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International Journal of Distributed Sensor Networks
Volume 2012 (2012), Article ID 831427, 7 pages
A Distributed Algorithm for Virtual Backbone Construction with Cellular Structure in WSNs
School of Computer Science and Engineering, University of Electronic Science and Technology of China, XiYuan Road 2006 No., GaoXin District (West District), ChengDu 611731, China
Received 27 June 2011; Revised 14 November 2011; Accepted 24 November 2011
Academic Editor: Weifa Liang
Copyright © 2012 Yu Xiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
The normal node in wireless sensor networks (WSNs) often has no constant energy supply. To make efficient use of limited energy resources significant, a major way to save limited energy resources is constructing a smaller virtual backbone in WSNs. In this paper, we propose a distributed algorithm for virtual backbone construction with cellular structure in WSNs (DBC), which is an improvement of algorithm ABP (Duresi and Paruchuri, 2008) and minimize the distortion of constructed cellular structure by improving the way of calculating strategic points using 1-hop neighbor information. Our simulation results show that algorithm DBC has smaller virtual backbone than algorithm ABP, and the number of backbone nodes is much fewer than that one.
The energy supply of nodes in wireless sensor networks (WSNs) is normally battery powered. As battery energy is limited and battery replacement or recharging is usually impractical for WSNs, it is significant to reduce energy consumption by redundancy transmission to prolong network working hours. Constructing virtual backbone is one of major ways to save limited network resources and optimize network performance [1, 2]. A smaller virtual backbone will not only benefit the design of energy-efficient routing, but also save energy of nondominated nodes which can enter sleep (energy-saving) mode without monitoring task.
The first method is constructing a virtual backbone by calculating the minimum connected dominating sets (MCDS). As calculating MCDS of arbitrary graphs is a NP-complete problem, heuristic algorithms are usually used. Algorithm MISB  and algorithm OHDC  are two typical distributed algorithms. Algorithm MISB constructs virtual backbone by calculating MCDS on the basis of maximum independent set. The number of backbone nodes calculated from algorithm MISB is fluctuant according to the node density or scale. As network size is increasing, the backbone nodes also increase their redundancy. Furthermore, algorithm MISB needs two hops neighbor information to calculate MCDS. As a result, more messages are exchanged, which means more energy consumption and more time delay. Algorithm OHDC also constructs virtual backbone by calculating MCDS. Comparing with algorithm MISB, algorithm OHDC uses only 1-hop neighbor information; however, the backbone nodes’ redundancy is higher. Especially when the network size is large or the density of nodes is high, the redundancy will seriously affect the WSN performance.
The second method is constructing a virtual backbone by generating a hierarchy topology of entire network. Paruchuri et al. [5, 7, 8] proposed algorithms based on the theory that hexagonal lattice arrangement is the most efficient way for circles covering a plane surface. These algorithms use delay mechanism to select backbone nodes without any neighborhood knowledge. However, the cellular structure constructed during the process of selecting backbone nodes is distorted seriously which results in the situation that the algorithm can fail to deliver all the recipients connected to the source, even for highly connected networks. The representative of these algorithms is ABP .
In this paper, a distributed algorithm, named virtual backbone construction with cellular structure in WSNs (DBC), is proposed, which is an improved algorithm of algorithm ABP. The algorithm minimizes the distortion of constructed cellular structure and has better reliability and lesser backbone nodes by improving the method of selecting backbone nodes and the way of constructing cellular structure. Simulation results show that the number of virtual backbone nodes calculated by algorithm DBC is much fewer than those of original algorithms and almost constant for the same network areas with different node density. The results also show that DBC is fit for large scale WSNs.
The rest of this paper is organized as follows. Section 2 describes terms used in this paper. Section 3 analyses the problem and gives the solutions. Section 4 presents details of our algorithm. Section 5 presents performance evaluation. Section 6 concludes the paper.
2. Terms Description
Terms used in this paper are shown in Table 1. We assume that the transmission radius of each node is , and all nodes are randomly deployed in a 2-dimensional geographical region.
3. Problem Statement
Kershner  has proved that hexagonal lattice arrangement is the most efficient arrangement of circles covering the plane. There are two ways to construct the virtual backbone based on the hexagonal lattice (cellular structure), one is the distributed method, where locations of strategy points depend on locations of local selected backbone nodes; another is the centralized method, where location of each strategy point is fixed and cellular structure is determined.
3.1. Distributed Method
As shown in Figure 1, BN is SP ’s RN, and BN is SP ’s, besides BN is BN ’s last hop BN. With the line between BN and BN as reference edge of regular hexagons, calculate SP and SP , so that edge and edge both satisfy that its length is equal to the side of regular hexagon and it forms an 120 degree angle with reference edge . Then select node which is closest to SP as its RN. In the same way, SP and SP can be selected. And keep on executing these processes until strategy points and backbone nodes of the entire network are selected.
Algorithm ABP adopts this distributed method. Source node locates at the centre of certain regular hexagon. It starts the process of broadcasting messages for constructing virtual backbone. The message contains locations of the current sender and last sender (for source node , the two locations are same). After nearby nodes received the message, they will calculate the distance to the strategic point from position information included in the message and then set a time delay which varies with the distance, so the node closest to certain strategy point should become backbone node and broadcast the message with locations of current sender and last sender. Algorithm ABP is a distributed algorithm; it is easier to achieve this distributed method by setting time delay. However, as Figure 1 shows obviously, the cellular structure constructed in this way is not uniformed as expected, as it is impossible that every strategy point has a node in realistic networks. What is more, the distortion is magnified gradually using distributed method, and the structure will get more twisted as closer to the boundary of the network. As a result, the communication coverage of the obtained virtual backbone is not unreliable, and there might be a lot of nodes which cannot connect any backbone node within 1-hop.
3.2. Centralized Method
Another is the centralized method, which means that locations of all strategy points are fixed and not affected by the positions of nodes, as long as any edge of certain regular hexagon is determinated. As shown in Figure 2, the initiator is node whose position is the first strategy point and chose its 1-hop neighbor node which is farthest to node as the second backbone node. Then utilizing similar triangle theory shown in Figure 3, calculate SP by (1), in which is the location of BN , and is BN ’s, is SP ’s, and is the distance between BN and BN
As the positions of SP and SP are determinated, according to the cellular structure, the locations of all the other strategy points in the network are fixed. As Figure 2 shows, vertices , , , and form a geometrical unit of which three edges form an 120 degree angle with each other, then calculate locations of all strategy points based on this geometrical unit. Utilizing cosine law shown in Figure 3, calculate SP and SP by (2) with known locations of SP and SP
Then select RN for SP and RN for SP in 1-hop neighbor nodes of BN by (3), in which means the distance between node and certain SP and means the set of BN ’s all 1-hop neighbor nodes. Calculate locations of SP and SP and select RNs, respectively, from 1-hop neighbor nodes of BN in the same way. And then locations of SPs , , , and can be obtained by locations of SPs , , and , and their RNs will be chosen from 1-hop neighbor nodes of BN and BN . Execute this process along directions shown in Figure 2; locations and RNs of all RPs in the entire network are selected
Algorithm DBC adopts this centralized method, after initiator and second BN were determinate; locations of all the other SPs are determinated according to the cellular structure. What is more, each time we select a new BN, the distortion of constructed cellular structure will be amended as far as possible, so the communication coverage of obtained virtual backbone will be closest to the optimal coverage of ideal cellular structure, and the size of obtained virtual backbone is smaller than that of virtual backbone obtained from the distributed method. It is obvious that obtained virtual backbone will be closer to ideal case with higher node density, no matter through which method. That is why algorithm DBC has a better performance in the network with a higher node density.
4. Algorithm DBC
Assume that network area is a rectangle, and the initiator is at the center of the rectangle. Algorithm DBC comprises initiator subalgorithm described in Section 4.2 and backbone node subalgorithm described in Section 4.3.
4.1. Network Model and Assumptions
In this section, description about the network model and assumptions used in our algorithm DBC are provided.(1)The initiator is at the center of the network area, and other nodes are randomly deployed in a two-dimensional geographical region.(2)Communication range: node is the center of a circle with radius , and is also the communication range.(3)All sensor nodes are homogeneous in terms of energy, communication, and processing capabilities.(4)All sensor nodes are location aware; that is, they are equipped with a GPS device.(5)All sensor nodes learn their 1-hop neighbor information including IDs and locations by neighbor discovery.
4.2. Initiator Subalgorithm(1)The initiator sets its position as the first strategy point , then chooses its 1-hop neighbor which is the farthest to node as a candidate, and then calculates SSP (node ’s RP) by (1). After that, initiator sends candidate notice (CN) message containing locations of SP and SP to candidate .(2)Calculate SSP and SSP by (2).(3)Select candidate for SP and candidate for SP within 1-hop neighbors of BN by (3), which ensures the closest node to certain RP can be selected as far as possible. Then initiator sends CN message containing locations of SP and SP or SP to candidate or candidate .(4)End.
4.3. Backbone Node Subalgorithm(1)After received CN message, Candidate sends messages to to check out if any 1-hop neighbor backbone node is within the distance (always be set to R), which is to minish the redundancy of the virtual backbone. If it doesnot get any acknowledgement from neighbor node in time of , the delay time for waiting the acknowledgement, then candidate identifies itself BN and notices its neighbor nodes. Otherwise, go to 5.(2)BN calculates locations of SSP and SSP, that is, and by (4) means the location of’s RP, and means the location of’s FSP(3)The SSP of which coordinates satisfy requirement shown in (5) is a reasonable SP. If the number of reasonable SPs is 0, go to 5 BN selects candidates for its reasonable SP (or SPs) from according to (6), then sends CN message containing locations of RP and certain reasonable SP to corresponding candidate. After that, BN notices last candidate to be new and go on executing backbone node subalgorithm (4)End.
Variable in (5) is the length that algorithm DBC extends the boundary of cellular structure area, for it is impossible for nodes to exist on all strategy points in realistic networks. What’s more, the virtual backbone network will be more misaligned for the cellular structure when closer to the boundary of the network, so the virtual backbone network shrinks toward the initiator, and it is possible that nodes which exist at boundary of the network cannot connect any backbone node with 1-hop. The reason of extending the cellular structure area is to increase the number of backbone nodes on the network boundary and amend the communication coverage of the obtained virtual backbone.
4.4. Complexity Analysis
4.4.1. The Complexity of Initiator Subalgorithm
The complexity of initiator subalgorithm is mainly caused by the following two parts. (1) Sort 1-hop neighbor vertices by the distance to initiator, and then choose the second backbone node. (2) Sort 1-hop neighbor vertices of initiator by the distance to certain strategy point in order to find out its RN.
Suppose indicates the degree of the node. If we adopt Bubble Sort, the complexity of both parts is , so the complexity of initiator subalgorithm is .
4.4.2. The Complexity of Backbone Node SubAlgorithm
The complexity of backbone node subalgorithm is mainly caused by the following two parts. (1) Candidate sends messages to to check out if it has any 1-hop neighbor backbone node within the distance . (2) Sort the 1-hop neighbor vertices of by the distance to certain strategy point in order to find out its RN.
Suppose that indicates the degree of the node. The complexity of first part is , and we adopt Bubble Sort in the second part; its complexity is . So the complexity of backbone node sub-algorithm is .
Algorithm ABP constructs virtual backbone by broadcasting and delaying, which makes the algorithm easy to execute; however, in the other hand, to obtain virtual backbone is restricted to time delay and positions of nodes in the realistic networks. Simulation results show that the virtual backbone obtained by algorithm ABP is larger and lacks reliability.
5.1. Simulation Parameter Setting
To compare the performance of four different algorithms, algorithm DBC, algorithm MISB, algorithm ABP, and algorithm OHDC, simulations have been made in 100 different topologies. Figure 6 shows the average number of backbone nodes of 100 topologies with different network scale. The topologies of the WSNs are generated according to the rules as follows.(1)Nodes disperse randomly in a 1800 m × 1800 m square area.(2)The transmission range of each node is 240 m. (3)The size of network changes from 300 nodes to 1800 nodes with increment of 150 nodes, respectively.(4)The final result is the average of testing with 100 different topologies.
In the simulations, the parameters of algorithm DBC are as follow: , . It means that the length of algorithm DBC which extends the boundary of cellular structure area is , and the distance between certain SP and its RN must be limited in .
5.2. Simulation Results and Analysis
Figure 4 shows the backbone networks of algorithm DBC and algorithm ABP, respectively, as there are 900 nodes. Similarly, when the number of nodes is 1800, the backbone networks are shown in Figure 5. In each figure, every point represents a WSN node, the thin lines represent the edges of cellular structure in optimal coverage, and the heavy lines represent the sides of backbone of algorithm DBC or ABP. The ID of initial node is 26, which lies in the centre of the square area. We choose the best value of ABP provided in literature . It is shown obviously in Figure 4 that the backbone of DBC is much fewer than that of ABP, and it has been proved that the cellular structure constructed is distorted increasingly from the centre to the boundary. And it is also shown that the backbone is increasingly similar to the cellular structure while the node density is increasing. So we can come to a conclusion that algorithm DBC has a good performance in high-density WSNs.
It is shown in Figure 6 that the amount of backbone nodes chosen by the two algorithms, algorithm ABP and algorithm DBC, based on cellular structure coverage are both stable and have little changes along with the size of WSNs. The simulation results indicate that the cellular structure coverage is the optimal solution for 2D covering problem as well. In spite of the network size changing, the amount of backbone nodes chosen by algorithm DBC is about 50 less than that gotten by algorithm ABP. It indicates that algorithm DBC has better performance when constructing low redundancy backbone network than algorithm ABP. When the network size is less than 900 nodes, the backbone network size of algorithm MISB is as little as algorithm DBC, but increases fast while the network size increases. However, the virtual backbone obtained by algorithm OHDC has high redundancy, and its size almost increases linearly along with the network size increasing.
In summary, the backbone network constructed by algorithm DBC has advantages for its low redundancy, small size, and little influence while network’s size is changing.
In this paper, we proposed an algorithm DBC which is an improvement of algorithm ABP. Algorithm DBC is based on one-hop neighbor information and minimizes the distortion of constructed cellular structure by improving the method of selecting backbone nodes and the way of constructing cellular structure. The simulation results show that algorithm DBC has smallest virtual backbone, and the amount of backbone nodes is the fewest among the four algorithms, which almost doesnot change along with the variation of node density. Therefore, the superiority of algorithm DBC will show more obviously when the node density is high. It can benefit energy saving and reducing redundant forward nodes by constructing backbone route based on algorithm DBC.
This paper is supported by Project CNGI (no. CNGI-09-01-07) of National Development and Reform Committee (NDRC), research fund for the doctoral program of higher education of China (no. 200806141110), Project 863 (no. 2008AA01A303), and Project 973 (no. 2009CB320505).
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