Consecutive Geographic Multicasting Protocol in Large-Scale Wireless Sensor Networks

Transcription

1 21st Annual IEEE International Symposium on Personal, Indoor and Mobile Radio Communications Consecutive Geographic Multicasting Protocol in Large-Scale Wireless Sensor Networks Jeongcheol Lee, Euisin Lee, Soochang Park, Seungmin Oh, and Sang-Ha Kim Department of Computer Engineering Chungnam National University 22 Gung-dong, Yuseong-gu, Daejeon, , Republic of Korea {jclee, eslee, winter, and Abstract For multicast applications with large-scale groups in large-scale wireless sensor networks, it is an important issue for energy saving in geographic multicasting both to obtain location information of destination nodes and to construct the geographic multicast tree efficiently. To address this issue, there have been proposed global search based hierarchical geographic multicasting protocols. Such protocols divide the whole network into many multiple small areas and a leader node in each area manages the location information of destination nodes in its area. However, since they find location information of such leader nodes through global location search at a source, they consume much energy. Also, since they exploit hierarchical geographic multicast trees that have a higher tree consisting of the source node and the leader nodes and a lower tree consisting of a leader node and destination nodes in the area of the leader node, they consume more energy due to data detouring. To solve the problems of global search based hierarchical geographic multicasting protocols, we propose a local search based consecutive geographic multicasting scheme. Through avoiding the global search and removing the data detouring, the proposed protocol saves much energy of sensor nodes. Simulation results show that the proposed protocol is superior to the existing protocols. Index Terms local search, consecutive multicasting, largescale, wireless sensor networks I. INTRODUCTION In wireless sensor networks, geographic multicasting routing is considered as attractive approach because it can route data from a source node to multiple destination nodes through only location information instead of topology information of the network. Such geographic multicasting routing needs two requirements. One is that the source node obtains the location information of all destination nodes and the other one is that the source node constructs a multicast tree through the obtained location information. If the sensor network has a large-scale multicast group which has a number of destination nodes, it needs much energy consumption to achieve the two requirements. However, since the sensor network consists of sensor nodes with constrained energy, both obtaining the location information of destination nodes and constructing the multicast tree energy-efficiently is an important issue to save the energy of sensor nodes and thus to enhance the network lifetime of sensor network [1]. For multicast applications with large-scale groups in largescale wireless sensor networks, there have been recently proposed protocols [2-4] to reduce the energy consumption for obtaining location information of destination nodes and to efficiently construct the multicast tree. Such protocols divide the whole network into many multiple small areas and a leader node in each area manages the location information of destination nodes in its area. A source node collects the location information of leader nodes that has destination nodes in their own area, through global location search, and constructs a higher geographic multicast tree which is rooted at its location and is spanning all of their locations. Each leader node constructs a lower geographic multicasting tree which is rooted at its location and is spanning locations of destination nodes that belong to its area. In other words, the protocols use global search based hierarchical geographic multicasting tree construction approach. Nevertheless, such global search based hierarchical multicasting approach still remains the following problems. Firstly, as shown in Fig. 1, since a source uses global location search to get the location information of leader nodes in the areas that have destination nodes, such protocols excessively pay for the cost for the location service. Secondly, as shown in Fig. 2, since such protocols uses hierarchical geographic multicasting trees consisting a higher tree and a lower tree, multicast data from the source node to destinations detour due to the hierarchical characteristic of geographic multicasting tree. Moreover, if there are multiple n-tier hierarchies because of the increase of network size, the data detour has longer paths. Lastly, since leader nodes in such protocols provide the location service of destination nodes to a source node and the data delivery service from the source node to destination nodes, they consume energy excessively. In order to solve the problems of the global search based hierarchical geographic multicasting protocols, we propose a local search based consecutive geographic multicasting protocol. In the proposed protocol, a source node obtains the location information of destination nodes in its own area from the leader node of the area; then, the source node constructs a multicast tree that is spanning all the destination nodes. After that, some nodes that are involved in the source-based tree in the area and located edges of the area obtain the location /1/$ IEEE 2192

2 Grid Header Node Request & Reply Source Source-header tree (High-tier tree) Detour occurs! Grid Header Node Header-destination tree (Low-tier tree) Destination Figure 2. Source Destination A data detour problem in the previous hierarchical approach. Figure An example of global search based hierarchical multicast. information of destination nodes from leader nodes in their neighborhood areas in order to progressively extend the multicast tree to the neighborhood areas. Finally, through the local search per an area, the multicast tree would be globally established. Since the proposed utilizes the local search to obtain location information of all destination nodes in the whole sensor network, the proposed protocol may not consume energy in proportion to increase of the network size, i.e. the protocol can provide scalability in terms of the network size. In addition, because the multicast tree is progressively constructed on the whole sensor network, the proposed protocol could solve the detour problem of the global search based hierarchical geographic multicasting protocols. Simulation results show that the proposed protocol is more efficient than the global search based hierarchical geographic multicasting protocols in terms of the energy consumption and the data delivery hop counts. The remainder of the paper is organized as follows. Section II presents the related work. Proposed protocol is presented in Section III. Section IV presents the performance evaluation of the proposed protocol. Section V concludes this paper. II. RELATED WORK There have been proposed protocols [5-7] for supporting efficiently multicasting through only position information without the topology information of a whole sensor field in wireless sensor networks. GMR [5] selects the next hop neighbor nodes which have the shortest distance from a current node to destinations with localized calculation per each transmitting node while a multicast data of a source is forwarding to destinations. Therefore, a data packet should include a multicast data from a source as well as location information of all destinations. GMP [6] processes an effective multicast data delivery with heuristic Steiner method from a source to multiple destinations. Since a whole multicast tree is calculated in advance by a source, a data packet includes a multicast data as well as this tree information. SEAD [7] constructs an energy-efficient multicast tree, D-tree, through replica nodes and a source. A source attempts the Steiner method to construct multicast tree, and replicas manage information of sinks through access nodes. However, they are not scalable to large groups because of the packet encoding overhead and a large number of location services of multicast members. Recently, several multicast protocols for large-scale sensor networks, e.g. [2] and [3], are proposed by the requests. tries to enable lightweight membership management, which reduces the packet encoding overhead by using distributed geographic hashing and hierarchical decomposition of large multicast groups. When a grid header has one or more multicast members, the grid header sends its position information to a node which is closed to the pre-defined rendezvous point, and a source constructs data delivery paths between the source and grid headers through periodical message exchanges with the rendezvous node. A grid header which receives sources data constructs a local data delivery path between its members and itself, so it could effectively deliver the sources data to the multicast members. tries to enable efficient multicast data delivery to sinks by using grid-based shared tree and hierarchical location servers. A sink updates its location information to a low-tier location server, and then it is forwarded to a high-tier location server. The source constructs an efficient multicast tree through information exchanges with these location servers. also considers a multi-source scenario, and it thus could share an entire multicast tree between multiple sources. However, all of them fail to reduce the costs that are required for member location services. Also, these grid-level tree constructions lead to the inevitable inefficiencies of the multicast tree. Moreover, the problems become more serious with respect to the large-scale networks. As a result, a new type of scalable multicast protocol is required for large-scale wireless sensor networks. III. PROPOSED PROTOCOL In this section, we propose a local search based Consecutive 2193

3 P b Grid Header Node Destination Source Figure 3. The multicast tree construction in the proposed scheme. Geographic Multicasting (CGM) scheme. To design efficient multicast protocol for large-scale wireless sensor networks, our goals are 1) to minimize the searching costs for location acquisitions of multicast members, and 2) to avoid the data detour problem, therefore it enables to delivery the source s data with shorter paths. A. Network Initialization and Registration of Multicast Member We divide an entire network into multiple virtual grids for efficient management of large-scale WSNs which have a number of sensor nodes and multiple destinations. All sensor nodes are aware of own location information by GPS technologies [8] or other localization techniques [9], and grid structure (own cell belonged on grid structure) by the predefined rule through the following two parameters: the grid size α and the reference coordinate (x r, y r ). A sensor node which is the closest from the center point of a grid is selected as a grid header by local signaling within the grid such as the local flooding. After the grid header selection, each multicast member registers own location to own grid header. In the previous studies, leader nodes do not only work as location servers that notify registered location information of multicast members to other members or a parent leader, but also is used to data delivery paths. By contrast, in the proposed scheme, grid headers only work as location servers. This distributed method which distinguishes data delivery path and location service path could solve the packet converging problem, so it has an advantage that it effectively reduces the overhead of grid header. B. Local Multicast Tree Construction Given a multicast message generated by a source node, the source node requests location information of multicast members to its grid header as shown in Fig. 3. It then constructs the local multicast tree with location information from the grid header by the Steiner method such as the TM P a Request P c heuristic [1]. In other words, at first, the multicast tree is constructed within the local grid where the source node is located; that is, the source of CGM does not make an effort to send its data to other grids. For example, the source in Fig. 3 requests location of multicast members to its grid header in the grid 11, then it constructs the local multicast tree. C. Consecutive Multicast Tree Construction In this section, we explain a tree expansion algorithm for delivering the multicast data to all multicast members. Through the local member acquisition above mentioned, the source node selects a member where is the closest to neighbor grid as a proxy member node. The source node can also select multiple proxy member nodes, or a proxy member node which is the closest to multiple girds according to where the grid of the source is located in the entire network. For example, as shown in Fig. 3, P a is selected for the grid 14 and 15, P b is selected for the grid 6, and P c is selected for the grid 7 as proxy member nodes, of all multicast members in the grid 11 by the source. A proxy member node requests member location to its target grid headers, constructs its local multicast tree, and chooses proxy member nodes for next target grids. Since the performance of the proposed scheme is affected by the target grid selection, we explain it in the next section. Note that the proxy member node works like the source node. However, the source node obtains location information of the member nodes from its grid header only, while the proxy member node obtains the information from adjacent target grid headers. At first, the source puts pairs of location information of a picked proxy member node and its target grid headers into the header of multicast data packet. When a member node receives the data packet, it compares between location information in the data packet header and its own location information, so that the member node could knows whether it is the proxy member node or not. If the node is selected as the proxy member node, the node stores location information of its target grid headers and it then works as the proxy member node above mentioned. These consecutive local searches and local tree constructions continue until global location service and global tree construction are completed. However, the consecutive process might be interrupted when they meet a non-member grid. Therefore, the grid header of this grid relays a received location query to its next target grid. As shown in Fig. 3, the proxy member node P c sends a location query to the grid header in the grid 7. Since the grid 7 is nonmember grid, the grid header relays to the next target grid, the grid 3. The grid header in the grid 3 sends its member location to P c. P c therefore could construct the local multicast tree using this member location information. D. Target Grids Selection In the proposed scheme, both a source and a proxy member node construct a local multicast tree using its own location information. A source node only request to its grid header, while a proxy member node could request to multiple adjacent 2194

4 A B C Figure 4. The target grid selection mechanism in the proposed scheme. grid headers. Here, the consideration we are facing is that: which grid headers are selected as target grids for the efficient expansion of multicast tree. If all grid headers in the network allow one-way local request, the costs for location search would be minimized. By contrast, if they allow multi-way local request, e. g., duplicated location requests from multiple proxy member nodes, the costs for location search would be increased, but the probability of optimal path selecting would be increased. To facilitate expression, the proposed scheme chooses the mechanism that allows one location request per grid. Basically, except a beginning grid where a source is located, the rest grids locally search with a regular direction. In detail, the beginning grid requests location services to all adjacent grids (maximum 8 grids), and the rest each grid requests location service to an adjacent grid that is located at the same direction from previous location query is sent. For example, as shown in Fig. 4, the grid B requests to the grid C because the grid B is received a location query from the grid A. However, there exists an exceptional case such as a grid which receives a diagonal query. In this case, the grid does not only request to next grid that is located at the same diagonal direction, but it also requests to two grids that is located at the straight line: horizontal and vertical. In other words, the grid sends three location queries. As shown in Fig. 4, the grid D requests to the grid E, F and G because the grid D is received a diagonal query from the beginning grid. These target grid selection mechanism enables local location services to all grids in the network without duplicated location requests. E. Multicast Member Management In this section, we describe the multicast member management in the proposed scheme. We classify it into the following four cases: joining a grid with existing other members, joining a grid without other members, leaving a grid with existing other members, and leaving a grid without other members. D G F E 1) Joining a grid with existing other members If a new multicast member appears in a grid where other members exist, the new member node gets the information of grid header from its neighbor nodes. It then registers its location information to the grid header. The grid header delivers information of the new member node to its parent proxy member node. The parent proxy member node reconstructs its local multicast tree after it receives the information. If a new member node is closer to target grids than a proxy member node in the grid, the proxy member node would be changed to the new member node. 2) Joining a grid without other members If a new multicast member appears in a grid where other members does not exist, the new member node also registers to its grid header, updates to the parent member node, and reconstructs the local multicast tree. However, against to the preceding case, the new member node or one of the other member nodes would become a child proxy member node, so that a local multicast tree is divided into two multicast trees with hierarchy when the local multicast tree is reconstructed by the parent proxy member node. 3) Leaving a grid existing other members When a member node wants to leave from a grid where other members exist, the member node notifies a leaving message to its grid header and a parent proxy member node. If the leaving member node is a proxy member node in the grid, the parent proxy member node selects a new child proxy member node, and then notifies location information of the new child proxy member node to the leaving member node. When the leaving member node receives this message, the leaving member node sends its local tree information such as location information of its child members to the new child proxy member node. These mechanisms enables to effective proxy member node replacement. 4) Leaving a grid without other members When only one member exists in a grid, if the member node wants to leave from the grid, it notifies a leaving message to its grid header. If the leaving member node only has a parent proxy member node, that is, the leaf of a local multicast tree, it sends the leaving message to the parent proxy member node. By contrast, if the leaving member node has both a parent and child proxy member nodes, the leaving member node sends its local tree information to the parent proxy members. Therefore, multicast data could be delivered, continuously. IV. PERFORMANCE EVALUATION In this section, we first describe the methodology and metrics of our study. We then present the simulation results. We evaluate the proposed scheme by comparing it with [2] and [3]. A. Methodology and Metrics We implement CGM in the Qualnet 4. network simulator [11]. The model of sensor nodes are followed by the specification of MICA2 [12]. The default simulation settings 2195

5 Total control overhead (packets) Total hop count Total control overhead (packets) Network Size(m 2 ) Network size (m 2 ) Grid size (m) (a) (b) (c) Total hop count Total control overhead (packets) way -3 way -1 way Total hop count way -3 way -1 way Grid size(m) Network size (m 2 ) Network size (m 2 ) (d) (e) (f) Figure 5. Simulation results. have 5 destinations and 1 sensor nodes randomly distributed in a 1 m 1 m fields. The entire network region is divided into 5 5 grids, where the grid size α is 2 m. The transmission range of sensor nodes is 5 m. We use GPSR [13] as the underlying geographic routing protocol. We use two metrics to evaluate CGM. The total control overhead is defined as sum of location service costs for all multicast members and tree construction overhead. The total hop count is defined as the number of transmission of sensor nodes from a source to all multicast members. This metric helps to know how efficient a multicast tree is by comparing other multicast trees. B. Impact of network sizes We evaluate the total control overhead and the total hop count in different network sizes from 1 m 2 to 2 m 2. For each network size, we also vary the number of sensor nodes from 1 to 2, so we respectively maintain the same node density. For each network size, we also vary the number of destinations from 5 to 2. Figure 5(a) shows the total control overhead for network size. From the results, we can see that growth of the total control overhead of is larger than the other protocols. The finding is caused by the following reasons. As the network size grows, the number of the grids proportionally increases. In, since all grid headers exchange the global location search message with a source node, the cost for exchanging is raised very much. is more robust than in terms of increasing network size. Nevertheless, has the detour problem of location query message which is always forwarded via the parent location server to the child location server. The proposed scheme has the lowest growth of overhead since the location search of member nodes is locally distributed although the network size increases. Figure 5(b) indicates the total hop count for increasing network size. As the network size increases, the total hop count also increases. It is because that increasing network size makes proportional increase of the number of the destinations. The proposed scheme has lower growth of total hop count than the other protocol. In both of and, since they configure the 2-tier multicast tree (between source and grid header, between grid header and multicast members), the data from a source node are forwarded to the multicast members of the tree via the grid header. The more the numbers of grid and members increase, the more the probability of the detour problem increases. C. Impact of grid size We evaluate the total control overhead and the total hop count in different grid size α from 5 m to 5 m when the network size is 1 1 m 2. In other words, the entire network region varies from 2 2 grids to 2 2 grids. Figure 5(c) shows the result of total control overhead impacted by increasing grid size. From the results, we could see that the grid size is inversely proportional to the total control overhead in all protocols. It is because that the smaller 2196

6 the grid size is, the more grids the network has. In the figure, when the grid is small, has the largest control overhead. It is because that increasing network size also makes the number of the grids increase. achieves the better performance than as it has the higher probability of location service message aggregation due to the hierarchy of location server. However, the figure shows that the total control overhead does not decrease greatly although the grid size increases. The reason is that the messages of location service are detoured unnecessarily when the grid size increases too much. In case of the grid size at 5 m, the proposed scheme has similar control overhead to. Since the entire network is divided into four grids, there is no difference between local search and global search. Figure 5(d) shows the result of hop count impacted by grid size. The total hop count of the proposed protocol does not increase sharply due to increasing grid size. On the contrary, the other protocols have highest hop count as the size of grid increase. It is because that the problem of detour path via grid header is more serious as the grid size increases in the other protocols. D. Impact of multi-way location search In this scenario, we investigate the effect of multi-way location search mechanism that allows multiple location queries in a grid. We evaluate the total control overhead and the total hop count in different network sizes with 1-way (basic mechanism), 3-way, and 5-way location search mechanism. Note that the 1-way mechanism is a basic scheme which allows only one location request per grid. The 3-way mechanism is extended from the 1-way mechanism with additional left and right side template queries. We also extend the 3-way mechanism to 5-way mechanism that additionally includes two diagonal template queries. To avoid infinite query creation, we define the additional query as a template query which do not allow consecutive location search. The simulation settings follow in Section IV. B. As shown in Fig. 5(e), we can see that the multiple location queries could cause the high total overhead. As the network size increases, growth of the total overhead also is larger. It is because that the cost for location search is increased as the number of target grids increases; also, these messages would be more duplicated as the network size increases. However, as shown in Fig. 5(f), we can see that if the number of target grids increases, the total hop count would be decreases as well as the growth would be lower with respect to the network size. Because they select the better proxy member node that makes the whole multicast paths to more optimal. In other words, the probability of optimal path selecting would be increased. In this graph, we could find the fact that is more interesting. There is no significant difference between 3-way and 5-way mechanism from 1 m 2 to 14 m 2 although 5-way search costs the more for the location service as shown in previous simulation. It means that the increase of the number of target grids is not always provides the more optimal multicast path. In other words, there is an upper bound of the number of target grids that enables to construct an optimized multicast tree as well as to take the least costs for location service according to the network conditions. V. CONCLUSION In this paper, we propose CGM, which exploits local search based member location service and consecutive multicast tree expansion algorithm. The local search based member location service is efficiently minimizing the searching costs for location acquisitions of multicast members, and the consecutive multicast tree expansion algorithm enables to avoid the data detour problem, therefore source s multicast data would be delivered with shorter paths. Our simulation results show that CGM is more scalable than other protocols in terms of network size, it also constructs efficient multicast tree that reduces the data detouring. For the future work, we will find an optimal value of the grid size to reduce the total hop count of an entire multicast tree. This is because that if the grid size increases, the costs for location search decreases, but the number of destinations per grid relatively increases, so an encoding overhead of a grid header might increases. We are also interested for supporting mobile destinations using CGM. REFERENCES [1] I. F. Akyildiz et al., A survey on sensor networks, IEEE Communications Magazine, Vol. 4, pp , Aug. 22. [2] S. M. Das, H. Pucha, and Y. C. Hu, Distributed Hashing for Scalable Multicast in Wireless Ad Hoc Networks, IEEE TRANSACTION ON PARALLEL AND DISTRIBUTED SYSTEMS, VOL. 19, NO. 3, Mar. 28. [3] S. Hwang, K. Lu, Y. Su, C. Hsien, and C. 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