Hole Detection and Healing in WSN

IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, TPDS-2012-06-0554 1

Localized Movement-Assisted Sensor Deployment Algorithm for Hole Detection and

Healing Mustapha Reda SENOUCI, Abdelhamid MELLOUK, Senior Member, IEEE and Khalid ASSNOUNE

Abstract— One of the fundamental services provided by a Wireless Sensor Network (WSN) is the monitoring of a specified Region of Interest (RoI). Considering the fact that emergence of holes in the RoI is unavoidable due to the inner nature of WSNs, random deployment, environmental factors, and external attacks, assuring that the RoI is completely and continuously covered is very important. This paper seeks to address the problem of hole detection and healing in mobile WSNs. We discuss the main drawbacks of existing solutions and we identify four key elements that are critical for ensuring effective coverage in mobile WSNs: (i) determining the boundary of the RoI, (ii) detecting coverage holes and estimating their characteristics, (iii) determining the best target locations to relocate mobile nodes to repair holes, and (iv) dispatching mobile nodes to the target locations while minimizing the moving and messaging cost. We propose a lightweight and comprehensive solution, called HEAL (Holes dEtection and heALing), that addresses all of the aforementioned aspects. The computation complexity of HEAL is O(v²), where v is the average number of 1-hop neighbors. HEAL is a distributed and localized algorithm that operates in two distinct phases. The first identifies the boundary nodes and discovers holes using a lightweight localized protocol over the Gabriel Graph of the network. The second treats the hole healing, with novel concept, hole healing area. We propose a distributed virtual forces-based local healing approach where only the nodes located at an appropriate distance from the hole will be involved in the healing process. Through extensive simulations we show that HEAL deals with holes of various forms and sizes, and provides a cost-effective and an accurate solution for hole detection and healing.

Index Terms— Hole detection; hole healing; coverage; mobile WSN; deployment; movement-assisted algorithms.

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1 INTRODUCTIONWireless Sensor Network (WSN) comprises small sensors with limited computational and communi-

cation power. By design, sensors are quite fragile and vulnerable to various forms of failure, such as sudden shock caused by their deployment (air dropping) or de-pletion of their limited energy resources. This intrinsic fragility must be regarded as a normal property of the network.

Several anomalies can occur in WSNs that impair their desired functionalities resulting in the formation of dif-ferent kinds of holes, namely: coverage holes, routing holes, jamming holes, and worm holes [1]. In this work we are interested in large bounded holes, i.e., large holes that are circumscribed by sensor nodes. In this case, cov-erage holes, i.e., areas not covered by any node, and communication holes, i.e., areas devoid of any nodes, become equivalent and will be referred to as holes in the rest of this paper.

One of the fundamental services provided by a WSN is the monitoring of a specified Region of Interest (RoI), where the main duty is sensing the environment and communicating the information to the sink. Assuring that the RoI is completely covered at all time is very important [2]. However, the emergence of holes in the RoI is una-voidable due to the inner nature of WSNs, random de-ployment, environmental factors, and external attacks. Thus, an event occurring within these holes is neither de-tected nor reported and, therefore, the main task of the network will not be completed. Thus, it is primordial to provide a self-organizing mechanism to detect and recover holes.

This paper seeks to address the problem of hole detec-tion and healing. Most of the existing solutions use global operations to calculate the size of a big hole and then relocate a group of mobile sensors to heal the hole. While some existing localized solutions requires strong assump-tions or even unrealistic ones. The incompleteness of previous works motivates our research presented here.

We propose a comprehensive solution, called HEAL (Holes dEtection and heALing) that has a very low com-plexity and avoid some drawbacks noticed in previous works. HEAL is a distributed and localized algorithm that operates in two distinct phases. The first phase consists of three sub-tasks; hole identification, hole discovery and border detection. Unlike prior efforts, we propose a dis-tributed and localized hole detection algorithm (DHD)

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———————————————— M.R. Senouci is with the A.I. Laboratory, Ecole Militaire Polytechnique, Algiers, Algeria; and also with the LiSSi Laboratory, University of Paris-Est Creteil (UPEC), France. E-mail: [email protected] A. Mellouk is with the LiSSi Laboratory, University of Paris-Est Creteil (UPEC), France. E-mail: [email protected] K. Assnoune is with the A.I. Laboratory, Ecole Militaire Polytechnique, Algiers, Algeria. E-mail: [email protected]). Please note that all acknowl-edgments should be placed athe end of the paper, before the bibliograp

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2 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, TPDS-2012-06-0554

that operates over the Gabriel Graph of the network. DHD has a very low complexity and deals with holes of various forms and sizes despite the nodes distribution and density.

The second phase treats the hole healing with novel concept, hole healing area. It consists of two sub-tasks; hole healing area determination and node relocation. We propose a distributed virtual forces-based local healing approach based on the Hole Healing Area (HHA), in which the forces will be effective. This allows a local heal-ing where only the nodes located at an appropriate dis-tance from the hole will be involved in the healing pro-cess.

The main contribution of this work is the design and evaluation of HEAL, a distributed, localized and compre-hensive two-phase protocol, that can effectively estimate and enhance the area coverage in a mobile WSNs. This paper makes the following specific contributions. First, a collaborative mechanism, called Distributed Hole Detec-tion (DHD), is proposed to identify the boundary nodes and discover holes. We have conducted extensive simula-tions to validate DHD. Second, we present a virtual forc-es-based hole healing algorithm. Unlike existing algo-rithms, our algorithm relocates only the adequate nodes within the shortest times with the lowest cost. Experi-mental results show that HEAL provides a cost-effective and an accurate solution for hole detection and healing in mobile WSNs.

The rest of the paper is organized as follows. Section II looks at the related work. Section III defines the problem statement while Section IV details our proposed mecha-nisms. Section V presents our experiments. Section VI concludes the paper and discusses some future directions.

2 RELATED WORK

2.1 Hole and Border Detection There has been much related research on the hole and border detection problem [3-13]. Authors in [3] studied the problem of detecting topological holes in WSN with no localization information. They presented a distributed scheme that is based on the communication topology graph. A node decides whether it is on the boundary of a hole by comparing its degree with the average degree of its 2-hop neighbors. Not all boundary nodes can be iden-tified correctly by this algorithm. Indeed, for a large WSN with a few holes this method is not efficient [3].

An algebraic topological method using homology the-ory detects single overlay coverage holes without coordi-nates [4], [5]. Ghrist et al. [4] employed a central control algorithm that requires connectivity information for all nodes in the RoI. For N nodes, the time complexity is

. For [5], it is , where D is the maximum number of other active nodes that overlap a node’s sens-ing area, and H is the worst-case number of redundant nodes in a large hole, with . In [5], the complexity does not depend on the overall size of the network, whereas the homology algorithm encounters severe diffi-culties with dense networks. Additionally, the message forwarding overhead can be impractically large, since the

algorithm is centralized. Funke in [6] presented a heuristic for detecting holes

based on the topology of the communication graph. The heuristic computation is not localized as it requires the computation of distance fields over the whole network. In a more recent paper [7], Funke et al. described a linear-time algorithm for hole detection. They require that the communication graph follows the unit disk graph model. Compared to the heuristic approach presented in [6], the algorithm does slightly worse. Furthermore, when de-creasing the node density, the algorithm breaks down more and more.

Fekete et al. [8] presented a coordinate-free method to identify boundaries in WSNs. They assume a uniform node distribution in non-hole areas. In a more recent paper [9], the same authors presented a deterministic approach for boundary recognition that does not rely on a uniform node distribution but requires a high node densi-ty. Wang et al. [10] described a distributed algorithm to find the boundary nodes by using only connectivity in-formation. They exploit a special structure of the shortest path tree to detect the existence of holes. The authors did not provide a complexity analysis but the proposed algo-rithm relies heavily on a repetitive network flooding.

Fang et al. [11] proposed the BoundHole algorithm us-ing the right-hand rule to identify nodes on the boundary of geometric holes. Kröller et al. [12] proposed an overall framework for self-organization based on topological considerations and geometric packing arguments to de-termine the boundary nodes and the topology of the whole network. Although, authors make few assump-tions, they deal with relatively complex combinatorial structures, such as flowers. Shirsat et al. [13] presented a hole boundary detection algorithm assuming the relative geographic information of 2-hop neighbors. The proposed algorithm takes a local best-effort approach and requires synchronization among nodes.

Table 1 summarizes the main drawbacks of hole and border detection algorithms found in the literature.

TABLE 1 COMPARISON OF PROPOSED SOLUTIONS TO THE HOLE AND

BORDER DETECTION PROBLEM

Proposed Solution

Main drawbacks

[3] - For a large WSN with a few holes, this method is not efficient

[4], [5], [6] - Centralized; - High complexity (e.g, for [4], the time complexity is , where N is the num-ber of nodes)

[7], [9] - Require a high node density [8] - Assumes a uniform nodes distribution

- Requires a high node density [10] - Based on a repetitive network flooding [11] - High message complexity [12] - Deals with relatively complex combinato-

rial structures, such as flowers [13] - Requires synchronization among nodes

The incompleteness of previous work motivates our


SENOUCI ET AL.: LOCALIZED MOVEMENT-ASSISTED SENSOR DEPLOYMENT ALGORITHM FOR HOLE DETECTION AND HEALING 3

research presented here. Our proposed hole and border detection algorithm is distributed and lightweight, and thus more suited to the energy constrained WSNs. It does not require flooding for gathering the topology infor-mation, as is the case in [10] or synchronization among nodes as in [13]. In addition, we do not assume a particu-lar nodes distribution as done in [8].

2.2 Coverage Enhancement and Hole Healing Mobile sensor nodes (for example, Robomote [14] and

iMouse [15]) are equipped with mobile platforms and can move around after initial deployment. In a mobile WSN, one of the objectives of the movement is to maximize area coverage. Several movement strategies for improving network coverage exist in the literature. Algorithms that belong to this category include [2], [7], [16-29], to name a few. In coverage pattern-based movement [16], [17], the target locations for mobile nodes are computed based on a predefined coverage pattern, while mobile nodes are likened as electromagnetic particles in virtual forces-based movement [16-19]. In grid-quorum based move-ment, the mobility-assisted network redeployment prob-lem is viewed as a load-balancing problem in traditional parallel processing systems [16], [17], [20]. The RoI is partitioned into many small grid cells, and the number of nodes in each cell is considered as the load of the cell [20]. In [21], authors consider the point coverage problem with novel evaluation metric, coverage radius.

Wang et al. [22] proposed three different deployment protocols that relocate mobile sensors once coverage holes are detected using Voronoi diagrams. In [23], the authors proposed a scheme called Co-Fi that relocates mobile nodes to replace low energy nodes. Authors in [24] de-veloped three hole-movement strategies for moving an existing big hole in a way that either the total energy consumption is minimized or the power consumption of sensors is balanced.

In a hybrid network consisting of both stationary and mobile sensor nodes, one objective for using mobile sen-sor nodes is to minimize coverage holes created by those stationary nodes [25-28]. Authors in [25] solve the cover-age problem by using a moving robot. They describe a tracking mechanism and a robot repairing algorithm. The tracking mechanism leaves the robot’s footmark on sen-sors so that they can learn better routes for sending re-pairing requests to the robot while the repairing algo-rithm constructs an efficient path that passes through all failure regions. Li et al. [29] propose a family of localized robot-assisted sensor relocation algorithms, by which mobile robots pickup passive sensors and deliver them to encountered sensing holes. Nadeem et al. [27] proposed MAPC, a mobility-assisted probabilistic coverage for enhancing and maintaining the area coverage by moving mobile sensor nodes to strategic positions in the uncov-ered area.

Table 2 summarizes the main drawbacks of proposed solutions to coverage enhancement and hole healing problem. The incompleteness of previous work motivates our research presented here. In this work we propose HEAL, a localized and distributed hole healing algorithm

as opposed to the centralized approach employed in [19]. The Co-Fi protocol [23] fails if the coverage loss is due to physical damage of the nodes. Our work addresses the problem of coverage loss due to both energy depletion and physical damage of the nodes. In addition, we do not assume that the WSN boundary is known a priori as done in [29]. In HEAL the hole detection and healing process can be triggered by any node in the network or even by several nodes as opposed to MAPC [27], where the sink is used for starting the hole detection and healing process.

HEAL is based on a localized healing process that: (i) minimizes the WSNs’ resources consumption; (ii) acceler-ates the healing process; and (iii) preserves as much as possible the initial WSN topology. The number of nodes solicited in the healing process depends on the holes’ characteristics. In addition, only the nodes located at an appropriate distance from the hole will be involved in the healing process.

3 PROBLEM STATEMENTThe basic scenario can be described as follows: during normal operation of the network, a great loss of nodes occurs, due to an external attack for example, causing the creation of one or several large holes within the network making it ineffective. Our problem is to design a mecha-nism for detecting and recovering holes by exploiting only the nodes mobility. It should be noted that only the holes within the network are considered. The holes on the border that are the result of the initial deployment are not addressed.

Most hole healing algorithms require coordinate in-formation about the sensor nodes in the RoI and use computational geometry with tools, such as Voronoi dia-

TABLE 2COMPARISON OF PROPOSED SOLUTIONS TO THE COVERAGE

ENHANCEMENT AND HOLE HEALING PROBLEM

Proposed Solution

Main drawbacks

[18] - Centralized approach [19], [26] [28]

- Requirement for global computation (i.e., all the nodes need to run the algorithm)

[20] - It may generate huge message overhead when the network is very dense due to the increased number of rounds of scans - At the end of the clustering process, if two adjacent clusters are empty the scan process will not be correct

[21] - Considers only the point coverage problem [22] - Requirement for global computation

- For large holes this method is not efficient [23] - Do not addresses the problem of coverage

loss due to physical damage of the nodes [24] - Requires synchronization among nodes [25] - Assumes that the WSN has been deployed

using robot deployment mechanisms [27] - Only the sink can triggers the hole detec-

tion and healing process [29] - Assumes that the WSN boundary is known

a priori



grams to detect holes [2], [16], [17], [22], [26], [28]. With these algorithms, each node, independently of other nodes in the RoI, tries to detect and repair holes in its Voronoi polygon.

Fig. 1 illustrates the principle of hole detection and healing of a well-known approach proposed by Wang et al. [22]. A node needs only to check its own Voronoi pol-ygon. If it detects the existence of a hole, it will move toward its farthest Voronoi vertex and stops when this

vertex can be covered. This way of doing is not suitable for large holes be-

cause each node, in the hole’s boundary, moves without taking into account the movement of other nodes, which, generally, will not help to recover the hole. In the exam-ple of Fig. 1, the nodes S1 and S6 move in the direction that covers their holes, which does not contribute in the healing of the hole bounded by the six vertices.

Before discussing our proposal, we make the following assumptions:

1. A dense mobile WSN is deployed in an obstacle-free RoI;

2. The deployment can be either deterministic or random;

3. All deployed nodes are homogeneous; 4. Location information is available to each sensor

node by using some GPS-less sensor network lo-calization scheme [30];

5. We consider the isotropic sensing model [2]. We denote by Rs, the sensing range and Rc, the com-munication range. We assume that to en-sure that the TENT rule [11] is applicable.

4 PROPOSED MECHANISMSIn our algorithm we propose collaborative mechanisms to detect and heal holes. Our hole detection mechanism deals with holes of various forms and sizes. We try to alert a limited number of nodes surrounding the hole, only those nodes have the task of moving and repairing the hole.

As suggested by Wang [2], when designing a hole healing algorithm, the following issues need to be ad-

dressed: 1) How to detect a hole and how to estimate its size? 2) Where are the best target locations to relocate mobile nodes to repair coverage holes? 3) How to dis-patch mobile nodes to the target locations while minimiz-ing the moving and messaging cost? In this section, we address all of the aforementioned issues.

Our Distributed Hole Detection (DHD) algorithm al-lows us to discover holes, to compute their characteristics and to discover the network boundary. In a second phase, HEAL performs a local healing where only the nodes located at an appropriate distance from the hole will be involved in the healing process. We define an attractive force that acts from the hole center and attracts the nodes towards the hole center. At the same time, a repulsive force is defined among nodes to minimize the overlap-ping among them. These forces will be effective in a lim-ited area, which we call the Hole Healing Area (HHA). The proposed algorithms consist of hole detection and hole healing steps. We first discuss how to detect and heal a single hole and then we show how to deal with several holes.

4.1 Hole Detection Fang et al. [11] defined the stuck nodes where packets can possibly get stuck in greedy multi-hop forwarding. A node is a stuck node if there exists a location out-side ’s transmission range in , so that none of the 1-hop neighbors of is closer to than itself. Fang et al. [11] developed a local rule, the TENT rule, for each node in the network to test if it is a stuck node. The TENT rule specifies that a node is not a stuck node when there is no angle spanned by a pair of its angularly adjacent neigh-bors greater than 2π/3. We adapt this technique to con-ceive our distributed hole detection algorithm and inte-grated it in HEAL. To identify holes in the network, we proceed in three steps.

4.1.1 Hole Identification First, we have to assess the existence of a hole, which is done by identifying stuck nodes. Each node in the net-work executes the TENT rule to check if it is a stuck node as follow. First, it orders all its 1-hop neighbors counter-clockwise. Let and be a pair of angularly adjacent

Fig. 1. Ineffectiveness of VOR in treatment of large holes.

Fig. 2. is a strongly stuck node [11].



nodes. Second, it draws the perpendicular bisector of and , , . and intersect at point and divide the plane into 4 quadrants (Fig. 2).

Only the points in the quadrant containing are closer to than and . Finally, if is outside the communica-tion range of , the angle is a stuck angle and con-siders itself stuck node.

4.1.2 Hole Discovery All the nodes that are marked as stuck nodes by the TENT rule trigger the discovery of holes. The aim of this step is to find the boundary of the hole and the computa-tion of the holes’ characteristics (center and radius).

A stuck node bi creates a new Hole-Discovery (HD) packet, marked with its ID (the hole will take the same ID as that of the node bi), whose mission is to collect location information of hole boundary nodes, and forwards it to the next boundary node bi+1 by right-hand rule over the Gabriel Graph (GG) of the network. Node bi+1 inserts its location information into the received HD packet and forwards it to the node bi+2 by right-hand rule over the GG and so on. This process is repeated until the HD packet has traveled around the hole and eventually been re-ceived by the initiator node bi.

Node bi extracts from the received HD packet the loca-tions of the boundary nodes . It selects two nodes bn and bm so that the distance between them is the longest between any two nodes in the set of boundary nodes.

Then, it calculates the hole center, which is the mid-point v of segment .

We note that each stuck node sends a HD packet with-out any coordination among stuck nodes. Thus, there will be redundancy in the discovery process. This will gener-ate unnecessary traffic and more packet collisions; the situation may become worse especially for large holes. To avoid this problem, we use a mechanism to prevent re-dundancy in the discovery process. The basic idea is to remove redundant HD packets as soon as possible. The criterion for judging whether a HD packet is redundant is as follows: at each node, if a HD packet arrives and dis-covers that the packet has a Hole-ID greater than a Hole-ID carried by a packet already passed, the packet will be considered redundant and it will be deleted.

At the end of this step the node that has the smallest Hole-ID removes the HD packet and names itself as ‘Hole Manager (HM)’: it will be responsible for the hole-healing announcement. In another hole manager selection strate-gy, one can pick the node with the largest residual energy among all the boundary nodes.

4.1.3 Border Detection The network boundary nodes (nodes on the limits of the RoI) execute the TENT rule and, as a result, they detect that they are stuck nodes, which will launch the hole

discovery and the healing processes even if these nodes are actually not stuck nodes (they are the borders of the network). That is why we need to carry out the network boundary-node identification to avoid that the hole dis-covery process be launched by those nodes.

To discover the network boundary in a distributed way, we follow the following steps:

1. Each node of the network executes the TENT rule; 2. Each stuck node launches DHD to identify the

nodes that surround the hole; 3. In the HD packet, we define four Boolean varia-

bles to identify the network boundary Xmax, Ymax, Xmin, Ymin. Each stuck node, which receives a HD packet, compares the coordinates Xmax, Ymax, Xmin, Ymin defined in the packet with its coordinates, and if it finds that it has a higher or a lower value than one of these values compared to all its neighbors, it sets the corresponding Boolean variable to 1;

4. At the end of this procedure, the largest hole that defines the network boundary will be defined by the coordinates Xmax, Ymax, Xmin, Ymin;

5. We cancel the healing process that will be launched by the HM node.

The above hole detection steps can be summarized as the pseudo-code depicted in Appendix A1.

4.2 Hole Healing At the end of the hole detection phase the healing

phase is executed. We exploit here nodes locomotion facilities to heal detected holes. Our relocation algorithm is completely distributed and it is based on the concept of virtual forces. To heal the discovered hole we define an attractive force that acts from the hole center and attracts the nodes towards this center. Similarly, a repulsive force is defined among nodes to minimize the overlapping in between. We define the Hole Healing Area (HHA) in which the forces will be effective. This allows a local heal-ing where only the nodes located at an appropriate dis-tance from the hole will be involved in the healing pro-cess.

In this phase, the HM node plays the role of determin-ing the HHA and informing nodes on their movement. The HM node has information about the size of the hole and boundary nodes. Details of the healing process are presented in the following paragraphs.

4.2.1 HHA Determination The HHA constitutes the basic idea of our algorithm; the determination of this area will determine the number of nodes that must be relocated to ensure a local repair of the hole. After the identification of the hole by the DHD algorithm, the HM node calculates the center and the size of the hole. As mentioned before, we approximate the hole by a circle whose radius is the longest distance be-tween two hole boundary nodes. Therefore, to determine the HHA, we need to determine the radius of the circle that defines the HHA.

To find the appropriate radius, we have used an itera-tive approach based on the following formula:

1 All appendices can be found in the supplemental file of this paper.



where is the hole radius. is a positive constant,

which depends on the nodes density and the sensing range Rs.

For , we start with a radius equals to the estimat-ed hole radius due to the fact that this area may contain a sufficient nodes to heal the hole. The area defined by this circle (denoted HHA-0) is equal to . The number of nodes necessary to cover the area HHA-0 is equal to:

The HM node calculates the number of nodes present in the HHA-0. It solicits its direct neighbors to calculate the number of their neighbors in this area. It sends a Hole Healing Area Determination packet containing infor-mation about the hole. This communication is done over the GG to reduce the number of exchanged messages. If the number of nodes found by the HM node is less than the required number to heal the hole, the movement of these nodes will create new holes. To avoid this scenario the HM node starts a new round by increasing , then it repeats this process until it finds a sufficient number of nodes to recover the hole. The above HHA determination step can be summarized as the pseudo-code depicted in Appendix A. Under the assumption of uniform deploy-ment, in Appendix B, we devise a relationship between the required number of nodes for a full hole healing and the radius of the HHA.

After determining the HHA, the HM node sends to the concerned nodes a movement packet containing infor-mation about the hole to heal. The nodes that receive this packet will enter in the relocation phase presented in the next section.

4.2.2 Node Relocation After determining the HHA, the HM informs the nodes involved in the healing process. Nodes that receive forces from the hole center, move towards it. We try to balance the tradeoffs between attractive and repulsive forces to recover the hole without side effects (Fig. 3). We describe in the next sections the concept of virtual forces used within our HEAL algorithm.

A. Attractive Force The hole center applies an attractive force on every node within the HHA and located at a distance greater than from . A node p within the HHA receives an attractive force form given by:

where is the unit vector oriented from the point p to the hole center v, is the Euclidean distance be-tween the node p and the hole center located at . is a distance coefficient, is a coefficient that defines the intensity of the attractive force and is the hole radius.

The exponential factor enables us to control the movement of nodes in the HHA, so that the nodes closest to the hole center will move longer distance than those on

the boundary to prevent the creation of new holes during the healing process. The formulation of the exponential factor is a heuristic that we have proposed and that is based on the logic of the nodes’ movement, where the distance is inversely proportional to the force.

B. Repulsive Force To minimize overlapped coverage, a repulsive force exists among nodes within the range where denotes repulsive force. If the distance between two nodes p and q is less than then will separate those nodes to minimize the overlapped coverage. increases as the distance decreases and is given by:

where is the unit vector oriented from node q to p, is a factor that defines the intensity of the repulsive

force and is a distance coefficient such as .

C. Movement Equation The final position of a node p is dictated by the resultant force of the composition of all repulsive forces that sus-tained the node from its Neighbors Set (denoted by NS) and the attractive force applied by the hole center. This force is

Note that the magnitude and orientation of can be easily calculated, e.g., Robomote [14] uses an on-board compass combined with localization information for nav-igation purposes. If then the node p remains in its original position. Otherwise, p moves one time step in the direction imposed by . The final position of p is given by:

where is the node velocity and is its position at instant t.

Fig. 3. Principle of the healing process.



The above relocation phase can be summarized as the pseudo-code depicted in Appendix A. It should be noted that orientation and distance measurement errors could affect the hole-detection/hole-healing process and there-fore the healing is not complete. A simple approach to overcome this problem consists in checking the existence of a hole upon the termination of the healing process. If a hole is detected, the hole-detection/hole-healing process may be triggered a second time (we have to trade-off between healing accuracy and energy consumption).

4.3 Treatment of Holes The case of a single hole in the network is the simplest

case. In many applications where several holes appear in the network at the same time, the proposed protocol must be readapted so it becomes able to heal the discovered holes as much as possible. This re-adaptation is as fol-lows.

4.3.1 Hole Detection Each stuck node is associated with only one hole in each direction it is stuck in. As the TENT rule detects all the directions where the nodes are stuck [11], the DHD algo-rithm will still work with the presence of many holes. However, a stuck node bi can be on the boundaries of multiple holes. In this case, bi initiates a HD packet in each direction it is stuck in (at most three). The second or the third HD packet could be considered as redundant and thus removed. To avoid this behavior, redundant HD packets detection is based on the direction of the HD packet in addition to the Hole-ID. At each hop, the direc-tion of a HD packet is defined by the previous node that forwards this packet. This allows the detection of multi-ple adjacent holes.

4.3.2 Node Relocation For multiple holes, we use the same healing process to heal the discovered holes. However, several HHA may overlap; therefore, a node in the overlapping area will be solicited by more than one HM. In this case, we must answer the following question: how a node solicited by several HM should move?

We adopted two strategies: 1. Strategy 1: distance-based. The purpose of this strat-

egy is to minimize the distance traveled by nodes in the healing process to preserve nodes’ energy. A node considers only the first movement packet that it receives.

2. Strategy 2: hole-based. In this strategy, when a node receives a movement packet, it waits for a certain period of time. After that it compares the received movement packets and moves towards the biggest hole. The purpose of this strategy is to prioritize the healing of the large holes based on the as-sumption that a large hole constitutes a much seri-ous threat in comparison to a small one. In addi-tion, unlike large holes, small holes can be covered by running a simple algorithm likes DSSA [18].

A complete HEAL complexity analysis is reported in Appendix C.

5 EXPERIMENTSFor validation, we have implemented HEAL in the ns2 simulator [31]. We have carried out a two-stage simula-tion. The first scenario points out the effectiveness of HEAL in terms of hole detection and healing. This is done by varying the size, the shape and the number of holes created within the RoI. We also compare our approach with that of Wang et al. [10], in terms of bounded holes discovery, while varying the node density. In the second scenario, we compare HEAL to DSSA [18] and SMART [20] using a random deployment strategy. That is to say that the protocols will operate directly on the holes result-ing from the initial deployment.

5.1 Scenario 1: Validation of HEAL This scenario contains two sub-scenarios. In the first one, we vary the radius of the hole created within the RoI. We use a deterministic deployment strategy with holes of different radii. The purpose of this sub-scenario is the evaluation of HEAL in terms of hole detection and heal-ing. In the second sub-scenario, we analyze the behavior of HEAL in the presence of several holes in the network. We vary the number and the size of holes and we analyze the rate of detection and healing provided by HEAL. Tests will be conducted on deterministic and random scenes. Parameters of the first scenario are described in Table 3.

5.1.1 Sub-scenario A: Varying the size of hole In this sub-scenario we vary the size of the hole. The re-sult of the healing process depends on the forces parame-ters and . These parameters are not fixed but depend on the size of the hole. We proposed adaptive values for

and depending on the size of the hole. So after re-ceiving the movement packet, each node decides, inde-pendently, on and based on the estimated radius of the hole. The simulation results are presented by display-ing the network state (sensing disks) before and after the execution of HEAL (Fig. 4).

By varying the hole radius form 20m to 60m we obtain almost the same results. The repair rate is thereabouts 100%, nodes were able to detect and heal the hole. The hole with a radius of 38m (resp. 48m, 58m) requires the movement of 50 nodes (resp. 62, 82). We notice that the nodes closest to the hole have moved longer distance than those on the border of the HHA (Fig. 4).

We have analyzed the total distance traveled by nodes according to the radius of the hole. As depicted on Fig. 5,

TABLE 3SCENARIO 1 PARAMETERS

Sub-scenario A Sub-scenario B Max (X,Y) (m,m) (200,200) (200,200)

Deployment deterministic deterministic / random

Rs (m) , Rc (m) 12 , 24 12 , 24 Simulation time (s) 500 500 Max speed (m/s) 10 10 Number of nodes 169 169, 400 Routing protocol AODV AODV



this distance is proportional to the hole radius. This dis-tance increases from 66m for a radius of 27m to 840m for a radius of 58m. This is due to the density of nodes required to heal the hole and the distance between the nodes and the hole center. The distance traveled is not uniformly distributed on the nodes. Since according to our reloca-tion model, the nodes near the hole will travel a longer distance compared to those far from the hole, and thus ensure the connectivity of the network after the move-ment.

Fig. 6 shows the number of movements performed by nodes to heal the hole. This number reflects the number of nodes within the HHA. It is proportional to the size of the hole, which is logical because when the hole is large its recovery needs more nodes. For a large hole with a radius of 58m, 82 nodes have moved among 169 nodes; thus 87 nodes remained fixed, which represents half of the nodes. These results confirm that the principle of local healing that we proposed is functional.

5.1.2 Sub-scenario B: Varying the number and size of hole

In this sub-scenario we vary the number and the size of holes created in the RoI, and we analyze the behavior of HEAL. Using deterministic deployment, we created two holes with different shapes (44m and 32m). Detection as well as the healing of the holes were made successfully with a very high percentage of healing. We have checked that the presence of both large and small holes did not affect the correctness of HEAL. We have checked also that HEAL is still effective for small holes.

Some results for stochastic deployment are depicted in Fig. 7-8. As we can see, HEAL is still effective and the holes were detected and repaired successfully. In Fig. 7 the healing of the two holes required the movement of only 86 nodes among 400 nodes. We varied the number of holes and their sizes. With two, four and even seven holes (Fig. 8), detection as well as healing are always functional and the healing is almost complete. More extensive eval-uation results can be found in Appendix D.

5.2 Scenario 2: Comparing HEAL to SMART and DSSA

In this scenario, we vary the network density and we compare the performances of HEAL to those of DSSA [18] and SMART [20]. On one hand, DSSA [18] is a centralized movement-assisted virtual forces-based algorithm, this allow us to compare HEAL to a competitive approach based on the same concept, i.e., virtual forces. On the other hand, SMART [20] is a grid-quorum based move-ment-assisted localized algorithm, this allow us to com-pare HEAL to a competitive approach based on a differ-ent principle. The performances of the three algorithms

Fig. 5. Total traveled distance vs. hole radius.

Fig. 6. Number of movements vs. hole radius.

(a) (b)Fig. 7. Result for two holes. (a) original network, (b) repaired network

(a) (b)

Fig. 4. Result for radius of 58m. (a) original network, (b) re-paired network.

(a) (b)Fig. 8. Result for seven holes. (a) original network, (b) repaired network



are evaluated in terms of traveled distance, number of movements and rate of improvement in network cover-age. The three algorithms will operate directly on the holes resulting from the initial random deployment. The presented results are the average of 20 runs. Parameters of the second scenario are shown in Table 4. We note that SMART and DSSA are movement-assisted deployment protocols that try to improve the network coverage by exploiting nodes mobility, which is not the case of our protocol. The purpose of this comparison is to see the performance of HEAL when it is used to improve the network coverage of the initial deployment.

Fig. 9 shows the improvement in coverage realized by HEAL, DSSA and SMART. For a deployment of 200 nodes, we see that HEAL gives the worst results com-pared to others; this is explained by the fact that the ran-dom deployment of 200 nodes in the RoI of 200m×200m may produce important holes within the RoI and on its borders. HEAL will detect holes and repair some of them since the nodes density is not enough for a complete heal-ing. Moreover, if the node density increases, the perfor-mance of HEAL increases; the mechanism of healing be-comes more effective with a large number of nodes. The performances of HEAL exceed those of DSSA and SMART when the number of nodes reaches 350.

In Fig. 10, the total distance traveled by the nodes is shown as a function of nodes density. In the interval [200, 300] the distance traveled by the nodes in HEAL increases with the nodes density, this is due to the significant num-ber of holes which HEAL detects and tries to repair. Fur-thermore, the number of holes resulting from the initial deployment decreases with the increase of the nodes density, consequently the traveled distance is stabilized before decreasing. Compared with SMART, the total dis-

tance traversed in HEAL is much lower whereas the rate of coverage improvement is equal or better than that presented by SMART. We note that SAMRT is supposed to be more effective with dense topologies [20]. The per-formances of DSSA decrease for dense topologies. The traveled distance decreases with the increase in the num-ber of nodes because the inter-nodal force is increasingly weak, thus the displacement of the nodes is small and the rate of coverage improvement is weak.

We note that the distance traveled by the nodes in HEAL and DSSA is much lower than that of SMART. This is due to the fact that for SMART, as the nodes density increases there will be more nodes regrouping in some parts of the network; so to balance the number of nodes in each cluster, SMART requires the movement of more nodes and thus the traveled distance increases.

In Fig. 11, we see clearly that SMART and HEAL have almost the same performances in terms of number of movements for a number of nodes between 200 and 300 nodes; but HEAL outperforms SMART after that. The number of relocated nodes by the two algorithms in this interval is between 40 and 160 nodes for an improvement in coverage of 7%. DSSA, in this interval, gives a greater coverage improvement but with a very excessive number of movements (1200-1600). This is due to the fact that the movements of the nodes in HEAL and SMART is a selec-tive movement at only one time step (few nodes only are concerned by the movement from a source towards a destination).

Fig. 9. Comparison in terms of coverage improvement.

TABLE 4SCENARIO 2 PARAMETERS

Max (X,Y) (m,m) (200,200) Deployment Uniform Rs (m) , Rc (m) 10 , 20

Simulation time (s) 500 Max speed (m/s) 10 Number of nodes 200-450 Routing protocol AODV

Fig. 11. Comparison in terms of number of movements.

Fig. 10. Comparison in terms of total distance traveled by nodes.



For DSSA, all the nodes are concerned by the move-ment according to the principle of virtual forces, a node may move several times before the algorithm converges. In the interval [350, 450] nodes, HEAL gives the best re-sults in comparison to those of DSSA and SMART.

Finally, the obtained results allow us to assert that alt-hough HEAL is a hole healing protocol but it is still effec-tive for the improvement of network coverage by healing holes resulting from the initial random deployment.

6 CONCLUSIONThis paper has proposed and implemented a lightweight and comprehensive two-phase protocol, HEAL, for ensur-ing area coverage employing a mobile WSN. The protocol uses a distributed hole detection algorithm (DHD) to detect holes in the network. The computation complexity of DHD is O(v²), where v is the average number of 1-hop neighbors. Compared to the existing schemes, DHD has a very low complexity and deals with holes of various forms and sizes despite the nodes distribution and densi-ty. By exploiting the virtual forces concept, our approach relocates only the adequate nodes within the shortest time and at the lowest cost.

Through the performance evaluation, we validated HEAL, using different criteria and showed that it detects and heals the holes despite their number or size with less mobility in various situations. The evaluation results demonstrate that HEAL provides a cost-effective and an accurate solution for hole detection and healing in mobile WSNs.

In the future, we plan to investigate the interaction be-tween HEAL and the network layer for on-demand hole detection and healing. We are currently working on open holes located at the network boundary. We also plan to investigate a special case of holes with obstacles.

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Mustapha Reda SENOUCI is currently working toward the Ph.D. degree in co-supervision between UPEC (University of Paris-Est, France) and USTHB (University of Science and Technology Houari Boumediene, Algeria). He received the Master’s degree in Mobile Com-puter Science from USTHB in 2009

(with distinction) and the Engineer degree in Computer Science from the Polytechnic School of Algiers (EMP) in 2005 (with distinction), where he is currently a teacher-researcher. His research interests include mobile ad-hoc networks, wireless sensor networks and wireless sen-sor/actor networks.

Abdelhamid MELLOUK (Senior Member IEEE). Full Professor at UPEC (University of Paris-Est), Net-works and Telecommunication (N&T) DepT IUT C/V, France, He graduated in Computer Network Engineering from the University of Paris Sud XI Orsay (with distinction),

received his Ph.D. in informatics Engineering from the same university and the Doctorat of Sciences (Habilita-tion) Diploma from the Paris-Est University (UPEC).

Founder and Leader of Network Control research ac-tivity with extensive international academic and industri-al collaborations, his general area of research is in high-speed new generation wired/wireless networking and quality of service/experience for added value services. Currently, he is working on routing and switching opti-mization in dynamic traffic networks; human and bio-inspired artificial intelligence approaches; wireless sensor networks; multimedia and high-speed communications. He investigates particularly the use of artificial neuronal intelligence together with biologically inspired techniques such as reinforcement learning, to control network behav-ior in real-time so as to provide users with the quality of service that they request, and to improve network pro-vide robustness and resilience.

Abdelhamid is the Chair of IEEE TC Communications Software and Senior Member of IEEE. He has pub-lished/coordinated many books and several refereed international publications in journals, conferences and books, in addition to numerous keynotes and plenary talks in flagship venues. He is serving as Editorial Board or Associate Editor for many top journals and he is/has chaired (or co-chaired) some of top international confer-ences and symposiums.

Khalid Assnoun is a Ph.D. candidate in Computer Science at the USTHB Uni-versity in Algiers, Algeria. He received his Diploma-Engineer in Computer Sci-ence from the Polytechnic School of Al-giers (EMP) in 2010. His research inter-ests include wireless sensor networks.


Hole Detection and Healing in WSN

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