Energy-Efficient Detection and Monitoring for Continuous Objects in ...

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PAPER

DEMOCO: Energy-Efficient Detection and Monitoring for Continuous Objects in Wireless Sensor Networks Jung-Hwan KIM† , Member, Kee-Bum KIM† , Sajjad Hussain CHAUHDARY† , Wencheng YANG† , and Myong-Soon PARK†a) , Nonmembers

SUMMARY The proliferation of research on target detection and tracking in wireless sensor networks has kindled development of monitoring continuous objects such as fires and hazardous bio-chemical material diffusion. In this paper, we propose an energy-efficient algorithm that monitors a moving event region by selecting only a subset of nodes near object boundaries. The paper also shows that we can effectively reduce report message size. It is verified with performance analysis and simulation results that total average report message size as well as the number of nodes which transmit the report messages to the sink can be greatly reduced, especially when the density of nodes over the network field is high. key words: wireless sensor networks, target tracking, boundary, continuous objects

1.

Introduction

Large scale wireless sensor networks have been enabled by rapid technological advances in MEMS and wireless communication. They are used in a wide variety of monitoring applications, ranging from habitat/environmental monitoring to military surveillance. One of typical and famous research area in wireless sensor networks is target tracking. There have been enormous research achievements on target tracking with sensor networks. The majority of them are to identify and track one or multiple number of small moving targets. However, there have been relatively small research efforts on detection and tracking continuous objects such as forest fires, mud flows, bio-chemical material diffusion and oil spills. To monitor such phenomena in real time, it usually requires inordinate amount of message exchanges between sensors to collaboratively estimate the objects’ movement and location information. Therefore, it is judicious to invent an efficient algorithm that minimizes the communication cost as much as possible in operating sensor networks so as to prolong the lifetime of the sensor networks. There are various ways to monitor continuous objects. The simplest way would be to let every node that actually detects an object transmit its reading to the sink node (Fig. 1(a)). The sink is a node that relays gathered information, i.e., boundary information to outside computers or the Internet. However, this approach causes the nodes to dissipate energy at breakneck pace. In [1], X. Ji et al. propose a mechanism that selects only a few nodes nearby object boundaries that are grouped into Manuscript received June 6, 2008. The authors are with the Department of Computer Science and Engineering, Korea University, Seoul, Korea. a) E-mail: [email protected] DOI: 10.1093/ietcom/e91–b.11.3648 †

Fig. 1

Comparison of number of nodes that send report messages to sink.

several clusters (Fig. 1(b)) [1]. Solid dots in Fig. 1(b) depict nodes selected nearby the boundary of an object (a.k.a. boundary nodes, hereafter it is acronymized as BNs). It is clear that there would be much more energy saving compared to Fig. 1(a) since only those which are near the object boundary are associated with transmitting data to the sink. In [2], C. Zhong and M. Worboys propose a new energy-efficient boundary monitoring algorithm described in Fig. 1(c). According to [2], although more BNs are selected than [1], the number of nodes that actually report to the sink (a.k.a. representative nodes, hereafter it is acronymized as RNs) is much less than [1]. The RNs are drawn red triangles in Fig. 1(c). In this paper, we present an energy-efficient algorithm that detects boundaries of moving phenomena so as to monitor their shapes and movement in wireless sensor networks. We will focus on reducing the number of BNs that eventually results in lowering the number of RNs to reduce traffic as well as communication between sensor nodes. Figure 1(d) illustrates nodes that are possibly selected as BNs and RNs in our proposed architecture when the object

c 2008 The Institute of Electronics, Information and Communication Engineers Copyright 

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shrinks. We also show that light report messages will be sent to the sink, particularly when many nodes are set up in the field. Furthermore, we allege that expected shape from our algorithm is as near as ones in previous work. It is assumed in our paper that continuous monitoring and a node is regarded to be in the event region if the node’s reading is beyond a threshold value. Moreover, although it is more realistic to consider three-dimensional space, we examine only two-dimensional space in our architecture for simplicity. The rest of this paper is organized as follows: Sect. 2 discusses previous work and explores the shortcoming of it. In Sect. 3, we present definitions as well as assumptions required for our algorithm. Section 4 presents the proposed algorithm in detail and Sects. 5 and 6 analyze the performance of our algorithm with mathematics and simulation. Finally in Sect. 7, we conclude our paper. 2.

Related Work

There has been abundant research on detecting and tracking single or multiple targets in sensor networks [7], [8]. In [4] and [9], authors analyze the detection of non-local events which are closely related to the continuous object detection and tracking. The main difference is that they do not examine the situation where the phenomena change their shapes in real time fashion. Furthermore, [4] may lead to massive quantity of energy consumption since all BNs report data to the sink. Xiang Ji et al. [1] propose a dynamic cluster based algorithm that tracks the movement of continuous objects by monitoring the boundary of those objects. In [1], when a sensor detects the emergence of any phenomena at current time, it immediately broadcasts a query message to its neighbors to ask for the neighbors’ readings and the neighbors reply by sending their current readings to the sensor. If the sensor receives at least one different detection status from any neighbors, the sensor becomes a BN. After the boundary node selection, cluster formation process takes place among the BNs. However, the cluster formation explanation in [1] is somewhat ambiguous. Also, we claim that the clustering formation itself is not an appropriate approach when the goal of forming clusters is to save energy since the application they consider is tracking objects that randomly and unexpectedly diffuse and drift with gust in real time. Above all, the main drawback of the paper [1] appears where every BN including every cluster head is directly or indirectly involved in routing data to the sink, which will yield more overhead and traffic. In [2], COBOM, an energy-efficient algorithm for boundary detection and monitoring is proposed. When a sensor’s current reading is different from the one previously observed, the sensor broadcasts its reading and ID. A node that receives the reading and ID stores the received reading into its array (called BN-array) (Fig. 2) and if the node finds that any different reading exists in the BN-array, then the sensor becomes a BN. Among those BNs, a few RNs will be selected. The more number of different detection status a BN has in

Fig. 2 Node z’s BN-aray is shown. Reading status of neighbors is stored in anti-clockwise cyclic order.

its array, the more likely it becomes a RN that eventually reports all the gathered detection result to the sink. This algorithm is energy-efficient in a way such that 1) only a few representative nodes will be chosen and 2) by using the BNarray, the report message size would not increase considerably since each message contains all of its neighbors’ detection status as bits only rather than keeping the neighbors’ IDs also, which requires only few bits while the precision of boundary estimation might be guaranteed. In this paper, however, we claim that we can get fewer numbers of BNs than [2], which will also lead to fewer numbers of representative nodes. As few representative nodes will be chosen, more energy saving will be achieved. Further discussion in detail will be made in Sects. 4.1.2 and 4.1.3. We also argue that if we can truly get more precise prediction on the expected shape of the objects when RNs report all their neighbors’ detection status to the sink. We will discuss in more details about this argument in Sect. 4.2. 3.

Preliminaries

In this section, we make general assumptions about sensor nodes and the framework of sensor networks. And then we discuss definitions employed in our algorithm. 3.1 Assumptions • Hundreds or thousands of static sensor nodes and a sink node are deployed. • The sensor node is arbitrarily deployed in the network. • Every node has the same capability and functionality. • Each node has the same communication range r. • The sink node knows all the sensor nodes’ IDs and locations. • Each sensor node knows its own location by possibly using the global positioning system (GPS) [5] or other techniques such as triangulation [6] or localization [3]. • Possible data loss or contention is not considered. • Any destruction of nodes in targeted application is not considered. 3.2 Definitions Definition 1 (Interior (IN) of a phenomenon): We define

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the interior of a phenomenon (IN) to be the spatial region R2 such that a sensor detects an event, that is, identify a higher value than a predefined threshold at present time and thus it is supposed to be placed inside the region. Its reading function would have evaluated to 1(or true). Definition 2 (Exterior (OUT) of a phenomenon): The exterior of a phenomenon (OUT) can be analogously defined as definition 1. The spatial region R2 where no phenomenon exists or reading lower than a threshold is discovered at present time is defined OUT. The reading function of the nodes in the OUT region would have evaluated to 0 (or false). Definition 3 (Neighbors (Nu )): Let u and Nu represent a node and neighbors of u respectively. The neighborhoods Nu are those nodes that are within communication range r of u. Definition 4 (Changed Value Nodes (CVNs)): Nodes which detect emergence of the object at current time (i.e., at time slot t) when they did not identify any phenomena at previous time (t − 1) or vice versa are defined CVNs. Definition 5 (CompareOneZero message (COZ message)): CVNs broadcast a COZ message to their neighbors. This message includes a CVN’s ID and detection status. Definition 6 (Boundary Nodes (BNs)): A boundary node u is a node that receives at least one COZ message with different detection status. For example, if u’s current detection status is 0 (false) and it receives from one of its neighbors, say v, a COZ message that includes v’s reading 1 (true), now u is a boundary node since u’s current detection status is different from v’s. Definition 7 (Representative nodes (RNs)): A representative node is a node that actually sends data to the sink. Only a few representative nodes will be selected among BNs to save energy. 4.

Tracking Continuous Objects with Boundary Detection

In this section, we illustrate our proposed algorithm in detail. In Sect. 4.1, we explain each step one by one carefully and in Sect. 4.2, we will discuss precision of expected boundary and BN-array in detail. 4.1 DEMOCO Algorithm In 4.1.1, we describe how CVNs react to as objects change their shapes and 4.1.2 explain how BNs are selected. In 4.1.3, we show how RNs will be chosen among the BNs. 4.1.1 CVNs and COZ Messages Step 1: Each sensor, say u, activates and makes local observations periodically. Step 2: CVNs appear when there are some changes of shape of phenomena. u becomes a CVN when the detection status of the previous time slot t − 1 and the present time slot t are different.

Step 3: Each CVN broadcasts a COZ message to its neighbors. The COZ message includes its own ID and detection status. The more significant expansion, shrinking or change of the object shape occurs, the more number of sensors will be implicated in broadcasting the COZ message to its neighbors since more sensors’ readings will be changed 0 (false) to 1 (true) or vice versa. 4.1.2 Boundary Nodes Step 4: A sensor u may receive the COZ messages sent from its neighbor CVNs (i.e., the CVNs are in u’s communication range) and compares this with its own current detection status. If the detection result is the same, then u ignores the COZ messages. If u receives any COZ message that includes different detection status from its own, now u is called a boundary node (BN) and it counts the number of receiving COZ messages during a given time. Based on the number of COZ messages received, the sensor will set a different waiting time. Figure 3 is presented for clarification of the explanation for CVNs and BNs. Figures 3(a) and (b) show which sensors become BNs when the continuous object is expanding and shrinking respectively. As clearly illustrated, among the sensors which are adjacently located to the boundary of the object, only the nodes that are in the OUT region will get privileges to become BNs when the object expands and conversely, only the nodes in the IN region will become BNs when the object shrinks according to the proposed algorithm. This is mainly due to the BNs’ positions regarding the boundary of the object. Here, we can see one major difference between COBOM [2] and our proposed algorithm. As depicted in Fig. 3(c), COBOM algorithm selects all the nodes that are approximately situated to the boundary of objects (i.e., in the IN and OUT region) as BNs since the nodes that have any different reading in its BN-array from that of itself all become BNs whereas in our proposed architecture, only those nodes that are in the IN or OUT region nearby the boundary of the object can become BNs. 4.1.3 Representative Node Selection and Back-Off Time Step 5: It would still be energy inefficient if all the BNs are involved in sending their data to the sink. Since our main objective in this paper is to construct an energy efficient algorithm that monitors changeable continuous objects, we try to choose as few representative nodes (RNs) as possible that actually transmit the report to the sink while tolerable accuracy is reserved. An effective method to determine the RNs without excessive message exchanges would be to figure in the number of received COZ messages. Based on the number of received COZ messages that includes different detection status, each BN sets a waiting time. The higher the number of the COZ messages a BN receives, the shorter the random back-off time it will be set such that BNs more approximately situated around the real boundary of an object get a higher probability to become RNs. The back-off

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Fig. 4

Expected boundary lines.

second term of U, i.e., [W/COZtotal − 1, W/COZ total ]. By using this equation for setting the back-off time, there would be a low chance that BNs in communication range to one another are set with the same back-off time, which is to prevent potential collision. Step 6: The BNs that have shorter back-off time will wake up earlier and broadcast a message to suppress its neighbors to become RNs. The node that sent the broadcasting message becomes a RN and on behalf of the nodes around, only the RNs send report data to the sink. The report data includes the RN’s own ID and an ID of a CVN with the most powerful signal strength, which is one of the CVNs that sent a COZ message to the RN in step 4. In this way, the RN might keep the closest CVN’s COZ message that might result in better estimation of the continuous object boundary. 4.2 Discussion

Fig. 3

CVNs and BNs when expanding and shrinking.

timer is set as the following Eq. (1): D

  ⎧ ⎪ W W W ⎪ ⎪ − +U , if COZtotal > 2 ⎪ ⎪ ⎪ COZtotal COZtotal −1 COZtotal ⎪ ⎪ ⎞ ⎛ ⎪ ⎪ W W ⎪ ⎜⎜ COZtotal −1 − COZtotal ⎟⎟⎟ ⎪ ⎪ ⎨ W ⎟⎟⎠ , if COZtotal = 2 + U ⎜⎜⎜⎝ =⎪ ⎪ COZtotal 2 ⎪ ⎪ ⎪ ⎞ ⎛ ⎪ W ⎪ ⎪ ⎟⎟ ⎜⎜⎜ COZWtotal +1 − COZ ⎪ W ⎪ total ⎟ ⎪ ⎟⎟⎠ , if COZtotal = 1 ⎜ ⎪ − U ⎜ ⎪ ⎝ ⎩ COZtotal 2 (1) where D denotes back-off time, W is the maximum waiting time, COZ total implies total number of COZ messages received (with different current reading by a node) and U represents the uniform distribution in between the first and

In this part, we debate on the precision of boundary tracking through algorithms used in our proposed algorithm and in COBOM [2]. We claim that the expected shape of a typical object derived from the proposed algorithm might be as accurate as the one generated by COBOM while ours is more energy efficient. In COBOM, a RN sends all its neighbors’ detection status to the sink whereas in ours, each RN sends only one possibly closest CVN’s ID. Figure 4 compares expected shapes that can be resulted from COBOM and our proposed idea. To measure and compare the precision of the expected shape of an object, we define a boundary point as a virtual point that is on the half of a RN and its neighbor node with a different reading (in COBOM) or one of those CVNs whose signal strength was the strongest to the RN. i.e., the one explained in step 6 (in DEMOCO). We also let an expected boundary line be a line connecting all the boundary points. As shown in Fig. 4, knowing more boundary sensors may not guarantee that we can accurately localize the boundary of an object since the sensors are randomly deployed, i.e., not arranged in a horizontal line with the same distance between. Therefore, what we can expect is in somewhere between two nodes there exists an authentic boundary of the object. Certainly, the more number of sensors deployed in the whole area of network, the more precise localization of the object will be achieved. Nevertheless, it

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still does not mean that awareness of all neighbors from each RN create a better shape. Secondly, we claim that the report data to the sink in our algorithm can be lighter than COBOM when we assume that each node’s ID is not more than 1 byte and node density is high. There are numerous research that achieves ID assignments to each node with a few bits only in wireless sensor networks [10], [11]. In our architecture, each RN transmits its own ID and reading and a neighbor’s ID whereas in COBOM, the report message consists of a RN’s ID, its reading and BN-array. The size of the BN-array can vary, especially when the sensors are densely deployed, it will be heavy as the number of neighbors for each sensor is high. The report message size estimation is performed in Sect. 6.3. 5.

Performance Analysis

To gain some intuition about how many number of BNs and RNs will be selected and in which circumstances our proposed idea surpasses COBOM [2] in energy efficiency, we analyze the performance of the proposed statistical scheme. For our analysis, we assume that nodes are placed at locations drawn from a uniform density function with density ρ sensors per unit area. Also, for simplicity we consider an object’s shape a perfect circle and it continuously changes its size varying its radius. The object with unsmoothed boundary will be discussed in Sect. 6 with simulation data. The expected number of nodes, E[r] around a sensor, i.e., within its communication radius r can be defined as: E[r] = πr2 ρ

(2)

Now we compare the expected number of BNs and RNs in COBOM and our proposed algorithm. A BN range, i.e., an area that BNs can be possibly located in is drawn within a circular object as depicted in Fig. 5 where R represents the object’s radius. According to COBOM, since all nodes

Fig. 5 Possible BN range in COBOM (a) and DEMOCO (b), (c) given that an object (red circle) is a perfect circle.

closely located in the boundary become BNs, i.e., BNs are located in both IN and OUT regions, the BN range is between R − r and R + r in a circle. Figure 5(a) illustrates the BN range in COBOM. On the other hand, the expected BN range in DEMOCO can be depicted as Figs. 5(b) or (c) where the range is either between R and R + r (when expanded) or R − r and R (when shrunk). Therefore, the expected number of BNs in COBOM and DEMOCO can be expressed as follows:   (3) ECOB [BN] = π (R + r)2 − (R − r)2 ρ = 4πrRρ   E DE MO1 [BN] = π (R + r)2 − R2 ρ = πr(2R + r)ρ (4)   E DE MO2 [BN] = π R2 − (R − r)2 ρ = πr(2R − r)ρ (5) where ECOB [BN] indicates the expected number of BNs in COBOM algorithm and E DE MO1 [BN] and E DE MO2 [BN] represent the expected number of BNs within range R and R + r (when expanded) and R − r and R (when shrunk) respectively in DEMOCO algorithm. Each RN will be selected among the BN nodes within BN range, occupying its communication range with radius r. Therefore, by dividing the BN range by the RN’s communication range, we can approximately estimate the expected number of RNs. The corresponding expected number of RNs to Eqs. (3), (4) and (5) are computed as follows: 4rπRρ 4R = r r2 πρ πr(2R + r)ρ 2R + r E DE MO1 [RN] = = r r2 πρ πr(2R − r)ρ 2R − r E DE MO2 [RN] = = r r2 πρ ECOB [RN] =

(6) (7) (8)

which are derived from dividing the expected number of BNs (from Eqs. (3) to (5)) by the expected number of RNs in (r2 πρ). Now, we can deduce in which circumstance the number of RNs in DEMOCO is expected to be less or more compared to in COBOM by simply comparing Eq. (6) with (7) and (8) since Eq. (6) indicates the expected number of RNs in COBOM and (7) and (8) represent the expected number of RNs in DEMOCO when the object expands and shrinks respectively. We can derive the following equations (left) and its result (right) is as follows: r 2R + r 4R ≥ , R≤ (9) r r 2 2R − r 4R r ≥ , R≤− (10) r r 2 Equation (9) shows that the number of RNs selected by COBOM algorithm will be less only when the radius of an object (R) is equal to or less than half of the radius of the communication range (r) when the object is expanding. Moreover, Eq. (10) states that our proposed algorithm always selects less RNs than COBOM when the object is shrinking. According to our performance analysis, therefore, we can conclude that our proposed algorithm usually excels COBOM in energy-efficiency except for only that the

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object is smaller than a half size of communication range of a typical sensor node when the object is expanding. 6.

Simulation Results

In this section, we evaluate the performance of DEMOCO based on simulation results. We developed a simulator using Java to evaluate and compare the performance of Dynamic Structure [1], the COBOM algorithm [2] and our proposed idea. Unlike COBOM algorithm, we do not consider static objects in this paper due to the limitation of page but we fully focus on moving objects only. Albeit our simulations do not concern possible data loss or contention between nodes and how to route data to the sink, the simulations will experientially assure that DEMOCO is usually more energy-efficient than previous work due to less number of BNs and RNs generated. Each simulation is run 1000 times and we assume the node ID is 1 byte. 6.1 Simulation Model In our simulation, as the number of BNs and RNs will be surely dependent on density of nodes deployed, we will vary the number of nodes while fixing the field size. Sensor nodes are distributed over a 500 × 500 m2 field. In each experiment, 1500 or 5000 sensor nodes are deployed arbitrarily in the field to simulate a sparse or dense setting. We set two different types of objects for the simulation: smoothed and unsmoothed objects. Firstly, for the smoothed object, a circle is initially centered at (250, 250) and it continually expands with increasing its radius by 1 meter in each time slot. The unsmoothed object will be expanded from the bottom left-hand corner and it grows in the shape of stairs. Figure 6 may help understand how it grows. According to Fig. 1(a), the black straight line with a shape of stairs is a boundary of an object at present and the dotted line is the boundary of the object at previous periods (Each period is 3 time slots and at every 3 time slots representative node selection process happens). Figures 1(b) and (c) shows every normal node, nodes in the In region, BNs and RNs in COBOM and DEMOCO respectively, which are drawn by simulating the algorithms. In both environments, the objects will keep growing during 75 time slots and all sensors in the network activate and make local observations every 3 time slots. Communication range for each sensor is set to 25 meters when simulating the smoothed object, 15 meters or 25 meters when simulating the unsmoothed object.

Fig. 6

The unsmoothed object’s shape and corresponding BNs and RNs.

Fig. 7

Comparison of the number of BNs (Smoothed).

6.2 Comparison of the Number of BNs and RNs In this part, we compare different number of BNs and RNs selected during simulation in different environments varying the density and communication range of nodes. 6.2.1 Smoothed Object We compare the number of BNs and RNs with a growing

circular object in this part. Figure 7 compares the number of BNs that are directly or indirectly involved in transmitting data to the sink. According to Figs. 7(a) and (b), it is obvious that COBOM [2] generates many more number of BNs than Dynamic Structure [1] and our proposed algorithm (DEMOCO) in any situation (i.e., whether sparsely or densely deployed) since any node that receives different detection status from its neighbors becomes BNs in COBOM,

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Fig. 8

Fig. 9

Comparison of the number of BNs (Unsmoothed).

Fig. 10

Comparison of the number of RNs (Unsmoothed).

Comparison of the number of RNs (Smoothed).

i.e., the nodes near the boundary of the object in both IN and OUT regions whereas in Dynamic Structure and DEMOCO, BNs are those near the boundary of the object in either IN or OUT region. DEMOCO produces slightly more number of BNs than Dynamic Structure because, in case the object expands (not shrinking or only a part of the object changes), nodes that are outside of the object will be determined as BNs in our approach whereas for Dynamic Structure, no matter the object shrinks or expands, it will get BNs from inside the object. However, from the fact that 1) each CVN in Dynamic Structure broadcasts a request first and nodes that receive the request messages need to reply to the request (i.e., twoway communication) and 2) All BNs selected are eventually necessitated to be participated in transmitting data to either their cluster heads or the sink and thus, it is unreasonable to allege that less number of BNs in Dynamic Structure saves more energy than in DEMOCO. In Fig. 8, we compare the number of RNs selected in COBOM and DEMOCO. Since Dynamic Structure does not select RNs, we do not take it into consideration. As apparently illustrated, difference of the number of RNs becomes larger as the object’s radius increases. As the event region that BNs and RNs should cover gets larger, it is natural that the number RNs involved in detection also increases in both algorithms. However, the RNs in COBOM should cover both IN and OUT regions whereas only the nodes that are either IN or OUT region can become the RNs in our proposed architecture and thus, the number of RNs in DEMOCO slowly increases compared to that of COBOM. This status quo becomes more apparent where the density of nodes deployed is high. As seen in Fig. 8(b), when the object’s radius becomes 75 meters, the difference of the number of RNs becomes more than doubled. Another interesting feature we can observe from the diagrams is that less number of RNs is elected in COBOM when the object is small in both Figs. 8(a) and (b). This occurrence can be clarified by the analysis performed in Sect. 5 where it is deduced that our algorithm is superior to COBOM only when the radius of the object is longer than a half of that of a node’s communication range given that the object is generically expanding.

6.2.2 Unsmoothed Object Comparison of the number of BNs and RNs with unsmoothed object and the corresponding analysis is discussed in this part. Note that one period is 3 time slots. According to Fig. 9, it is easy to find that the result is not quite different from Sect. 6.2.1, i.e., the number of BNs in COBOM is obviously more than that of DEMOCO especially when the density of the nodes is high. Moreover, the higher communication range is assigned, the more number of boundary nodes are found. It is easy to guess that if communication range of a sensor is large, there will be many neighbor nodes that it can communicate with, which will result in more BNs. The number of RNs generated is also as expected. More RNs are found in COBOM according to Figs. 10(a) and (b). The most interesting feature is that, for sparse setting, communication range with 25 meters allows to yield more RNs whereas the communication range 15 meters in dense setting builds more number of RNs. As a result, it can be possibly alleged that there is no strong relationship between the number of RNs and the communication range. Notice that in the 25th period, the number of BNs and RNs decreases. This is due to the object is too large such that the object expands up to where the nodes are not able to cover. Through the observation and analysis from Sect. 6.2, it is apparent that our algorithm, DEMOCO, can generally reduce more traffic between the RNs and the sink as well as the communication between the RNs and their neighbors. 6.3 Comparison of Average Report Data Size In this part, we compare average report data size with mod-

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Average report data size.

the total values. In this way, we can know which method is more effective for reducing report data size. It is shown in Table 1 that as the density of nodes in the network increases, our proposed architecture produces less report data size overall. However, it is also clearly demonstrated that when the density is low, total average report message is heavier than that of COBOM but still as close as COBOM. We hereby conclude that our algorithm for reducing the size of report data is effective especially when the density of deployed nodes is high. 7.

eling the COBOM algorithm. Before we start examining and comparing the report data, note that parameters determined are set for simple computation and the result is performed with the smoothed object only. We assume that each RN sends its own ID (1 byte), reading (1 bit) and only one neighbor’s ID (1 byte) in our algorithm, which means each RN always sends 3 bytes long report message to the sink. For COBOM, each report message size can vary since the message contains a RN’s ID (1 byte), the RN’s own reading (1 bit) and a BN-array. Because the size of the BN-array depends on the number of neighbors within the RN’s communication range, the report message size also varies. Therefore, the higher density of nodes deployed, the longer report messages would be required for RNs in COBOM. In contrast, DEMOCO only requires 3 bytes by all means. Each figure in Table 1 shows an average value of bytes sent from all RNs to the sink in each period. The figure is computed as the following Eq. (11): ψ=

n 

i · μi

(11)

i=2

where i and μ represent report data size, i.e., the number of bytes of the report data and average number of RNs that corresponds to the i respectively. ψ is, hence, average report data size in a period, which is each figure in Table 1. After the computation of the average report data size for each period, we simply add up all the computed values and compare

Conclusion

This paper proposes the DEMOCO algorithm for boundary detection and monitoring of continuously moving phenomena in wireless sensor networks. The algorithm selects only a few representative nodes (RNs), which transmit report data to the sink among a small subset of boundary nodes (BNs). This will result in reducing traffic between the RNs and the sink node. Furthermore, by sending only one neighbor node’s ID, which is possibly the closest node among its neighbors that have different current reading, we have verified that the report message size generated by RNs can be smaller than the previous work, especially when the density of the deployed nodes is high. We also believe that the expected shape of the object can be as precise as the ones demonstrated in previous work. In Sects. 5 and 6, we presented performance analysis and simulation results that support our allegation. The results show that our proposed algorithm greatly outperforms the previous work as demonstrated in the simulations in terms of energy-efficiency. Our future work will include verification of precision of expected boundary and invention of a new algorithm that considers residual energy of each node as well as destructive nodes or failures of connection in targeted application. References [1] X. Ji, H. Zha, J.J. Metzner, and G. Kesidis, “Dynamic cluster structure for object detection and tracking in wireless ad-hoc sensor networks,” Proc. IEEE ICC, 2004. [2] C. Zhong and M. Worboys, “Energy-efficient continuous boundary monitoring in sensor networks,” Technical Report, 2007. Available: http://ilab1.korea.ac.kr/papers/ref2.pdf [3] P.-K. Liao, M.-K. Chang, and C.-C.J. Kuo, “Distributed edge detection with composite hypothesis test in wireless sensor networks,” Proc. IEEE, Communication Society Globecom, 2004. [4] K. Chintalapudi and R. Govindan, “Localized edge detection in sensor fields,” Proc. Ad-hoc Networks Journal, pp.273–291, 2003. [5] US Naval Observatory (USNO) GPS Operations, 2001 [Online]. Available: http://tycho.usno.navy.mil/gps.html [6] N. Bulusu, J. Heidemann, and D. Estrin, “GPS-less low cost outdoor location for very small devices,” Proc. IEEE Pers. Commun. (Special Issue on Smart Space and Environments), vol.7, pp.28–34, 2000. [7] J. Singh, U. Madhow, R. Kumar, S. Suri, and R. Cagley, “Tracking multiple targets using binary proximity sensors,” Proc. IPSN, 2007. [8] J. Jeong, T. Hwang, T. He, and D. Du, “MCTA: Target tracking algorithm based on minimal contour in wireless sensor networks,” Proc. IEEE INFOCOM, 2007. [9] J. Liu, P. Cheung, L. Guonidas, and F. Zho, “A dual-space approach to tracking and sensor management in wireless sensor networks,”

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Proc. ACM International Workshop on Wireless Sensor Networks and Applications Workshop, 2002. [10] J.H. Kang and M.-S. Park, “Structure-based ID assignment for sensor networks,” Proc. IJCSNS International Journal of Computer Science and Network Security, vol.6, no.7, 2006. [11] E.M. Oud-Amed-Vall, D.M. Blough, B.S. Heck, and G.F. Riley, “Distributed unique global ID assignment for sensor networks,” Proc. IEEE International Conference on Mobile Ad-Hoc and Sensor System (MASS), 2005.

Jung-Hwan Kim received the B.S.C. degree in Computer Science from The University of Auckland, New Zealand, in 2005 and is currently pursuing Master degree in computer science and engineering from Korea University, Seoul, Korea. His research interests include clustering, continuous object tracking in wireless sensor networks.

Kee-Bum Kim received the B.S. degree in Computer Science and Engineering from Inha University, Incheon, Korea in 2007, and he is currently a M.S. student in Korea University, Seoul, Korea. In 2000, he was a developer at Icommunity21 corp., to construct and manage web servers. His research interests include security authentication in wireless sensor network.

Sajjad Hussain Chauhdary received the B.S.C. degree in Computer Science from The University of Management and Technology, Pakistan, in 2003 and M.S. degree in electronics and electrical engineering from Ajou University, Korea. He is currently pursuing Ph.D. in computer science and engineering from Korea University, Korea. His research interests include object trakcing and 6LowPan in wireless sensor network.

Wencheng Yang received the B.S.C. degree in Computer Science from Wuhan University of Technology, China, in 2006 and is currently pursuing Master degree in computer science and engineering from Korea University, Seoul, Korea. His research interests include object tracking and security authentication in wireless sensor network.

Myong-Soon Park received B.S. in Electronics Engineering from Seoul National University, M.S. in Electrical Engineering from University of Utah in 1982, and Ph.D. in Electrical and Computer Engineering from University of Iowa, 1985. During 1985–1987, he was an assistant professor of Electrical Engineering and Computer Science at Marquette University. During 1988–1989, he was an assistant professor of Computer Science, Electronic and Electrical Engineering at POSTECH. He is now a professor of Computer Science and Engineering at Korea University. He was a chair of the SIG on parallel processing of KISS from 1997 to 2000. He is serving on the program committees of many international conferences. His research interest includes parallel processing, cluster computing, network storage, and mobile computing.