve it. The
262 %maximum observed area fully covered by a minimum active sensors. In this work, the major property is to getting rid from the redundant sensors in high-density WSNs and putting them in sleep mode, and choosing a smaller number of active sensors so as to be sure that the full area is k-covered, and all events appeared in that area can be precisely and timely detected. This algorithm minimized the total energy consumption and increased the lifetime.
264 %A novel method to divide the sensors in the WSN, called node coverage grouping (NCG) suggested~\cite{lin2010partitioning}. The sensors in the connectivity group are within sensing range of each other, and the data collected by them in the same group are supposed to be similar. They are proved that dividing n sensors via NCG into connectivity groups is a NP-hard problem. So, a heuristic algorithm of NCG with time complexity of $O(n^3)$ is proposed.
265 %For some applications, such as monitoring an ecosystem with extremely diversified environment, It might be premature assumption that sensors near to each other sense similar data.
267 %In~\cite{zaidi2009minimum}, the problem of minimum cost coverage in which full coverage is performed by using the minimum number of sensors for an arbitrary geometric shape region is addressed. a geometric solution to the minimum cost coverage problem under a deterministic deployment is proposed. The probabilistic coverage solution which provides a relationship between the probability of coverage and the number of randomly deployed sensors in an arbitrarily-shaped region is suggested. The authors are clarified that with a random deployment about seven times more nodes are required to supply full coverage.
269 %A graph theoretical framework for connectivity-based coverage with configurable coverage granularity was proposed~\cite{dong2012distributed}. A new coverage criterion and scheduling approach is proposed based on cycle partition. This method is capable of build a sparse coverage set in distributed way by means of only connectivity information. This work considers only the communication range of the sensor is smaller two times the sensing range of sensor.
271 %Liu et al.~\cite{liu2010energy} formulated maximum disjoint sets problem for retaining coverage and connectivity in WSN. Two algorithms are proposed for solving this problem, heuristic algorithm and network flow algorithm. This work did not take into account the sensor node failure, which is an unpredictable event because the two solutions are full centralized algorithms.
273 %The work that presented in~\cite{aslanyan2013optimal} solved the coverage and connectivity problem in sensor networks in
274 %an integrated way. The network lifetime is divided in a fixed number of rounds. A coverage bitmap of sensors of the domain has been generated in each round and based on this bitmap, it has been decided which sensors
275 %stay active or turn it to sleep. They checked the connection of the graph via laplacian of adjancy graph of active sensors in each round. the generation of coverage bitmap by using Minkowski technique, the network is able to providing the desired ratio of coverage. They have been defined the connected coverage problem as an optimization problem and a centralized genetic algorithm is used to find the solution.
277 %Several algorithms to retain the coverage and maximize the network lifetime were proposed in~\cite{cardei2006energy,wang2011coverage}.
279 %\uppercase{\textbf{shortcomings}}. In spite of many energy-efficient protocols for maintaining the coverage and improving the network lifetime in WSNs were proposed, non of them ensure the coverage for the sensing field with optimal minimum number of active sensor nodes, and for a long time as possible. For example, in a full centralized algorithms, an optimal solutions can be given by using optimization approaches, but in the same time, a high energy is consumed for the execution time of the algorithm and the communications among the sensors in the sensing field, so, the full centralized approaches are not good candidate to use it especially in large WSNs. Whilst, a full distributed algorithms can not give optimal solutions because this algorithms use only local information of the neighboring sensors, but in the same time, the energy consumption during the communications and executing the algorithm is highly lower. Whatever the case, this would result in a shorter lifetime coverage in WSNs.
281 %\uppercase{\textbf{Our Protocol}}. In this paper, a Lifetime Coverage Optimization Protocol, called (LiCO) in WSNs is suggested. The sensing field is divided into smaller subregions by means of divide-and-conquer method, and a LiCO protocol is distributed in each sensor in the subregion. The network lifetime in each subregion is divided into periods, each period includes 4 stages: Information Exchange, Leader election, decision based activity scheduling optimization, and sensing. The leaders are elected in an independent, asynchronous, and distributed way in all the subregions of the WSN. After that, energy-efficient activity scheduling mechanism based new optimization model is performed by each leader in the subregions. This optimization model is based on the perimeter coverage model in order to producing the optimal cover set of active sensors, which are taken the responsibility of sensing during the current period. LiCO protocol merges between two energy efficient mechanisms, which are used the main advantages of the centralized and distributed approaches and avoids the most of their disadvantages.
283 \section{ The LiCO Protocol Description}
284 \label{sec:The LiCO Protocol Description}
286 \noindent In this section, we describe in details our Lifetime Coverage
287 Optimization protocol. First we present the assumptions we made and the models
288 we considered (in particular the perimeter coverage one), second we describe the
289 background idea of our protocol, and third we give the outline of the algorithm
290 executed by each node.
292 % It is based on two efficient-energy mechanisms: the first, is partitioning the sensing field into smaller subregions, and one leader is elected for each subregion; the second, a sensor activity scheduling based new optimization model so as to produce the optimal cover set of active sensors for the sensing stage during the period. Obviously, these two mechanisms can be contribute in extend the network lifetime coverage efficiently.
293 %Before proceeding in the presentation of the main ideas of the protocol, we will briefly describe the perimeter coverage model and give some necessary assumptions and definitions.
295 \subsection{Assumptions and Models}
298 \noindent A WSN consisting of $J$ stationary sensor nodes randomly and uniformly
299 distributed in a bounded sensor field is considered. The wireless sensors are
300 deployed in high density to ensure initially a high coverage ratio of the area
301 of interest. We assume that all the sensor nodes are homogeneous in terms of
302 communication, sensing, and processing capabilities and heterogeneous from
303 energy provision point of view. The location information is available to a
304 sensor node either through hardware such as embedded GPS or location discovery
305 algorithms. We assume that each sensor node can directly transmit its
306 measurements to a mobile sink node. For example, a sink can be an unmanned
307 aerial vehicle (UAV) flying regularly over the sensor field to collect
308 measurements from sensor nodes. A mobile sink node collects the measurements and
309 transmits them to the base station. We consider a Boolean disk coverage model,
310 which is the most widely used sensor coverage model in the literature, and all
311 sensor nodes have a constant sensing range $R_s$. Thus, all the space points
312 within a disk centered at a sensor with a radius equal to the sensing range are
313 said to be covered by this sensor. We also assume that the communication range
314 $R_c$ satisfies $R_c \geq 2 \cdot R_s$. In fact, Zhang and Zhou~\cite{Zhang05}
315 proved that if the transmission range fulfills the previous hypothesis, a
316 complete coverage of a convex area implies connectivity among active nodes.
318 \indent LiCO protocol uses the same perimeter-coverage model than Huang and
319 Tseng in~\cite{huang2005coverage}. It can be expressed as follows: a sensor is
320 said to be perimeter covered if all the points on its perimeter are covered by
321 at least one sensor other than itself. They proved that a network area is
322 $k$-covered if and only if each sensor in the network is $k$-perimeter-covered.
323 %According to this model, we named the intersections among the sensor nodes in the sensing field as intersection points. Instead of working with the coverage area, we consider for each sensor a set of intersection points which are determined by using perimeter-coverage model.
324 Figure~\ref{pcm2sensors}(a) shows the coverage of sensor node~$0$. On this
325 figure, we can see that sensor~$0$ has nine neighbors and we have reported on
326 its perimeter (the perimeter of the disk covered by the sensor) for each
327 neighbor the two points resulting from intersection of the two sensing
328 areas. These points are denoted for neighbor~$i$ by $iL$ and $iR$, respectively
329 for left and right from neighbor point of view. The resulting couples of
330 intersection points subdivide the perimeter of sensor~$0$ into portions called
335 \begin{tabular}{@{}cr@{}}
336 \includegraphics[width=75mm]{pcm.jpg} & \raisebox{3.25cm}{(a)}
337 \\ \includegraphics[width=75mm]{twosensors.jpg} & \raisebox{2.75cm}{(b)}
339 \caption{Perimeter coverage of sensor node 0 (a) and finding the arc of $u$'s
340 perimeter covered by $v$.}
344 Figure~\ref{pcm2sensors}(b) describes the geometric information used to find the
345 locations of the left and right points of an arc on the perimeter of a sensor
346 node~$u$ covered by a sensor node~$v$. Node~$s$ is supposed to be located on the
347 west side of sensor~$u$, with the following respective coordinates in the
348 sensing area~: $(v_x,v_y)$ and $(u_x,u_y)$. From the previous coordinates we can
349 compute the euclidean distance between nodes~$u$ and $v$: $Dist(u,v)=\sqrt{\vert
350 u_x - v_x \vert^2 + \vert u_y-v_y \vert^2}$, while the angle~$\alpha$ is
351 obtained through the formula $\alpha = arccos \left(\dfrac{Dist(u,v)}{2R_s}
352 \right)$. So, the arc on the perimeter of node~$u$ defined by the angular
353 interval $[\pi - \alpha,\pi + \alpha]$ is said to be perimeter-covered by sensor
356 Every couple of intersection points is placed on the angular interval $[0,2\pi]$
357 in a counterclockwise manner, leading to a partitioning of the interval.
358 Figure~\ref{pcm2sensors}(a) illustrates the arcs for the nine neighbors of
359 sensor $0$ and figure~\ref{expcm} gives the position of the corresponding arcs
360 in the interval $[0,2\pi]$. More precisely, we can see that the points are
361 ordered according to the measures of the angles defined by their respective
362 positions. The intersection points are then visited one after another, starting
363 from first intersection point after point~zero, and the maximum level of
364 coverage is determined for each interval defined by two successive points. The
365 maximum level of coverage is equal to the number of overlapping arcs. For
366 example, between~$5L$ and~$6L$ the maximum level of coverage is equal to $3$
367 (the value is highlighted in yellow at the bottom of figure~\ref{expcm}), which
368 means that at most 2~neighbors can cover the perimeter in addition to node $0$.
369 Table~\ref{my-label} summarizes for each coverage interval the maximum level of
370 coverage and the sensor nodes covering the perimeter. The example discussed
371 above is thus given by the sixth line of the table.
373 %The points reported on the line segment $[0,2\pi]$ separates it in intervals as shown in figure~\ref{expcm}. For example, for each neighboring sensor of sensor 0, place the points $\alpha^ 1_L$, $\alpha^ 1_R$, $\alpha^ 2_L$, $\alpha^ 2_R$, $\alpha^ 3_L$, $\alpha^ 3_R$, $\alpha^ 4_L$, $\alpha^ 4_R$, $\alpha^ 5_L$, $\alpha^ 5_R$, $\alpha^ 6_L$, $\alpha^ 6_R$, $\alpha^ 7_L$, $\alpha^ 7_R$, $\alpha^ 8_L$, $\alpha^ 8_R$, $\alpha^ 9_L$, and $\alpha^ 9_R$; on the line segment $[0,2\pi]$, and then sort all these points in an ascending order into a list. Traverse the line segment $[0,2\pi]$ by visiting each point in the sorted list from left to right and determine the coverage level of each interval of the sensor 0 (see figure \ref{expcm}). For each interval, we sum up the number of parts of segments, and we deduce a level of coverage for each interval. For instance, the interval delimited by the points $5L$ and $6L$ contains three parts of segments. That means that this part of the perimeter of the sensor $0$ may be covered by three sensors, sensor $0$ itself and sensors $2$ and $5$. The level of coverage of this interval may reach $3$ if all previously mentioned sensors are active. Let say that sensors $0$, $2$ and $5$ are involved in the coverage of this interval. Table~\ref{my-label} summarizes the level of coverage for each interval and the sensors involved in for sensor node 0 in figure~\ref{pcm2sensors}(a).
374 % to determine the level of the perimeter coverage for each left and right point of a segment.
378 \includegraphics[width=137.5mm]{expcm.pdf}
379 \caption{Maximum coverage levels for perimeter of sensor node $0$.}
383 %For example, consider the sensor node $0$ in figure~\ref{pcmfig}, which has 9 neighbors. Figure~\ref{expcm} shows the perimeter coverage level for all left and right points of a segment that covered by a neighboring sensor nodes. Based on the figure~\ref{expcm}, the set of sensors for each left and right point of the segments illustrated in figure~\ref{ex2pcm} for the sensor node 0.
389 \includegraphics[width=90mm]{ex2pcm.jpg}
390 \caption{Coverage intervals and contributing sensors for sensor node 0.}
397 \caption{Coverage intervals and contributing sensors for sensor node 0.}
398 \begin{tabular}{|c|c|c|c|c|c|c|c|c|}
400 \begin{tabular}[c]{@{}c@{}}Left \\ point \\ angle~$\alpha$ \end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ left \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Interval \\ right \\ point\end{tabular} & \begin{tabular}[c]{@{}c@{}}Maximum \\ coverage\\ level\end{tabular} & \multicolumn{5}{c|}{\begin{tabular}[c]{@{}c@{}}Set of sensors\\ involved \\ in interval coverage\end{tabular}} \\ \hline
401 0.0291 & 1L & 2L & 4 & 0 & 1 & 3 & 4 & \\ \hline
402 0.104 & 2L & 3R & 5 & 0 & 1 & 3 & 4 & 2 \\ \hline
403 0.3168 & 3R & 4R & 4 & 0 & 1 & 4 & 2 & \\ \hline
404 0.6752 & 4R & 1R & 3 & 0 & 1 & 2 & & \\ \hline
405 1.8127 & 1R & 5L & 2 & 0 & 2 & & & \\ \hline
406 1.9228 & 5L & 6L & 3 & 0 & 2 & 5 & & \\ \hline
407 2.3959 & 6L & 2R & 4 & 0 & 2 & 5 & 6 & \\ \hline
408 2.4258 & 2R & 7L & 3 & 0 & 5 & 6 & & \\ \hline
409 2.7868 & 7L & 8L & 4 & 0 & 5 & 6 & 7 & \\ \hline
410 2.8358 & 8L & 5R & 5 & 0 & 5 & 6 & 7 & 8 \\ \hline
411 2.9184 & 5R & 7R & 4 & 0 & 6 & 7 & 8 & \\ \hline
412 3.3301 & 7R & 9R & 3 & 0 & 6 & 8 & & \\ \hline
413 3.9464 & 9R & 6R & 4 & 0 & 6 & 8 & 9 & \\ \hline
414 4.767 & 6R & 3L & 3 & 0 & 8 & 9 & & \\ \hline
415 4.8425 & 3L & 8R & 4 & 0 & 3 & 8 & 9 & \\ \hline
416 4.9072 & 8R & 4L & 3 & 0 & 3 & 9 & & \\ \hline
417 5.3804 & 4L & 9R & 4 & 0 & 3 & 4 & 9 & \\ \hline
418 5.9157 & 9R & 1L & 3 & 0 & 3 & 4 & & \\ \hline
425 %The optimization algorithm that used by LiCO protocol based on the perimeter coverage levels of the left and right points of the segments and worked to minimize the number of sensor nodes for each left or right point of the segments within each sensor node. The algorithm minimize the perimeter coverage level of the left and right points of the segments, while, it assures that every perimeter coverage level of the left and right points of the segments greater than or equal to 1.
427 In LiCO protocol, scheduling of sensor nodes' activities is formulated with an
428 integer program based on coverage intervals. The formulation of the coverage
429 optimization problem is detailed in~section~\ref{cp}. Note that when a sensor
430 node has a part of its sensing range outside the WSN sensing field, as in
431 figure~\ref{ex4pcm}, the maximum coverage level for this arc is set to $\infty$
432 and the corresponding interval will not be taken into account by the
433 optimization algorithm.
437 \includegraphics[width=62.5mm]{ex4pcm.jpg}
438 \caption{Sensing range outside the WSN's area of interest.}
441 %Figure~\ref{ex5pcm} gives an example to compute the perimeter coverage levels for the left and right points of the segments for a sensor node $0$, which has a part of its sensing range exceeding the border of the sensing field of WSN, and it has a six neighbors. In figure~\ref{ex5pcm}, the sensor node $0$ has two segments outside the border of the network sensing field, so the left and right points of the two segments called $-1L$, $-1R$, $-2L$, and $-2R$.
444 %\includegraphics[width=75mm]{ex5pcm.jpg}
445 %\caption{Coverage intervals and contributing sensors for sensor node 0 having a part of its sensing range outside the border.}
449 \subsection{The Main Idea}
451 \noindent The WSN area of interest is, in a first step, divided into regular
452 homogeneous subregions using a divide-and-conquer algorithm. In a second step
453 our protocol will be executed in a distributed way in each subregion
454 simultaneously to schedule nodes' activities for one sensing period.
456 As shown in figure~\ref{fig2}, node activity scheduling is produced by our
457 protocol in a periodic manner. Each period is divided into 4 stages: Information
458 (INFO) Exchange, Leader Election, Decision (the result of an optimization
459 problem), and Sensing. For each period there is exactly one set cover
460 responsible for the sensing task. Protocols based on a periodic scheme, like
461 LiCO, are more robust against an unexpected node failure. On the one hand, if
462 node failure is discovered before taking the decision, the corresponding sensor
463 node will not be considered by the optimization algorithm, and, on the other
464 hand, if the sensor failure happens after the decision, the sensing task of the
465 network will be temporarily affected: only during the period of sensing until a
466 new period starts, since a new set cover will take charge of the sensing task in
467 the next period. The energy consumption and some other constraints can easily be
468 taken into account since the sensors can update and then exchange their
469 information (including their residual energy) at the beginning of each period.
470 However, the pre-sensing phases (INFO Exchange, Leader Election, and Decision)
471 are energy consuming, even for nodes that will not join the set cover to monitor
476 \includegraphics[width=80mm]{Model.pdf}
477 \caption{LiCO protocol}
481 We define two types of packets to be used by LiCO protocol:
482 %\begin{enumerate}[(a)]
484 \item INFO packet: sent by each sensor node to all the nodes inside a same
485 subregion for information exchange.
486 \item ActiveSleep packet: sent by the leader to all the nodes in its subregion
487 to transmit to them their respective status (stay Active or go Sleep) during
492 Five status are possible for a sensor node in the network:
493 %\begin{enumerate}[(a)]
495 \item LISTENING: waits for a decision (to be active or not);
496 \item COMPUTATION: executes the optimization algorithm as leader to
497 determine the activities scheduling;
498 \item ACTIVE: node is sensing;
499 \item SLEEP: node is turned off;
500 \item COMMUNICATION: transmits or recevives packets.
503 %Below, we describe each phase in more details.
505 \subsection{LiCO Protocol Algorithm}
507 \noindent The pseudocode implementing the protocol on a node is given below.
508 More precisely, Algorithm~\ref{alg:LiCO} gives a brief description of the
509 protocol applied by a sensor node $s_k$ where $k$ is the node index in the WSN.
511 \begin{algorithm}[h!]
512 % \KwIn{all the parameters related to information exchange}
513 % \KwOut{$winer-node$ (: the id of the winner sensor node, which is the leader of current round)}
515 %\emph{Initialize the sensor node and determine it's position and subregion} \;
517 \If{ $RE_k \geq E_{th}$ }{
518 \emph{$s_k.status$ = COMMUNICATION}\;
519 \emph{Send $INFO()$ packet to other nodes in subregion}\;
520 \emph{Wait $INFO()$ packet from other nodes in subregion}\;
521 \emph{Update K.CurrentSize}\;
522 \emph{LeaderID = Leader election}\;
523 \If{$ s_k.ID = LeaderID $}{
524 \emph{$s_k.status$ = COMPUTATION}\;
526 \If{$ s_k.ID $ is Not previously selected as a Leader }{
527 \emph{ Execute the perimeter coverage model}\;
528 % \emph{ Determine the segment points using perimeter coverage model}\;
531 \If{$ (s_k.ID $ is the same Previous Leader) And (K.CurrentSize = K.PreviousSize)}{
533 \emph{ Use the same previous cover set for current sensing stage}\;
536 \emph{Update $a^j_{ik}$; prepare data for IP~Algorithm}\;
537 \emph{$\left\{\left(X_{1},\dots,X_{l},\dots,X_{K}\right)\right\}$ = Execute Integer Program Algorithm($K$)}\;
538 \emph{K.PreviousSize = K.CurrentSize}\;
541 \emph{$s_k.status$ = COMMUNICATION}\;
542 \emph{Send $ActiveSleep()$ to each node $l$ in subregion} \;
543 \emph{Update $RE_k $}\;
546 \emph{$s_k.status$ = LISTENING}\;
547 \emph{Wait $ActiveSleep()$ packet from the Leader}\;
548 \emph{Update $RE_k $}\;
551 \Else { Exclude $s_k$ from entering in the current sensing stage}
554 \caption{LiCO($s_k$)}
559 In this algorithm, K.CurrentSize and K.PreviousSize refer to the current size
560 and the previous size of the subnetwork in the subregion respectively. That
561 means the number of sensor nodes which are still alive. Initially, the sensor
562 node checks its remaining energy $RE_k$, which must be greater than a threshold
563 $E_{th}$ in order to participate in the current period. Each sensor node
564 determines its position and its subregion using an embedded GPS or a location
565 discovery algorithm. After that, all the sensors collect position coordinates,
566 remaining energy, sensor node ID, and the number of its one-hop live neighbors
567 during the information exchange. The sensors inside a same region cooperate to
568 elect a leader. The selection criteria for the leader, in order of priority,
569 are: larger number of neighbors, larger remaining energy, and then in case of
570 equality, larger index. Once chosen, the leader collects information to
571 formulate and solve the integer program which allows to construct the set of
572 active sensors in the sensing stage.
574 %After the cooperation among the sensor nodes in the same subregion, the leader will be elected in distributed way, where each sensor node and based on it's information decide who is the leader. The selection criteria for the leader in order of priority are: larger number of neighbors, larger remaining energy, and then in case of equality, larger index. Thereafter, if the sensor node is leader, it will execute the perimeter-coverage model for each sensor in the subregion in order to determine the segment points which would be used in the next stage by the optimization algorithm of the LiCO protocol. Every sensor node is selected as a leader, it is executed the perimeter coverage model only one time during it's life in the network.
576 % The leader has the responsibility of applying the integer program algorithm (see section~\ref{cp}), which provides a set of sensors planned to be active in the sensing stage. As leader, it will send an Active-Sleep packet to each sensor in the same subregion to inform 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 stage.
578 \section{Lifetime Coverage problem formulation}
581 \noindent In this section, the coverage model is mathematically formulated. We
582 start with a description of the notations that will be used throughout the
585 First, we have the following sets:
587 \item $S$ represents the set of WSN sensor nodes;
588 \item $A \subseteq S $ is the subset of alive sensors;
589 \item $I_j$ designates the set of coverage intervals (CI) obtained for
592 $I_j$ refers to the set of coverage intervals which have been defined according
593 to the method introduced in subsection~\ref{CI}. For a coverage interval $i$,
594 let $a^j_{ik}$ denote the indicator function of whether sensor~$k$ is involved
595 in coverage interval~$i$ of sensor~$j$, that is:
599 1 & \mbox{if sensor $k$ is involved in the } \\
600 & \mbox{coverage interval $i$ of sensor $j$}, \\
601 0 & \mbox{otherwise.}\\
606 Note that $a^k_{ik}=1$ by definition of the interval.
607 %, where the objective is to find the maximum number of non-disjoint sets of sensor nodes such that each set cover can assure the coverage for the whole region so as to extend the network lifetime in WSN. Our model uses the PCL~\cite{huang2005coverage} in order to optimize the lifetime coverage in each subregion.
608 %We defined some parameters, which are related to our optimization model. In our model, we consider binary variables $X_{k}$, which determine the activation of sensor $k$ in the sensing round $k$. .
609 Second, we define several binary and integer variables. Hence, each binary
610 variable $X_{k}$ determines the activation of sensor $k$ in the sensing phase
611 ($X_k=1$ if the sensor $k$ is active or 0 otherwise). $M^j_i$ is an integer
612 variable which measures the undercoverage for the coverage interval $i$
613 corresponding to sensor~$j$. In the same way, the overcoverage for the same
614 coverage interval is given by the variable $V^j_i$.
616 If we decide to sustain a level of coverage equal to $l$ all along the perimeter
617 of sensor $j$, we have to ensure that at least $l$ sensors involved in each
618 coverage interval $i \in I_j$ of sensor $j$ are active. According to the
619 previous notations, the number of active sensors in the coverage interval $i$ of
620 sensor $j$ is given by $\sum_{k \in A} a^j_{ik} X_k$. To extend the network
621 lifetime, the objective is to activate a minimal number of sensors in each
622 period to ensure the desired coverage level. As the number of alive sensors
623 decreases, it becomes impossible to reach the desired level of coverage for all
624 coverage intervals. Therefore we uses variables $M^j_i$ and $V^j_i$ as a measure
625 of the deviation between the desired number of active sensors in a coverage
626 interval and the effective number. And we try to minimize these deviations,
627 first to force the activation of a minimal number of sensors to ensure the
628 desired coverage level, and if the desired level cannot be completely satisfied,
629 to reach a coverage level as close as possible to the desired one.
631 %A system of linear constraints is imposed to attempt to keep the coverage level in each coverage interval to within specified PCL. Since it is physically impossible to satisfy all constraints simultaneously, each constraint uses a variable to either record when the coverage level is achieved, or to record the deviation from the desired coverage level. These additional variables are embedded into an objective function to be minimized.
633 %\noindent In this paper, let us define some parameters, which are used in our protocol.
634 %the set of segment points is denoted by $I$, the set of all sensors in the network by $J$, and the set of alive sensors within $J$ by $K$.
637 %\noindent \begin{equation}
640 % 1& \mbox{if sensor $k$ is active,} \\
641 % 0 & \mbox{otherwise.}\\
647 %\noindent $M^j_i (undercoverage): $ integer value $\in \mathbb{N}$ for segment point $i$ of sensor $j$.
649 %\noindent $V^j_i (overcoverage): $ integer value $\in \mathbb{N}$ for segment point $i$ of sensor $j$.
651 Our coverage optimization problem can then be mathematically expressed as follows:
653 \begin{equation} %\label{eq:ip2r}
656 \min \sum_{j \in S} \sum_{i \in I_j} (\alpha^j_i ~ M^j_i + \beta^j_i ~ V^j_i )&\\
657 \textrm{subject to :}&\\
658 \sum_{k \in A} ( a^j_{ik} ~ X_{k}) + M^j_i \geq l \quad \forall i \in I_j, \forall j \in S\\
660 \sum_{k \in A} ( a^j_{ik} ~ X_{k}) - V^j_i \leq l \quad \forall i \in I_j, \forall j \in S\\
662 % \Theta_{p}\in \mathbb{N}, &\forall p \in P\\
663 % U_{p} \in \{0,1\}, &\forall p \in P\\
664 X_{k} \in \{0,1\}, \forall k \in A
669 $\alpha^j_i$ and $\beta^j_i$ are nonnegative weights selected according to the
670 relative importance of satisfying the associated level of coverage. For example,
671 weights associated with coverage intervals of a specified part of a region may
672 be given a relatively larger magnitude than weights associated with another
673 region. This kind of integer program is inspired from the model developed for
674 brachytherapy treatment planning for optimizing dose distribution
675 \cite{0031-9155-44-1-012}. The integer program must be solved by the leader in
676 each subregion at the beginning of each sensing phase, whenever the environment
677 has changed (new leader, death of some sensors). Note that the number of
678 constraints in the model is constant (constraints of coverage expressed for all
679 sensors), whereas the number of variables $X_k$ decreases over periods, since we
680 consider only alive sensors (sensors with enough energy to be alive during one
681 sensing phase) in the model.
683 \section{Performance Evaluation and Analysis}
684 \label{sec:Simulation Results and Analysis}
685 %\noindent \subsection{Simulation Framework}
687 \subsection{Simulation Settings}
690 The WSN area of interest is supposed to be divided into 16~regular subregions
691 and we use the same energy consumption than in our previous work~\cite{Idrees2}.
692 Table~\ref{table3} gives the chosen parameters settings.
695 \caption{Relevant parameters for network initialization.}
698 % used for centering table
700 % centered columns (4 columns)
702 Parameter & Value \\ [0.5ex]
705 % inserts single horizontal line
706 Sensing field & $(50 \times 25)~m^2 $ \\
708 WSN size & 100, 150, 200, 250, and 300~nodes \\
710 Initial energy & in range 500-700~Joules \\
712 Sensing period & duration of 60 minutes \\
713 $E_{th}$ & 36~Joules\\
716 $\alpha^j_i$ & 0.6 \\
717 % [1ex] adds vertical space
723 % is used to refer this table in the text
725 To obtain experimental results which are relevant, simulations with five
726 different node densities going from 100 to 300~nodes were performed considering
727 each time 25~randomly generated networks. The nodes are deployed on a field of
728 interest of $(50 \times 25)~m^2 $ in such a way that they cover the field with a
729 high coverage ratio. Each node has an initial energy level, in Joules, which is
730 randomly drawn in the interval $[500-700]$. If it's energy provision reaches a
731 value below the threshold $E_{th}=36$~Joules, the minimum energy needed for a
732 node to stay active during one period, it will no more participate in the
733 coverage task. This value corresponds to the energy needed by the sensing phase,
734 obtained by multiplying the energy consumed in active state (9.72 mW) with the
735 time in seconds for one period (3600 seconds), and adding the energy for the
736 pre-sensing phases. According to the interval of initial energy, a sensor may
737 be active during at most 20 periods.
739 The values of $\alpha^j_i$ and $\beta^j_i$ have been chosen to ensure a good
740 network coverage and a longer WSN lifetime. We have given a higher priority for
741 the undercoverage (by setting the $\alpha^j_i$ with a larger value than
742 $\beta^j_i$) so as to prevent the non-coverage for the interval i of the sensor
743 j. On the other hand, we have given a little bit lower value for $\beta^j_i$ so
744 as to minimize the number of active sensor nodes which contribute in covering
747 We introduce the following performance metrics to evaluate the efficiency of our
750 %\begin{enumerate}[i)]
752 \item {\bf Network Lifetime}: the lifetime is defined as the time elapsed until
753 the coverage ratio falls below a fixed threshold. $Lifetime_{95}$ and
754 $Lifetime_{50}$ denote, respectively, the amount of time during which is
755 guaranteed a level of coverage greater than $95\%$ and $50\%$. The WSN can
756 fulfill the expected monitoring task until all its nodes have depleted their
757 energy or if the network is not more connected. This last condition is crucial
758 because without network connectivity a sensor may not be able to send to a
759 base station an event it has sensed.
760 \item {{\bf Coverage Ratio (CR)}:} it measures how well the WSN is able to
761 observe the area of interest. In our case, we discretized the sensor field as
762 a regular grid, which yields the following equation:
765 \mbox{CR}(\%) = \frac{\mbox{$n$}}{\mbox{$N$}} \times 100.
767 where $n$ is the number of covered grid points by active sensors of every
768 subregions during the current sensing phase and $N$ is total number of grid
769 points in the sensing field. In our simulations we have set a layout of
770 $N~=~51~\times~26~=~1326$~grid points.
772 % MICHEL TO BE CONTINUED FROM HERE
774 \item{{\bf Number of Active Sensors Ratio(ASR)}:} It is important to have as few active nodes as possible in each round,
775 in order to minimize the communication overhead and maximize the
776 network lifetime. The Active Sensors Ratio is defined as follows:
779 \mbox{ASR}(\%) = \frac{\sum\limits_{r=1}^R \mbox{$A_r$}}{\mbox{$S$}} \times 100 .
781 Where: $A_r^t$ is the number of active sensors in the subregion $r$ in the current sensing stage, $S$ is the total number of sensors in the network, and $R$ is the total number of the subregions in the network.
785 \item {{\bf Energy Consumption}:} energy consumption (EC) can be seen as the
786 total energy consumed by the sensors during $Lifetime_{95}$ or
787 $Lifetime_{50}$, divided by the number of periods. Formally, the computation
788 of EC can be expressed as follows:
791 \mbox{EC} = \frac{\sum\limits_{m=1}^{M} \left( E^{\mbox{com}}_m+E^{\mbox{list}}_m+E^{\mbox{comp}}_m
792 + E^{a}_m+E^{s}_m \right)}{M},
795 where $M$ corresponds to the number of periods. The total energy consumed by
796 the sensors (EC) comes through taking into consideration four main energy factors. The first one, denoted $E^{\scriptsize \mbox{com}}_m$, represent the
797 energy consumption spent by all the nodes for wireless communications during
798 period $m$. $E^{\scriptsize \mbox{list}}_m$, the next factor, corresponds to
799 the energy consumed by the sensors in LISTENING status before receiving the
800 decision to go active or sleep in period $m$. $E^{\scriptsize \mbox{comp}}_m$
801 refers to the energy needed by all the leader nodes to solve the integer program
802 during a period. Finally, $E^a_{m}$ and $E^s_{m}$ indicate the energy consumed
803 by the whole network in the sensing phase (active and sleeping nodes).
809 \subsection{Simulation Results}
810 In this section, we present the simulation results of LiCO protocol and the other protocols using a discrete event simulator OMNeT++ \cite{varga} to run different series of simulations. We implemented all protocols precisely 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, the original execution time on the laptop is multiplied by 2944.2 $\left(\frac{35330}{2} \times \frac{1}{6} \right)$ so as to use it by the energy consumption model especially, after the computation and listening. Employing the modeling language for Mathematical Programming (AMPL)~\cite{AMPL}, the associated integer program instance is generated in a standard format, which is then read and solved by the optimization solver GLPK (GNU linear Programming Kit available in the public domain) \cite{glpk} through a Branch-and-Bound method.
812 We compared LiCO protocol to three other approaches: the first, called DESK and proposed by ~\cite{ChinhVu} is a fully distributed coverage algorithm; the second, called GAF ~\cite{xu2001geography}, consists in dividing the region
813 into fixed squares. During the decision phase, in each square, one sensor is
814 chosen to remain active during the sensing phase; the third, DiLCO protocol~\cite{Idrees2} is an improved version on the work presented in ~\cite{idrees2014coverage}. Note that the LiCO protocol is based on the same framework as that of DiLCO. For these two protocols, the division of the region of interest in 16 subregions was chosen since it produces the best results. The difference between the two protocols relies on the use of the integer programming to provide the set of sensors that have to be activated in each sensing phase. Whereas DiLCO protocol tries to satisfy the coverage of a set of primary points, LiCO protocol tries to reach a desired level of coverage $l$ for each sensor's perimeter. In the experimentations, we chose a level of coverage equal to 1 ($l=1$).
816 \subsubsection{\textbf{Coverage Ratio}}
817 Figure~\ref{fig333} shows the average coverage ratio for 200 deployed nodes obtained with the four methods.
822 \includegraphics[scale=0.5] {R/CR.eps}
823 \caption{The coverage ratio for 200 deployed nodes}
827 DESK, GAF, and DiLCO provide a little better coverage ratio with 99.99\%, 99.91\%, and 99.02\% against 98.76\% produced by LiCO for the first periods. This is due to the fact that DiLCO protocol put in sleep mode redundant sensors using optimization (which lightly decreases the coverage ratio) while there are more active nodes in the case of others methods. But when the number of periods exceeds 70 periods, it clearly appears that LiCO provides a better coverage ratio and keeps a coverage ratio greater than 50\% for longer periods (15 more compared to DiLCO, 40 more compared to DESK).
829 %When the number of periods increases, coverage ratio produced by DESK and GAF protocols decreases. This is due to dead nodes. However, DiLCO protocol maintains almost a good coverage from the round 31 to the round 63 and it is close to LiCO protocol. The coverage ratio of LiCO protocol is better than other approaches from the period 64.
831 %because the optimization algorithm used by LiCO has been optimized the lifetime coverage based on the perimeter coverage model, so it provided acceptable coverage for a larger number of periods and prolonging the network lifetime based on the perimeter of the sensor nodes in each subregion of WSN. Although some nodes are dead, sensor activity scheduling based optimization of LiCO selected another nodes to ensure the coverage of the area of interest. i.e. DiLCO-16 showed a good coverage in the beginning then LiCO, when the number of periods increases, the coverage ratio decreases due to died sensor nodes. Meanwhile, thanks to sensor activity scheduling based new optimization model, which is used by LiCO protocol to ensure a longer lifetime coverage in comparison with other approaches.
834 \subsubsection{\textbf{Active Sensors Ratio}}
835 Having active nodes as few as possible in each period is essential in order to minimize the energy consumption and so maximize the network lifetime. Figure~\ref{fig444} shows the average active nodes ratio for 200 deployed nodes.
839 \includegraphics[scale=0.5]{R/ASR.eps}
840 \caption{The active sensors ratio for 200 deployed nodes }
844 We observe that DESK and GAF have 30.36 \% and 34.96 \% active nodes for the first fourteen rounds and DiLCO and LiCO protocols compete perfectly with only 17.92 \% and 20.16 \% active nodes during the same time interval. As the number of periods increases, LiCO protocol has a lower number of active nodes in comparison with the three other approaches, while keeping of greater coverage ratio as shown in figure \ref{fig333}.
846 \subsubsection{\textbf{The Energy Consumption}}
847 We study the effect of the energy consumed by the WSN during the communication, computation, listening, active, and sleep modes for different network densities and compare it for the four approaches. Figures~\ref{fig3EC95} and \ref{fig3EC50} illustrate the energy consumption for different network sizes and for $Lifetime95$ and $Lifetime50$.
851 \includegraphics[scale=0.5]{R/EC95.eps}
852 \caption{The Energy Consumption per period with $Lifetime_{95}$}
858 \includegraphics[scale=0.5]{R/EC50.eps}
859 \caption{The Energy Consumption per period with $Lifetime_{50}$}
863 The results show that our LiCO protocol is the most competitive from the energy consumption point of view. As shown in figures~\ref{fig3EC95} and \ref{fig3EC50}, LiCO consumes much less energy than the three other methods. One might think that the resolution of the integer program is too costly in energy, but the results show that it is very beneficial to lose a bit of time in the selection of sensors to activate. Indeed this optimization program allows to reduce significantly the number of active sensors and so the energy consumption while keeping a good coverage level.
864 %The optimization algorithm, which used by LiCO protocol, was improved the lifetime coverage efficiently based on the perimeter coverage model.
866 %The other approaches have a high energy consumption due to activating a larger number of sensors. 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.
869 %\subsubsection{Execution Time}
871 \subsubsection{\textbf{The Network Lifetime}}
872 We observe the superiority of LiCO and DiLCO protocols against other two approaches in prolonging the network lifetime. In figures~\ref{fig3LT95} and \ref{fig3LT50}, network lifetime, $Lifetime95$ and $Lifetime50$ respectively, are illustrated for different network sizes.
876 \includegraphics[scale=0.5]{R/LT95.eps}
877 \caption{The Network Lifetime for $Lifetime_{95}$}
884 \includegraphics[scale=0.5]{R/LT50.eps}
885 \caption{The Network Lifetime for $Lifetime_{50}$}
889 As highlighted by figures~\ref{fig3LT95} and \ref{fig3LT50}, the network lifetime obviously increases when the size of the network increases, and it is clearly larger with DiLCO and LiCO protocols compared with the two other methods. For instance, for a network of 300 sensors, the coverage ratio is greater than 50\% about two times longer with LiCO compared to DESK method.
891 %By choosing the best suited nodes, for each period, by optimizing the coverage and lifetime of the network to cover the area of interest and by letting the other ones sleep in order to be used later in next rounds, LiCO protocol efficiently prolonged the network lifetime especially for a coverage ratio greater than $50 \%$, whilst it stayed very near to DiLCO-16 protocol for $95 \%$.
892 Figure~\ref{figLTALL} introduces the comparisons of the lifetime coverage for different coverage ratios for LiCO and DiLCO protocols.
893 We denote by Protocol/50, Protocol/80, Protocol/85, Protocol/90, and Protocol/95 the amount of time during which the network can satisfy an area coverage greater than $50\%$, $80\%$, $85\%$, $90\%$, and $95\%$ respectively. Indeed there are applications that do not require a 100\% coverage of the surveillance region. LiCO might be an interesting method since it achieves a good balance between a high level coverage ratio and network lifetime.
897 \includegraphics[scale=0.5]{R/LTa.eps}
898 \caption{The Network Lifetime for different coverage ratios}
903 %Comparison shows that LiCO protocol, which are used distributed optimization over the subregions, is the more relevance one for most coverage ratios and WSN sizes because it is robust to network disconnection during the network lifetime as well as it consume less energy in comparison with other approaches. LiCO protocol gave acceptable coverage ratio for a larger number of periods using new optimization algorithm that based on a perimeter coverage model. It also means that distributing the algorithm in each node and subdividing the sensing field into many subregions, which are managed independently and simultaneously, is the most relevant way to maximize the lifetime of a network.
906 \section{\uppercase{Conclusion and Future Works}}
907 \label{sec:Conclusion and Future Works}
908 In this paper we have studied the problem of lifetime coverage optimization in
909 WSNs. We designed a protocol LiCO that schedules node' activities (wakeup and sleep) with the objective of maintaining a good coverage ratio while maximizing the network lifetime. This protocol is applied on each subregion of the area of interest. It works in periods and is based on the resolution of an integer program to select the subset of sensors operating in active mode for each period. Our work is original in so far as it proposes for the first time an integer program scheduling the activation of sensors based on their perimeter coverage level instead of using a set of targets/points to be covered.
914 %To cope with this problem, the area of interest is divided into a smaller subregions using divide-and-conquer method, and then a LiCO protocol for optimizing the lifetime coverage in each subregion. LiCO protocol combines two efficient techniques: network
915 %leader election, which executes the perimeter coverage model (only one time), the optimization algorithm, and sending the schedule produced by the optimization algorithm to other nodes in the subregion ; the second, sensor activity scheduling based optimization in which a new lifetime coverage optimization model is proposed. The main challenges include how to select the most efficient leader in each subregion and the best schedule of sensor nodes that will optimize the network lifetime coverage
917 %The network lifetime coverage in each subregion is divided into
918 %periods, each period consists of four stages: (i) Information Exchange,
919 %(ii) Leader Election, (iii) a Decision based new optimization model in order to
920 %select the nodes remaining active for the last stage, and (iv) Sensing.
921 We carried out severals simulations to evaluate the proposed protocol. The simulation results show that LiCO is is more energy-efficient than other approaches, with respect to lifetime, coverage ratio, active sensors ratio, and energy consumption.
922 %Indeed, when dealing with large and dense WSNs, a distributed optimization approach on the subregions of WSN like the one we are proposed allows to reduce the difficulty of a single global optimization problem by partitioning it in many smaller problems, one per subregion, that can be solved more easily.
924 We have identified different research directions that arise out of the work presented here.
925 We plan to extend our framework such that the schedules are planned for multiple periods in advance.
926 %in order to compute all active sensor schedules in only one step for many periods;
927 We also want to improve our integer program to take into account the heterogeneous sensors, which do not have the same energy, processing, sensing and communication capabilities;
928 %the third, we are investigating new optimization model based on the sensing range so as to maximize the lifetime coverage in WSN;
929 Finally, our final goal is to implement our protocol using a sensor-testbed to evaluate their performance in real world applications.
931 \section*{\uppercase{Acknowledgements}}
932 \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.
937 \ifCLASSOPTIONcaptionsoff
942 \bibliographystyle{IEEEtran}
943 %\bibliographystyle{IEEEbiographynophoto}
944 \bibliography{LiCO_Journal}
948 %\begin{IEEEbiographynophoto}{Jane Doe}
