Superior Path Planning Mechanism for Mobile Beacon ... - IEEE Xplore

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IEEE SENSORS JOURNAL, VOL. 14, NO. 9, SEPTEMBER 2014

Superior Path Planning Mechanism for Mobile Beacon-Assisted Localization in Wireless Sensor Networks Javad Rezazadeh, Student Member, IEEE, Marjan Moradi, Member, IEEE, Abdul Samad Ismail, Member, IEEE, and Eryk Dutkiewicz, Member, IEEE

Abstract— In many wireless sensor network applications, such as warning systems or healthcare services, it is necessary to update the captured data with location information. A promising solution for statically deployed sensors is to benefit from mobile beacon-assisted localization. The main challenge is to design and develop an optimum path planning mechanism for a mobile beacon to decrease the required time for determining location, increase the accuracy of the estimated position, and increase the coverage. In this paper, we propose a novel superior path planning mechanism called Z-curve. Our proposed trajectory can successfully localize all deployed sensors with high precision and the shortest required time for localization. We also introduce critical metrics, including the ineffective position rate for further evaluation of mobile beacon trajectories. In addition, we consider an accurate and reliable channel model, which helps to provide more realistic evaluation. Z-curve is compared with five existing path planning schemes based on three different localization techniques such as weighted centroid localization and trilateration with time priority and accuracy priority. Furthermore, the performance of the Z-curve is evaluated at the presence of obstacles and Z-curve obstacle-handling trajectory is proposed to mitigate the obstacle problem on localization. Simulation results show the advantages of our proposed path planning scheme over the existing schemes. Index Terms— Localization, mobile beacon, path planning, wireless sensor networks.

I. I NTRODUCTION

A

WIRELESS Sensor Network (WSN) is formed where many sensors communicate wirelessly and monitor a physical region, cooperatively. Application scenarios of WSNs cover a wide spectrum of military, health, environment monitoring, household, and other commercial areas [1]. Some of the military applications of WSNs are enemy reconnaissance and attack detection, and battle damage assessment. Supporting the

Manuscript received March 16, 2014; accepted April 24, 2014. Date of publication May 9, 2014; date of current version July 22, 2014. The associate editor coordinating the review of this paper and approving it for publication was Dr. M. R. Yuce. J. Rezazadeh, M. Moradi, and A. S. Ismail are with the Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia, Johor 81300, Malaysia (e-mail: [email protected]; [email protected]; [email protected]). E. Dutkiewicz is with the Wireless Communication and Networking Laboratory, Macquarie University, Sydney, NSW 2109, Australia (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/JSEN.2014.2322958

elderly is a typical crucial application of sensor networks in the health area. Forest fire detection, monitoring disaster area, and target or animal tracking are few examples of environmental monitoring applications of WSNs. In these applications, just to mention a few, the reported event is meaningful and can be responded to only if its position is known. The process of determining physical coordinates of a sensor node or the spatial relationships among objects is known as localization [2]. Different services provided by WSNs such as coverage, security and routing [3] are matured enough. However, localization, as a critical service still requires further emphasis. Global Positioning System (GPS) is a commonly used and precise method for sensor localization. Unfortunately, the GPS solution is neither cost-effective nor energy-efficient [4], [5]. Additionally, the deployment-ability of sensor nodes which are equipped with GPS may be reduced due to the increased size. Finally, these GPS equipped sensors have limited applicability because GPS works only in an open field [6]. Localization algorithms can cope with the problem where they are able to estimate the location of sensors by using the knowledge of the absolute positions of a few sensors. Generally, these small proportions of sensor nodes with known location information (either equipped with GPS or installing at a fixed position) are called beacons. Ordinary sensors which urgently need to be localized are called unknown nodes. On the other hand, WSNs are usually applied for missions where human operation is impossible. So, installing beacon nodes in a predetermined location is often infeasible. This means that, beacon nodes equipped with GPS receivers must be employed for localization. Another observation is that the precision of the localization increases with the number of beacons [7], [8], but they increase the energy consumption and the cost of the WSN. Considering all the aforementioned problems of GPS receiver equipment, we are motivated to investigate how a single mobile beacon can be employed as an alternative solution to localize the entire network. Comparatively, localization through the use of a mobile beacon is inherently more accurate and cost-effective than localization using static beacons [9]–[11]. The mobile beacon travels around the region of interest where unknown sensor nodes are deployed and transmits the beacon signal that includes its location information [12]. Taking advantages of such a mobile beacon in location estimation is of importance. Since mobile

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REZAZADEH et al.: SUPERIOR PATH PLANNING MECHANISM FOR MOBILE BEACON-ASSISTED LOCALIZATION IN WSNS

beacon-assisted localization algorithms offer significant practical benefits, a fundamental issue is finding an optimum path for mobile beacon trajectory to take advantage of such an architecture. Hereafter, the mobile beacon assisted localization problem is limited to finding an optimum beacon trajectory. To mitigate this issue, various properties of an optimum path planning of the mobile beacon node should be investigated. A carefully selected deterministic trajectory can guarantee that all the unknown sensors receive beacon messages and obtain estimation for their positions, as the basic condition. On the other hand, traveling along a poor trajectory may cause certain unknown sensors not to be localized due to being far away from the trajectory. Authors in [9] addressed fundamental properties that the trajectory should have. The key property is full coverage by three non-collinear beacon messages (messages transmitted by the mobile beacon where at least one of them is not on a straight line) through the shortest path length and pass closely to as many potential unknown sensors (for improving accuracy). Several mobile beacon movement paths for mobile beacon assisted localization have been surveyed in [13]. Most of them are only focused on localization accuracy or path length of the trajectory. Here, we propose a novel mobile beacon path planning mechanism for localization named Z-curve which not only satisfies the mentioned fundamental properties but also considers other critical features. In summary, the contributions of this paper are as follows: First, we propose a novel superior path planning mechanism for mobile beacon assisted localization which can guarantee full coverage of sensors with highly precise estimation of location where it provides three consecutive non-collinear messages through the shortest possible path. The proposed trajectory can achieve a good trade-off between accuracy and time required for localization with minimizing futile beacon positions. Second, we investigate a reliable and accurate wireless channel model to provide realistic results and evaluate the performance of our proposed path planning mechanism in five highlighted trajectories. Third, we define and study new evaluation metrics such as the ineffective position rate for analyzing the efficiency of mobile beacon trajectories. Moreover, the obstacles are considered in the network field and the Z-curve obstacle handling trajectory is investigated for a real environment. The rest of the paper is organized as follows: Section II summarizes the state-of-the-art in mobile beacon trajectories and the existing localization methods for mobile beacon and static sensors. The Z-curve method is presented in Section III. Our simulation results are reported in Section IV followed by the conclusions in Section V. II. R ELATED W ORK There has been a large body of research on localization for wireless sensor networks over the last decade. Most existing localization schemes for WSNs are classified based on a key classification into two main groups: range-based or rangefree. Range-free techniques only use connectivity information between sensors and beacons. In [7], [14], and [15]

Fig. 1.

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Localization classification (based on mobility feature).

some range-free methods have been presented. Range-based techniques use distance or angle estimates for localization, such as methods proposed in [16]–[18]. Although it is a comprehensive categorization of localization algorithms, it is not distinct enough for further research in the presence of mobile beacon nodes and mobile sensor nodes. In a wide range of applications, a fully static network is not realistic [19]. One important factor is to let localization algorithms benefit from node mobility. To capture this possibility, we reclassify localization methods with respect to the mobility state of beacons and sensor nodes, as shown in Figure 1. As it is seen in Figure 1, localization methods can be represented into four groups: (1) static beacons and static nodes such as the methods proposed in [2] and [13], and [20], (2) static beacons and mobile nodes such as the schemes in [7] and [21], (3) mobile beacons and static nodes proposed in [9], [10], and [22], and (4) mobile beacons and mobile nodes like the methods in [23] and [24]. This paper focuses on the category of mobile beacons with static sensor nodes, because this kind of localization promises a wide spectrum of application scenarios. An example can be a military application or a monitoring task like fire detection, where sensor nodes are dropped from a plane on land, and transmitters are attached to soldiers or animals acting as mobile beacons. Localization studies with mobile beacons generally focus on two major problems, either proposing an efficient localization algorithm or developing an optimum mobile beacon movement strategy. In the rest of this section, we briefly survey representative methods for both issues. A. Mobile Beacon, Static Nodes Localization Algorithms A key paper presented in [9], has localized static nodes based on the RSSI of a mobile beacon and Bayesian inference. The paper employed statistical principles for processing the received information from mobile beacon, instead of imposing geometrical constraints. The major drawback of the scheme is its relatively high computation complexity which increases energy consumption. Ssu et al. [10] proposed a prior method for localization of static sensor nodes with four mobile beacon points. Obstacles in the sensing field are tolerated, although it causes radio irregularity. The major drawback of the mechanism is its long execution time and high beacon overhead. In order to further improve localization accuracy in Ssu’s scheme, Lee et al, also proposed another geometric constraint-based localization method in [11]. Only one mobile beacon moves around the network field. The main drawback of this scheme is increasing location error with enlarging the communication range. Another mobile-beacon assisted localization method has been proposed in [25] which utilizes

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the geometric relationship of the perpendicular intersection to compute node positions. The design was extended by a new mobile beacon to handle obstacle-resistance problem in the network field. The extended design suffers from the extra cost while it is made up of a rotating arm and wheels. Ou in [26] presented an approach for locating static sensor nodes by means of mobile beacon nodes equipped with four directional antennas. Obstacles were taken into account in the proposed range-free localization method. The method is efficient where the sensor nodes have no specific hardware requirements. B. Mobile Beacon Trajectories The main concern for developing an optimum trajectory for mobile beacon assisted localization is how to find the optimal path for the mobile beacon. Some fundamental properties of an optimum beacon path have been introduced in [4] and [9]. Here, a brief review is presented on the existing mobile beacon trajectories for localization. Scan, Double Scan, and Hilbert space filling curve are three well-known trajectories proposed in [4]. All the path types can successfully achieve higher precision location estimation than Random Way Point (RWP) [9], [27]. However, their accuracy directly depends on the resolution of the trajectory (the distance between two successive beacon positions). All the above path types can cover the network field, but SCAN suffers from colinearity (beacon messages as transmitted by the mobile beacon node when it moves on a straight line). To solve the above problem, Double SCAN traverses the field along both directions at the expense of doubling the distance. A Hilbert space filling curve is proposed to reduce the colinearity without significantly increasing the path length, but a new problem arises. Sensors located near the border of the deployment area are not able to estimate their locations. So, coverage is not fully achieved by this approach and error will be increased. CIRCLES and S-CURVES were proposed in [28] to reduce the amount of straight lines and mitigate the collinearity problem of path planning methods. Although they produce the shortest path length amongst the other methods, CIRCLES leaves the four corners, uncovered. A spiral trajectory for mobile beacon was proposed in [29]. The trajectory has trivial differences with CIRCLES and effectively solves the collinear problem as well the localization accuracy. However, the trajectory suffers from long path lengths and uncovered areas near the border of the network field. Han et al. [30] introduced a path planning scheme for localization based on trilateration. The mobile beacon moves according to an equilateral triangle to broadcast its current position. The path type successfully copes with the collinear beacons problem but it cannot maintain the trajectory through the whole network field. It causes to increase the localization error on the border of the deployment area. Moreover, the path length travelled by the mobile beacon is long. Authors in [12] have proposed a scan-based path planning scheme which can be directly applied to the localization method proposed by Ssu et al, in [10] to meet the specific requirements of the localization method. Moreover, the obstacle resistant trajectory has been considered to handle the obstacles where the obstacles can block the mobile beacon trajectory.


Since all the described path planning methods make the beacon movement possible along the statically deterministic trajectories without the reference to the actual distribution of the unknown nodes, several real time or dynamic path planning schemes were introduced in [5], [31], and [32] to consider the real distribution of the sensor nodes. The major drawback of real time path planning schemes in localization is the high numbers of message exchanges and high energy consumption. III. P ROPOSED M OBILE B EACON PATH P LANNING The novel mobile beacon path planning mechanism presented in this section is named Z-curve where the basic curve of the trajectory shown in Figure 2(a) is built based on the Z shape. The key motivation for designing the Z-curve is that such a trajectory has short jumps to overcome the collinear beacons problem and creates a superior path for transmitting three consecutive non-collinear beacons in order to reduce the localization time. If the mobile beacon moves on the Z-curve, unknown sensors have the chance to be localized more accurately while the trajectory is maintained through the border of the deployment area and the whole network field. We use the concept of the level of the curve as follows. The basic curve is said to be of level (1) where l=1. To derive level (l), a 2-dimensional field must be divided into 4l subsquares and the mobile beacon connects the centers of the cells while undertaking the Z-curve. The procedure to map level (l-1) into level (l) is to replace each vertex of the basic curve (C1, C2, C3, C4 in Figure 2(a)) with level (l-1), which may be appropriately rotated and/or reflected to fit the new curve. We illustrate level (2) and level (3) of Z-Curve in Figure 2(b) and 2(c), respectively. The proposed path planning mechanism consists of four phases and is described as follow: Phase 1: This phase aims to analyze the relation between the communication range and localizability of unknown sensors. All unknown sensors are localizable by Z-curve, if: (1) ∀si(i=1,...,n) ∃ b j ( j =1,2,3) | di st (b j , si ) ≤ Rc where si denotes unknown sensors and b j indicates the beacon messages transmitted from three different beacon positions (e.g. C1 ). di st (b j , si ) and Rc are distance between sensor and beacon, and communication range, respectively. Phase 2: The main goal of this phase is to adjust the communication range of the mobile beacon traversed by the Z-curve as all sensors fully cover for localization. As described, the network field in level (1) is divided into four sub-square, namely sqk , (k = 1, . . . , 4) and the centroid of each sub-square is shown by Ck . Co denotes the center of the basic curve. We define the resolution of the proposed trajectory as the side length of each sub-square and denoted by d. In order to provide the full coverage by the Z-curve, it is a requirement that the beacon message transmitted at Ck position would be received by sensors resident at the same sq in addition with two more adjacent sq. Then, each unknown sensor would be covered by three received beacon messages. Let s1 denote the most distant sensor (settled on the adjacent sq) from C1 . If s1


Fig. 2.

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Z-curve traveling mechanism.

can receive the beacon message from C1 , then it will guarantee that the message would be heard by all the sensors resident inside sq1 , sq2 , sq3 . It is observed from Figure 2(a) that: d 3d (di st (C1 , s1 ))2 = ( )2 + ( )2 2 2 5 d ⇒ di st (C1 , s1 ) = 2 where (di st (C1 , s1 )) denotes the distance between sensor s1 and beacon position C1 . It is quite a straightforward condition for applying the Pythagoras theorem. On the other hand, wecan get from equation (1) that di st (b j , si ) ≤ Rc . So, Rc ≥ 52 d. It also is valid for C2 , C3 and C4 . By doing so, all the unknown sensors are able to receive three beacon messages and fully cover for localization when the mobile beacon traverses based on the Z-curve with Rc ≥ 52 d. Phase 3: Shortest path will be selected by the mobile beacon to convey three beacon messages to unknown sensors in the area traversed by the Z-curve path planning mechanism. As can be seen in Figure 2(a), the first step in the Z-curve path is started by connecting the centers of two adjacent sq. The mobile beacon in position C1 provides beacon information for si deployed in sq1,2,3 . Similarly, unknown sensors inside sq1,2,4 collect the message from C2 . Moving on the Z-curve gives the chance to achieve the third beacon through shortest path. The beacon message generated from Co is collectable by si in sq1,2,3,4. Hence, all the sensors inside the lower half area are localizable and the process will be repeated for the upper half side. This phase specifically indicates the shortest path is traversed by the mobile beacon based on the Z-curve trajectory. Phase 4: The path dictated by the Z-curve provides three noncollinear continuous beacons via the shortest length. Let MSG represent a matrix formed by the coordinates of the three consecutive received beacons (x c1 , yc1 ), (x c2 , yc2 ), (x co , yco ) in positions C1,2,o . x − x C 1 yC 2 − yC 1 M SG = C2 (2) x C o − x C 1 yC o − yC 1

Hence, the three received beacons are non-collinear, when |M SG| = (x C2 − x C1 )(yCo − yC1 ) − (yC2 − yC1 )(x Co − x C1 ) = 0

(3)

It is clear that |M SG| implies the determinant of matrix MSG. The last phase aims to demonstrate that the collected three beacon positions via the shortest possible path dictated by the Z-curve are noncollinear. The total distance traveled by the mobile beacon based on the Z-curve trajectory at level (l) and resolution d is given by:

√ 5 3 length (Z −curve) = ( × 4l ) − 1 d + ( × 4l ) 2d (4) 8 8 Figure 2(a) also implies that total length travelled in level √ (l) is equal to 2d + 2d. • Obstacle-presence Scenario The mobile beacon traveling along the trajectory can be challenged in the presence of obstacles. However, in a realistic environment, obstacles may appear in the network field and block the path traversed by the mobile beacon. Among all the proposed static path planning mechanisms, the developed obstacle-resistant trajectory in [12] could handle the obstacle problem where the mobile beacon detours around the obstacle and broadcasts the beacon messages with a detour flag. An unknown sensor employs the beacon messages with the detour flag to calculate the virtual beacon position to obtain its own location. Once the mobile beacon moves away from the obstacle, it returns back to the original trajectory and start broadcasting the normal beacon messages again. As it is assumed in [33] and [34], the mobile beacon is able to discover an unknown obstacle where it resides within the communication range. Moreover, since the Z-curve is a deterministic path, the movement pattern and the beacon positions for message transmission are already known. Thus, the mobile beacon can recover the trajectory once it moves away from the obstacles. In this case, a straightforward solution for the mobile beacon is to cross the edge of the obstacle and return back to the path, immediately. However, the performance will be degraded due to forfeiture of the beacon positions covered by the obstacles. For instance, in Figure 3 the unknown sensor s1 resident in the sq2 cannot receive a sufficient number

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Fig. 3.

Z-curve obstacle-presence.

of messages for localization, because Co beacon position is obstructed by the obstacle. A beacon message transmitted in location C4 is also infeasible due to the obstacle and C5 creates a collinear position for s1 . Consequently, simply detouring around the obstacles to catch up with the trajectory is not enough for localization. Here, a Z-curve obstacle-handling trajectory is investigated to tackle the obstacle problem in a real environment. To develop the Z-curve trajectory at the presence of the obstacles, we define two different modes for the mobile beacon, normal mode and obstacle mode. The mobile node is initially in the normal mode and applies the original Z-curve trajectory (proposed for the obstacle-free environment). As the obstacle is detected, the obstacle mode will be replaced and the mobile beacon transmits its location (i.e., position M in Figure 3). Meanwhile the beacon detours around the obstacle and the beacon positions are broadcasted per each turn. This implies that changing the movement direction in the obstacle mode obliged the mobile beacon to broadcast its own position (i.e., m 1 and m 2 in Figure 3). These messages give the opportunity to the unknown sensors to receive a sufficient numbers of messages for localization. Once the mobile beacon moves away from the obstacle, it applies the normal mode to return the original path and the beacon message is started to broadcast at the leaving position (e.g., M‘ in Figure 3). The Z-curve obstacle-handling trajectory can cope with the obstacles where the transmitted messages around the obstacle are accurate enough for replacing the beacon positions covered by the obstacle. The Z-curve obstacle handling trajectory is depicted in Figure 4 where the gray blocks represent the obstacles and the line shows the Z-curve Obstacle-handling trajectory in level (3). IV. P ERFORMANCE E VALUATION In this section, we first describe different techniques which are considered in performance evaluation. Evaluation metrics are investigated and simulation setup with related parameters is also described. Moreover, the employed wireless channel

Fig. 4.

Z-curve obstacle handling trajectory.

is discussed in details. We also generate a variety of results through simulations and discuss them in this section. A. Localization Techniques A fundamental issue in studying the effectiveness of mobile beacon trajectories is the employed localization technique. In some localization methods all received location information by the unknown nodes is contributed in the localization process such as the methods proposed in [7], [9], while other techniques rely on selected beacon messages for location estimations like the techniques in [10]. Since the efficiency of path planning is influenced by different localization techniques, we consider both types of position calculation for further investigation. Moreover, since single hop networks have higher performance than multi-hop networks [35], all scenarios in this paper consider single-hop localization techniques. Three different position estimation methods are utilized including Weighted Centroid Localization, Time-Priority Trilateration and Accuracy-Priority Trilateration. 1) Weighted Centroid Localization (WCL): Unknown sensors, si calculate their own positions, p(si ) based on averaging the coordinates of all Nr received beacons. To investigate the impact of different received coordinates b j (x, y), the WCL method proposed in [36], defines a weight function wi j which depends on the RSS value of the mobile beacon at different positions. It is formulated as follows: Nr j =1 (wi j .b j (x, y)) p(si ) = (5) Nr j =1 wi j After replacing wi j by RSSi j the final equation is formed: Nr j =1 (RSSi j .b j (x, y)) (6) p(si ) = Nr j =1 RSSi j 2) Time-Priority Trilateration (TPT): Trilateration, as a comprehensive and most common method for deriving position of unknown nodes is considered in this Section. Once the


number of the beacon messages to start the localization process is large enough, an unknown node calculates its corresponding distance with received beacons and estimates its position via trilateration. So, for computing the position of si (x i , yi ) in a 2-D space, the following system of equations must be solved: ⎧ 2 2 ⎪ ⎨di st (b1, si ) = (x 1 − x i ) + (y1 − yi ) (7) di st (b2, si ) = (x 2 − x i )2 + (y2 − yi )2 ⎪ ⎩ 2 2 di st (b3, si ) = (x 3 − x i ) + (y3 − yi ) We compute the distance from beacons, di st (b j , si ), using the RSS technique. Time-Priority Trilateration (TPT) localizes unknown sensors with employing three earlier received messages. The main objective of this approach is estimation of the sensors’ location within the shortest possible time. 3) Accuracy-Priority Trilateration (APT): Since higher precision of estimated location is desired for all localization methods, we evaluate all the findings and results using the Accuracy-Priority Trilateration (APT) technique. It is similar to the TPT approach in the general concept and calculation, but it derives the location of the unknown sensors relying on the three nearest received messages from the mobile beacon in Equation 7. It provides the chance to estimate the location with higher accuracy while the three strongest RSS values are utilized in trilateration. All the mentioned position estimation methods is employed to compare the performance of the proposed path planning method with five different existing trajectories, namely Hilbert [4], [37], Scan [4], Circles [28], LMAT [30] and RWP [9] algorithms under different evaluation metrics. B. Evaluation Metrics To analyze and evaluate simulation results, some critical metrics need to be investigated. However, the existing related works just consider the accuracy provided by trajectories or coverage of the interested region as well the total length traversed by the mobile beacon. In spite of the fact that the mentioned metrics are necessary for performance evaluation, they are not adequate enough for expressing the efficiency of trajectories. In this section we also introduce metrics which have not been considered yet. 1) Accuracy: To analyze the accuracy of estimated locations, we consider the average localization error ratio. An average localization error is calculated by measuring the distance between the real location of a node and its estimated location. Localization error is formulated as follow: (8) err or(i) = (x ei − x i )2 + (yei − yi )2 where (x ei , yei ) shows the estimated coordinates of unknown sensor si . Hence, the average localization error, L e is given as: Le = (

n

err or(i) )/n

(9)

i=1

where n denotes the number of unknown sensors deployed over the area. Localization error is divided by communication range Rc to get the average localization error ratio.

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We can consider another aspect of localization accuracy to effectively evaluate the performance of localization. The standard deviation of the localization error (std) is a measure of how spread out the average localization error is. It is defined by the following equation: n 1 (err or(i) − L e )2 (10) std = n i=1

A low value of std indicates that the error estimated for the location of the nodes tends to be very close to the average localization error. 2) Localization Time: The motivation of this paper comes to tackle the drawback of existing trajectories of mobile beacon assisted localization. Accuracy or coverage provided by the mobile beacon is imperfect without considering the average time spent per unknown sensors localization. We define the average localization time, L t , as: Lt = (

m

(tloc(i) − trec(i) ))/m

(11)

i=1

where m defines the total number of localized sensors and tloc(i) and trec(i) show the completed time of localization and the received time of the first beacon message, respectively. 3) Localization Success: Through all the covered unknown sensors by the mobile beacon (the sensors that received at least one beacon message), some of them are localizable while they collected more than three messages. However, the important issue is the percentages of successfully localized sensors from the localizable nodes. The number of successfully localized sensor nodes (m) to the total number of unknown nodes (n) is defined as localization success L S . It is clear that the non-localized sensors are shown by localization failure. 4) Ineffective Position: In spite of the fact that increasing the average number of the received messages by the unknown sensors can improve the localization success, we introduce a new related metric which analyzes the efficiency of trajectories with regards to the created useful positions obtained from the received messages. Ineffective position (I p ) is produced by the number of triple beacons which are collinear. Let N p denotes the total number of ways for selecting three different beacon positions extracted from the received messages per unknown sensor. So, Nr ! Nr = (12) Np = C 3 3!(Nr − 3)! Consequently, Ip = Np − E p

(13)

where I p and E p denote ineffective positions and effective positions, respectively. E p refers to the number of noncollinear triple beacons which is determined by Equation 3. The ineffective position is divided by successfully localized sensors to get the ineffective position rate. In other words: I pr = (

n i=1

I pi )/m

(14)

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TABLE I S IMULATION PARAMETERS

where I pr shows ineffective position rate. Our concern is the reduction of ineffective position rate amongst received beacon messages per unknown sensor. C. Simulation Setup and Wireless Channel 1) The Wireless Channel: In order to perform a reliable evaluation, a realistic wireless link model is highly important. Hence, in this section we consider the channel model, modulation, and encoding scheme to extract the relationship between the transmission power and packet reception rate. Since the signal strength decays due to wireless propagation, path loss and bit error rate must be modeled for analyzing the physical layer [38], [39]. Let Prr denotes the packet reception rate which means the probability of successfully receiving a packet. The nature of Prr is a Bernoulli random variable which takes the value 1 if the packet is received and 0 for failure. It is given by: (15) Prr = (1 − Pbe )8 f ×M where f = 20 byte is the size of the frame based on the TinyOS implementation after being encoded (the frame consists of preamble, network payload and CRC). The Manchester encoding scheme is employed, so M = 2. Pbe is the probability of bit error which depends on the modulation scheme. Here, we selected non-coherent F S K modulation which is used in MICA2 motes and formulated by [38]: S N R BN 1 (16) Pbe = exp− 2 R 2 where B N is the noise bandwidth and R is the data rate in bits. MICA2 motes use the Chipcon CC1000 radio [40] where R = 19.2 kbps and B N = 30 kHz. The signal to noise ratio (S N R) at the receiver is calculated by: dB − Pnd B S N R d B = Prec

(17)

Prec shows the reception power and Pn defines the noise floor. Pn depends on both, the radio and the environment [41]. It is given by: Pn = (F + 1)kT0 B N (18) where F = 13d B is the noise figure and k is the Boltzmann’s constant. T0 = 27◦ C is the ambient temperature and B N is the equivalent bandwidth. The average noise floor is approximately −105dBm which has a 10dBm difference with the analytically computed value [39]. On the other hand, Prec is given as: dB dB = Ptrans − PLd B (19) Prec dB where PLd B and Ptrans are the power loss and transmitting power, respectively. To model the shadowing path loss effect, a log-normal model [41], as the most common model is utilized which gives: d PL (d)d B = PL (d0 )d B + 10γ log( ) + X σd B (20) d0

where PL (d)d B is the power loss after the signal propagates through distance d, PL (d0 ) is the power loss at the reference distance d0 , γ is the path loss exponent and X σ = N(0, σ 2 ) is a Gaussian random variable with mean 0 and standard deviation σ (shadowing effect).

2) Parameter Setting: The performance of the proposed path planning mechanism was evaluated by a series of simulations using MATLAB. We assume a random deployment of static sensors and a single mobile beacon moving around them. The results were performed for both the obstacle-free and obstacle-presence scenarios. The other parameters are listed in Table I. D. Simulation Results •

Obstacle-free Scenario In this scenario, we conduct extensive simulations in the obstacle-free or ideal environment. Hence, the mobile beacon adopts the developed path with no interruption. 1) Accuracy: Figure 5(a) plots the error ratio of trajectories for the WCL technique. We observe that our propose trajectory, Z-curve dedicates the lowest localization error by itself. This superiority is explained based on the fact that the beacons participating in the WCL technique are accurate enough where the mobile beacon is conducted to pass closely to the unknown nodes by the Z-curve. It reaches its highest level of accuracy when the ratio of range to resolution Rc /d is around 23 . However, in Rc /d = 3, LMAT slightly outperforms Z-curve. We also investigated the standard deviation of the localization error in the WCL technique depicted in Figure 5(b). As the ratio of range to resolution (Rc /d) increases, the standard deviation of the localization error also slightly increases. However, for all trajectories (except RWP) the lowest level of the standard deviation is achieved when 1 ≤ Rc /d < 2. This behavior is explained by the fact that the accuracy can be improved when the number of the received beacons from a further distance is decreased. It can be deduced from Figure 5(b) that Z-curve is able to provide much more reliable position estimations where its standard deviation of the localization error is lower than other trajectories. Figure 5(c) and 5(d) show the average localization error ratio and standard deviation of the error for the TPT localization technique, respectively. A significant finding from the figures is that the localization error ratio will be increased with increasing the ratio of the communication range to resolution. The trend is observed since three earlier messages required for localization can be collected from farther beacon positions. In this case, Z-curve again outperforms


Fig. 5.

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Localization errors of the trajectories and the corresponding Std for WCL (a,b) and TPT (c,d) methods (beacon speed=2m/s).

Fig. 6.

Localization error and its Std for APT technique (beacon speed=2m/s).

other trajectories, although LMAT and Hilbert methods can achieve an acceptable level of accuracy. We observed from Figure 5(d) that as the ratio of the range to resolution is increased, the standard deviation of the error is also increased. Our proposed path planning mechanism still outperforms the other schemes because three earlier received beacons are non-collinear and close enough for improving the accuracy. Higher accuracy is promised by the APT approach. Figure 6(a) also confirms the efficiency of trajectories in terms of the localization error when employing APT method. Z-curve still outperforms the other path planning approaches, however the difference between the proposed method and Hilbert, LMAT and Scan is negligible. For further investigation, we illustrated the localization error of trajectories in meters in Figure 6(b). It is observed that the increment of the ratio of Rc /d did not have a significant impact on the localization error. This can be explained by the fact that the three closest beacons will be selected for localization by APT. So, increasing the communication range does not have an effect for beacon selection. Generally speaking, based on Figure 6(c), as the communication range increases the standard deviation of the error decreases. It is a significant achievement for our proposed trajectory as it has the lowest standard deviation of error (0.003Rc with Rc /d = 3). It means that the resulting accuracy of Z-curve is much better. Figure 7 compares the localization error ratio under different values of the standard deviation of noise for various

trajectories in the three approaches. We have performed this set of simulations where the ratio of the communication range to resolution (Rc /d) equals 5/3. It is easily observed that the accuracy of the estimated location by three different localization techniques decreases as the standard deviation of noise is increased for all the path planning mechanisms. The important finding is that the proposed path planning mechanism has lower fluctuation than the other methods with increasing the standard deviation of noise. We can deduce from the behavior that Z-curve (and LMAT as the second trajectory) has less sensitivity to the standard deviation of noise, especially in the APT technique. As a conclusion for accuracy achieved by various trajectories when employing three localization techniques, we summarized the average localization errors in Rc and the related standard deviation of the estimated errors in Table II. Each approach provides highest accuracy based on a specific ratio of Rc /d. The ratio of the range to resolution was adjusted by Rc /d = 5/3, 1 and 2 for WCL, TPT and APT approaches, respectively. It is observed that Z-curve outperforms the other trajectories in all the approaches. However, LMAT and Hilbert achieved the closest accuracy to Z-curve. Our proposed trajectory also has the lowest value of the standard deviation of error. It means that it has a more stable precision. 2) Localization Time: In this set of simulations we measure the average localization time of trajectories where the mobile beacon velocity is set to 2m/s [4]. We consider two trilateration approaches (APT and TPT) illustrated in Figure 8. The WCL technique is not investigated in this section as

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Fig. 7.

Localization error versus standard deviation of noise for WCL, TPT and APT techniques ( Rdc = 53 , beacon speed=2m/s). TABLE II AVERAGE AND S TANDARD D EVIATION OF E RRORS FOR T HREE A PPROACHES AT F INE VALUE OF Rc /d FOR E ACH M ETHOD . ( Rc /d FOR WCL = 53 , TPT = 1, APT = 2.)

Fig. 8. Impact of Rc /d values on the average localization time in (a) TPT and (b) APT methods. (mobile velocity is 2m/s). (c) Time versus beacon traveling speed by applying TPT method for Rc /d = 3/2.

it has similar policy with APT for calculating the required localization time. Figure 8(a) compares the impact of (Rc /d) on the average time required for location estimation per unknown node for different trajectories in the TPT approach. It is observed from Figure 8(a) that increasing the ratio of (Rc /d) does not have a significant enhancement on the required time for localization in most cases (except RWP). Hilbert, Scan, LMAT and Z-curve have a common trend under varying values of (Rc /d). Another finding is that all the path planning mechanisms reach approximately a fixed average localization time when (Rc /d) = 1 and above (except for RWP). The Circles trajectory outperforms the others in this approach as it properly solves the collinearity problem and location is obtained in a shorter time. However, it has the largest error as shown in Table II. On the contrary, increasing the ratio of the communication range has a direct impact on increasing the average localization time in the APT technique. Since an unknown node should wait to collect all the beacon

messages for estimating its location, the time required for localization increases as the range is increased. Our proposed path planning mechanism requires a shorter localization time than the other trajectories. Figure 8(c) plots the average localization time per node under various travelling speeds of the mobile beacon from 0.5 to 4 m/s where Rc /d = 3/2. It is observed that the travelling speed greatly influences the localization time and a higher speed degrades the average localization time per node. 3) Localization Success: Localizability of the proposed trajectory and the existing path planning mechanisms are investigated under different values of (Rc /d) as depicted on Figure 9(a). In this set of simulations, we do not consider any of the mentioned position estimation techniques as the localization success is not affected by the localization technique. The success of trajectories must be evaluated when they simply receive sufficient numbers of beacon messages (at least three messages). An increase in the communication range produces


Fig. 9.

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Impact of Rc /d on (a) localizability, (b) success, (c) comparison of coverage, success and localizability of trajectories (Rc /d = 3/2).

Fig. 10. (a) Impact of Rc /d on the number of collinear positions. (b) Comparison of success (left y-axis) and collinearity (right y-axis) for Rc /d = 3/2. (c) Localization success obtained over different fraction of time where Rc /d = 3/2 and beacon velocity=2m/s.

a significant enhancement on the percentage of the localizable nodes for all the trajectories, except for RWP. The highest level of localizability is reached at Rc /d = 3/2 and above. Figure 9(b) plots the percentage of successfully localized sensors amongst the localizable nodes. It is observed that our proposed path planning strategy enables all the localizable sensors to be successfully localized when the ratio of the range to resolution reaches the value of Rc /d=3/2. Hereinafter, Z-curve can result in the highest localization performance (100%) and remains constant. LMAT has almost the same localization success and both schemes slightly outperform Hilbert. The improvement of localization success with increasing the communication range is due to the larger number of received messages by unknown nodes. As a comparison between the percentages of covered, localizable and successfully localized sensors, a bar graph is shown in Figure 9(c) where Rc /d = 3/2. In this figure, the superiority of Z-curve and LMAT is confirmed. The remaining path planning schemes leave some percentage of sensors, non-localizable. To show the efficiency of trajectories, localization failure is marked and defined as the percentage of non-localized sensors amongst the deployed unknown nodes. 4) Ineffective Position Rate: To better reflect the efficiency of a trajectory for mobile-beacon assisted localization, we investigated the ineffective position rate produced by the different path planning mechanisms. Indeed, the ineffective position rate explains the collinearity of the methods.

The efficiency of a path planning mechanism will be confirmed by fewer ineffective positions with higher localization success. These useless positions increase the computation complexity and energy consumed by the unknown sensors. It is observed from Figure 10(a) that our proposed trajectory can successfully achieve the lowest ineffective position rate. However, LMAT, Circles and Hilbert also cope effectively with the collinearity problem. RWP and Scan have a high ineffective position rate as much more collinear positions are caused for localizing the unknown sensors. The bar graph illustrated in Figure 10(b), highlights a significant result in terms of the ineffective position rate for beacon movement mechanisms where Rc /d = 3/2. It compares the acquired levels of localization success and the corresponding ineffective position rate. The essential issue is that the difference between the number of localized sensors and ineffective position rate should be as large as possible. As can be clearly seen, for RWP and Scan trajectories the number of futile positions is relatively high against the localization success. In contrast, Zcurve can successfully localize all 250 sensors at the expense of 92 useless positions per localized sensors while the value for LMAT is about 102, as the second most efficient method. The progress of localization success under different fractions of time is shown as localization acceleration in Figure 10(c). Indeed, the monitoring of the localization success behavior with time for the path planning mechanisms is non-negligible when the successfully localized sensors should

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Fig. 11.

Comparison of obstacle-free, obstacle-presence and obstacle-handling in terms of accuracy, success and ineffective position rate.

timely report their gathered data for further processing. We assume that the total required time for the mobile beacon to traverse the entire network is divided into 100 time units. In this result Rc /d = 3/2 and beacon speed is 2m/s. As shown in Figure 10(c), when 10% of the time passed, Z-curve and Circles have more than 10% success. At the same time, Scan is not able to localize any sensors. As shown in Figure 10(c), the localization acceleration of the Circles path scheme surpassed the others when 50% of the time is passed. However, it does not guarantee its efficiency while its success is lower than 90% when the time is over. At the same time, Z-curve and LMAT reach 100% localization success. 5) Traveling Length: A straightforward solution for reducing the energy consumption is shortening the total length travelled by the mobile beacon. However, an optimum path planning mechanism is able to reduce the energy consumption through the lowest number of ineffective messages transmitted by the mobile beacon. Here, we compare the total distance travelled by the mobile beacon in five different trajectories with the proposed mechanism. The total path length travelled by the mobile beacon in Z-curve is given by the Equation 4 in Section III. The length of the Circles, Scan, Hilbert and LMAT trajectories are calculated by: l l 2π S + ( − 1)S 4 2 S2 − 2d = (4l − 2)d = d S2 − d = (4l − 1)d = d √ 2 S + (S + 3d) = √ ×S× d 3

length (Circles) =

(21)

length (Scan)

(22)

length (H ilbert ) length (L M AT )

(23) (24)

when the communication range is as same as the resolution and equal Rc = d = 12.5m and S = 100m the total distance travelled by the trajectories for level (3) is equal to 756 m for Circles, 775 m for Scan, 787.5 for Hilbert, 911.76 m for Z-curve, 1045.41 m for LMAT and 1512.3 m for RWP. As can be seen, our proposed path planning mechanism travels a longer distance than Circles, Scan and Hilbert, but it still surpasses the LMAT and RWP trajectories. •

Obstacle-presence Results

In this set of simulation results, we consider the existence of the obstacles in the network field. There are 7 obstacles in size of d × d/2 m and 7 obstacles in size of d/2 × d m. It means that 10% of the network field is covered by the obstacles. In the presence of the obstacles, all parameters are the same as in the obstacle free environment. In Figure 11(a), the average localization error of the path planning in obstacle-free scenario is compared with the obstacle-presence and Z-curve obstacle-handling scenario. We employ the APT localization technique to evaluate the localization accuracy under different values of range to resolution. It can be seen that the localization error is kept to 0.035R where Rc /d = 5/2 at the presence of obstacles but the obstacle-free environment has a localization error of 0.015R. The lower precision is due to the fact that the unknown sensors may not receive enough beacon points for location estimation. It can be seen that the Z-curve obstacle-handling trajectory is able to significantly improve the efficiency of the localization at the presence of the obstacles. However, the localization accuracy is still lower than the obstacle-free environment due to the uncovered unknown sensors by the mobile beacon for localization. Figure 11(b) shows the percentage of successfully localized sensor nodes for obstacle-free, obstacle presence and obstacle handling. It is observed that the localization success degrades when the mobile beacon encounters obstacles. With the Z-curve obstacle handling, almost always the unknown sensors are able to determine their locations successfully (approximately 98% in Rc /d = 3/2). The Z-curve obstacle handling results in the lower percentage of localized nodes since a few sensors do not have the chance to obtain enough beacon messages for localization, even though the mobile beacon transmitted the location information at the presence of the obstacles. The bar graphs depicted in Figure 11(c) compare the localization success for obstacle-free, obstacle presence and obstacle-handling where Rc /d = 3/2. The right y-axis indicates the ineffective position rate. Because of the obstacles, the numbers of successfully localized sensors degrade and results in 88% localization success. The Z-curve obstacle-handling trajectory can significantly improve the percentages of successfully localized sensors at the presence of obstacles up to 98%. However, the messages transmitted on the obstacle-handling trajectory are imposed by the location


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Abdul Samad Ismail (M’07) is a Professor with the Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia. He graduated from the University of Wisconsin–Superior, and Central Michigan University. He received the Ph.D. degree from the University of Wales Swansea, Swansea, U.K. He is currently the Senior Director of Centre for Quality and Risk Management at UTM. He is also a member of the IEEE Computer Society and the Pervasive Computing Research Group at UTM. He is involved in several research related to computer networks, in particular, wireless sensor networks, mobile adhoc networks, grid computing, network security, and collaborative virtual environments.

Javad Rezazadeh (S’11) is currently pursuing the Ph.D. degree at the Department of Computer Science, Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia. He received the B.Sc. and M.Sc. degrees in software engineering in 2005 and 2008, respectively. He is a member of the Pervasive Computing Research Group at UTM. He was a Lecturer for more than five years and a Software Design Engineer for the industrial applications around 10 years. His research interests include mobile localization, location tracking algorithms, mobility model, performance evaluation, and mathematical modeling issues in wireless sensors and ad hoc networks.

Marjan Moradi (M’11) received the M.Sc. degree in computer science from the Faculty of Computing, Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia, in 2013. She received the B.Sc. degree in scientific-applied computer software engineering in 2009. She is currently an Active Researcher with the Pervasive Computing Research Group at UTM. She was a recipient of the Excellent Researcher Award from the Faculty of Computing at UTM in 2013. She is a member of the IEEE Women in Engineering. Her research interests include mobile localization, location tracking algorithms, mobility model, performance evaluation, and mathematical modeling for underwater and terrestrial wireless sensors and medical body area networks.

Eryk Dutkiewicz (M’05) received the B.E. degree in electrical and electronic engineering and the M.Sc. degree in applied mathematics from the University of Adelaide, Adelaide, SA, Australia, in 1988 and 1992, respectively, and the Ph.D. degree in telecommunications from the University of Wollongong in 1996. He was the Manager of the Wireless Research Laboratory at Motorola, in early 2000s. He is currently a Professor of Wireless Communications and the Director of the WiMed Research Centre at Macquarie University, Sydney, NSW, Australia. He has held visiting professorial appointments at several institutions, including the Chinese Academy of Sciences, the Shanghai Jiao Tong University, Shanghai, China, and Coventry University, Coventry, U.K. His current research interests cover medical body area networks and cognitive radio networks.