Hierarchical Location Service for Large Scale ... - Semantic Scholar

Hierarchical Location Service for Large Scale Wireless Sensor Networks with Mobile Sinks Yan Yan 1, 2 Baoxian Zhang 2,3 Hussein T. Mouftah 4 Jian Ma 5 1

Institution of Computing Technology, Chinese Academy of Sciences, Beijing 100080, China 2 CCCE, Graduate University of Chinese Academy of Sciences, Beijing 100049, China 3 Key Lab of WSN, Shanghai Institute of Microsystem and Information Technology of Chinese Academy of Sciences, China 4 School of Information Technology and Engineering, University of Ottawa, Ottawa, ON KIN 6N5, Canada 5 Nokia Research Center, Beijing 100013, China Email: [email protected], [email protected], [email protected], [email protected] Abstract—Location-based routing has been a critical and efficient routing strategy in large Wireless Sensor Networks (WSN) with mobile sinks. However, the performance of location-based routing highly depends on how position information of mobile sinks are managed and updated. This is typically the task of location service. In this paper, we present the design of a hierarchical location service for WSNs with mobile sinks. The main design objective is to greatly reduce the communication overhead for providing location service while maintaining high routing performance. Detailed simulation results are used to verify the high performance of our designed location service.

taking the role of location server(s) could change over time. However in mWSNs, sensor nodes are static, so selected location server(s) are fixed. On the other hand, an effective strategy to infrequently adjusting location servers is needed to avoid the overuse of energy at sensor(s) taking such roles. Third, since sensor nodes are static, a mobile sink could easily bind itself to an anchor node (which is a sensor node) to hide the short-range movement of the sink and then reduce location update frequency at the cost of slightly increased length of data forwarding paths. In MANETs, such binding is meaningless because the so called anchor can move freely.

Keywords- wireless sensor networks, mobile sinks, hierarchical location service, location-based routing.

In this paper, we focus on the design of scalable location service for supporting efficient geographical forwarding in mWSNs. Our service design utilizes an inherent characteristic of mWSNs, i.e., sinks are mobile while sensors are static, in the selection of location servers. Our contributions in this paper are as follows. First, we design a scalable hierarchical location service such that the location updating structure due to a mobile sink is hierarchically built, which can largely reduce the updating cost. Further, the hierarchy is built in a way such that no much routing performance penalty is introduced. Second, the overall updating cost in mWSNs with multiple sinks is further greatly reduced by enforcing Voronoi scoping (see [5]) such that each mobile sink only updates its location to the location servers closer to it than to any other sinks. Simulation results demonstrate that our design can achieve significant reduction in terms of protocol overhead while maintain good packet forwarding efficiency.

I. INTRODUCTION In a Wireless Sensor Networks (WSN), sink(s) can be either static or mobile. Existing work [1][2] shows that mobile sinks have many advantages over static sink. Without causing confusion, we refer to a WSN with one or multiple mobile sinks as mWSN hereafter. A representative example for multiple mobile sinks is a group of soldiers with a handheld PDAs patrolling in battlefield to gathering information from the sensor network deployed on the ground. However, sink mobility can cause unexpected changes of network topology, and the increase of sink number may bring excessive overhead. Therefore, the performance of mWSNs highly depends on routing protocols designed to adapt to the mobility of sinks. Location-based routing [3][4] has been considered as an effective routing strategy for large WSNs. It takes advantage of the location information of nodes when making decision on routing and thus scales well. Frequent network-wide location update can improve the location accuracy at the expense of excessive communication overhead. Thus to support efficient location-based routing in mWSN, a key issue is the design of scalable efficient location service. Most existing location services were dedicated for mobile ad hoc networks (MANETs). The following differences between MANETs and mWSNs require the design of new location services for the latter.

Numerous location services [6]-[14] have been proposed to address the location tracking and retrieval problem in wireless multi-hop networks.

First, MANETs adopt peer-to-peer communication mode while mWSNs use sensors-to-sink communication mode. All sinks are equivalent for sensor nodes in terms of data reporting. Second, in MANETs, since any node can move freely, node(s)

Existing location services can be divided into two types: Flooding-based and rendezvous-based. Flooding-based [6] protocols do not scale well in terms of network size since their implementations cause frequent network-wide location

The rest of this paper is organized as follows. In Section II, we briefly review related work. In Section III, we present the detailed design description of the proposed protocol. In Section IV, we conduct simulations to evaluate the performance of our protocol by comparing it with related work. In Section V, we conclude this paper. II.

RELATED WORK

updating. Rendezvous-based protocols set up the mapping relationship between nodes and several other nodes, called location server. Periodical or event driven location updates will be stored in these location servers or part of them. Rendezvousbased protocols can be further divided into two approaches: quorum-based and hashing-based. Good examples of quorumbased protocol were proposed in [7]. In hashing-based protocol, location servers are chosen by a well selected hashing function. Hashing-based protocol can be further divided into two categories: flat-based [8] and hierarchical-based [9]-[11]. Hierarchical-based protocols divide the network into a hierarchy thus scales well to large networks. More detailed description about the taxonomy and reference of location service can be found in [11]. All the above-mentioned location services were designed for MANETs and they perform ineffectively if directly employed in mWSNs. Recently, some protocols [12]-[14] addressed the issue of providing location services in WSNs. However, none of the above protocols provide effective methods that can establish a good relationship between protocol overhead and number of sinks, and further how to reduce the update cost as the number of mobile sinks grows. III.

PROTOCOL DESCRIPTION

A. Overview In this Section, we present the design of a hierarchical location service. Our designed location service has following desirable features. First, it can provide the location of the nearest sink to a querying sensor and thus the data transferring cost is kept the minimum. Second, by using hierarchical network architecture and introducing the Voronoi scoping to handle the multi-sinks scenarios, the overall communication overhead for location updating can be greatly reduced. Third, the frequency of location updating is greatly reduced by binding each mobile sink with an anchor node. Fourth, uniform power depletion among sensors can be achieved by using hashing function with a time era parameter. Therefore the protocol is very suitable for large mWSNs. We begin by making following reasonable assumptions. We focus on two-dimensional square field, which is covered by a large number of homogeneous uniformly distributed static sensor nodes. One or more mobile sinks move randomly in the deployment field. A uniform (virtual) ID is assigned to all sinks for hashing. All sensor nodes and mobile sink(s) are capable of knowing their respective positions. Upon detecting an interested event, a sensor tries to report it sensed data to a nearby sink immediately. B. Protocol Design A communication network can be modeled as G=(N,E), where N is a set of m sinks and n sensor nodes, V represents the set of n sensor nodes, S represents the set of m sinks. E represents the set of edges in the network. dij represents the Euclidean distance between two nodes i,j∈N. The deployment field is divided into to K2 equally spaced basic grids (K ≥ 1), and each basic grid has a location server inside it. LS represents the subset of nodes that serve as location servers. We have

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Figure 1. Illustration of how a sink selects its location servers set and constructs its updating SPT. The shaded grid indicates where the sink is located. The integer number in each grid indicates the level of the location server in the grid hierarchy. An arrow indicates an edge in the SPT.

LS⊂N and |LS| = K2. Each grid is assigned an integer grid coordinate. A grid (x,y) represents the grid at the intersection of x-th row and y-th column. 1) Hierarchy Creation and Location Servers Selection All nodes know the basic grid partitioning of the network field. The relative position of the location server inside each basic grid can be obtained by hashing sinks’ uniform virtual ID and the time era. The sensor node nearest to the hashed position will be the location server of that grid. We add time era to the hashing function so that the hashed position in a grid will change over time infrequently. The grid hierarchy is built by using the following rules. a) b)

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Location server in any basic grid in the network is also an order-1 location server in the hierarchy. Order-2 location servers must be in those grids with coordinates fitting the form of (ak,bk), where a,b are positive integers, and k is a positive integer parameter that determines the density of the high level location servers. In the paper, we set k to three. In this case, the distance measured by grid number between the closest pair of order-2 neighbors is three. Order-n location servers must be in those grids with coordinate (akn-1, bkn-1).

Each mobile sink will select a subset of LS as its location servers set, each of which stores the location of this sink, and construct a Shortest Path Tree (SPT) covering this set for its location updating. The choice of location servers set for a sink depends on the position of the sink and the grid hierarchy. Figure 1 illustrates how a sink selects its location servers set and constructs its updating SPT. Before proceeding further, we first clarify the concept of sibling location servers. For an order-n location server in grid (u3n-1, v3n-1), we choose the order-n location servers in grids ((u+x)3n-1, (v+y)3n-1), where u,v are positive integers and x,y∈{±1,0}, as its sibling location servers, which are also the 8 (8=k2-1 for k=3) closest location servers on the level n.

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Figure 2. Illustration of how our location service works in an mWSN with two mobile sinks. The shaded grid indicates where the sink locates. The integer number indicates the level of the location server in the grid hierarchy. The isolated shaded regions represent the Voronoi scopes of two sinks, respectively.

Now, we describe how the disseminating tree for a sink’s location updating is built. For a sink in a grid (u,v), it will first recruit all its order-1 sibling location servers of the grid (u,v) as its order-1 updating location servers, then the sink will find out the order-2 location server among its recruited nine order-1 location servers and then further recruit all sibling location servers of this order-2 location server. This process continues until the highest level location server in the hierarchy is reached. After a sink gets the location servers set, it can construct an SPT for its location updating. The implementation of above rules will create a hierarchy with the following desirable features. First, the choice of location server set can guarantee that a sink’s location information will be spread uniformly around it. Second, the SPTs built for two different sinks at different locations will surely merge at their lowest-level common ancestor in the hierarchy and a joint dissemination structure is then created. That is, those branches of these two trees for covering those location servers higher than (or equal to) this common ancestor are the same. This result can be easily extended to the multisinks case. Thus the above hierarchical natures make the Voronoi scoping strategy applicable for reducing the update cost caused by multiple mobile sinks in a distributed manner. 2) Location Updating A major protocol design objective is to effectively reduce the communication overhead of location updating in mWSNs. We introduce the Voronoi scoping in order to suppress the updating scopes due to different sinks and therefore minimizing the overall location updating overhead while maintaining full network coverage and high data packet forwarding efficiency. For our designed location service to work correctly, each location server l∈LS must keep all the sinks’ information that it receives recently. When a sink s∈S initiates a location updating process, s first compute its location server set and construct the SPT rooted at itself. Then s sends an update message containing its present position to its downstream on-tree location servers.

When a location server l receives an update message from sink s (or from its upstream location server on the SPT), it will first check if s will be its closest sink by comparing dls with distance to its known closest sink. If l chooses s to be its (new) closest sink, then l must locally compute the SPT rooted at s as s has done initially. If l is a leaf node of the SPT, it will stop the dissemination process. Otherwise l will locally determine whether it needs to forward the update message to its downstream neighbors. If l received another update message from a downstream neighbor before and knows that this downstream neighbor has a closer sink than s, l will not forward the update from s further along this tree branch. This is the so called mutual suppressing when multiple sinks are present. Note that in cases multiple sinks reside in the same grid, only the first update message from one of them will be disseminated and others are simply suppressed. With the above mutual suppressing, the update message issued by a sink s will only be disseminated to those location servers, whose distances to s are shorter than to any other sinks. It can greatly reduce the location updating scope for each mobile sink. Figure 2 illustrates the Voronoi scoping of a twosink sensor network. 3) Sink Mobility To effectively reduce the frequency of the location updating caused by sink mobility, each sink chooses an AP (anchor point) as its temporary position to hide its short distance movement. AP is responsible for managing a local path to the particular sink and forwarding packet that is subsequently receives (if any) to the sink. Before initiating a location updating, a mobile sink chooses the nearest neighboring sensor node as its AP and uses the AP’s position as its current location. When the sink’s (geometrical) distance to its binding AP exceeds a predetermined max AP association distance L, it will choose another sensor in its vicinity as its new AP and trigger a new location updating. In general, L will be set to be greater than the maximum nodal transmission range. Then based on the result of [8], we can get the estimated sink update time interval Ts, which is the mean time that a randomly moving sink at speed v may stay in a circular area with radius L.

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4) Location Querying If a sensor node x∈V does not have a sink in its direct neighbor list, it needs to send a query to its nearest location server (which could be at any level) to retrieve the location of a mobile sink. The queried location server may not have sink information. If so, it needs to further forward the query to its nearest high level location server. This process will continue until the query reaches a location server with sink information in its cache, which will look up its locally stored sink information and find the sink that is closest to x. It then replies a message containing the sink information to the last hop (either the last hop location server or the querying sensor). The process will be repeated until the reply reaches x. To ensure the correctness of the sink information, location server must execute a timeout mechanism: If a location server

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learns a piece of sink information for free during a query-reply cycle, it must timeout this information after an estimated sink update time interval Ts since after that sink may have changed its AP. The continuous receipts of queries at a location server l from its children location server means there are heavy datareporting activities in l’s vicinity. To improve the routing performance, l will send its locally-stored sink information to all its children location servers every Ts. This can effectively reduce the query latency and cost. 5) Location Server Migration Sensor nodes have limited power supply, so the role of location servers needs to switch among sensor nodes to avoid excessive power depletion at a single node. As mentioned earlier, we feed the hashing function with a time era parameter so that the position of location servers will change with time. On one hand, we need the position of location servers to be stable for a certain long period of time. On the other hand, we need to rotate the position of location servers for energy draining balancing. Therefore, the granularity of time is a parameter affecting the performance of location service. 6) Failure Handling Consider a node l is currently serving as a location server. If node l fails, queries destined to l will fail either. To handle this, a backup location server can be chosen as the second nearest sensor node to the hashed position of location server. Moreover, the sink location information stored at a location server can also be sent to its neighbors in its beacon packets (periodically or upon detecting a change) to enhance service reliability.

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IV.

SIMULATION RESULT

In this Section, we simulate our protocol using ns-2. Hereafter, we refer to our designed protocol as MLS. We compared our protocol with GLS [9]. In the implementation of both protocols, only sink nodes can move and trigger location updating while sensor nodes are static. The transmission range of nodes was set to 100 meters. We used GPSR [4] as the underlying geographical routing protocol. Each simulation run lasts for 100 seconds, and each result is averaged over 5 random network topologies. All random topologies were generated by the setdest tools in ns-2. Each sensor node randomly generates a query message and sends a data packet after received the corresponding reply message. The switching interval of location servers was set as 50 seconds. A. Impact of Sink Number In the test, 1000 sensor nodes were uniformly distributed in a 3600×3600 m2 square field. Both protocols divide the field into 18×18 basic grid. Max AP association distance L for MLS and the location update threshold for GLS is 350m. Sinks’ mobility follows the Random Waypoint Model with max speed 30 m/s. We will not consider the underlying routing forwarding cost because it is decided by the geographical routing mechanism. Thus we just use the number of control messages generated by the location service to evaluate its performance. Figure 3 shows the average number of update messages generated by the MLS and GLS. From the figure, we can see that the number of updates generated by GLS grows linearly with the number of sinks. In contrast, the corresponding overhead by MLS increases much slower. This results meets

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In this paper, we designed a new location service for wireless sensor networks with mobile sinks. Our service design has low update and query overhead and scales well in terms of network size and sink number while maintains high data forwarding path quality. Simulation results verify our expectation of the high performance of our designed protocol. The designed location service is simple, efficient, scalable, and therefore suitable for large-scale mWSNs.

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our expectation, and the small growth is mostly caused by the fact that the rate at which the AP disassociations occur is proportional to the number of sinks and each AP disassociation likely to trigger a regional location updating. Simulation results show that MLS is suitable for multi-sink networks in particular when the number of sinks is large. Figure 4 shows the average distance that a query message takes before reaching a location server with sink information in its cache. From the figure, we can observe that the average query distance of MLS decreases with the number of sinks. This is because more low level location servers in grid hierarchy will be updated as sink number increases. The results also show that MLS can effectively reduce the query cost and latency. B. Impact of Network Size Next we evaluate the impact of the network size. We keep the node density and grid side length unchanged and increase the number of basic grids from 18×18 to 27×27, thus the number of nodes and the networks size will grow with the grid number. Figure 5 shows that the designed hierarchical location service scales well with the network size. C. Impact of Max AP Association Distance and Sink Mobility We next evaluate the impact of the sink velocity and the maximum AP association distance L.

ACKNOWLEDGMENT This work was partially supported by High-Tech Research and Development Program of China under Grant No. 2006AA01Z207.

The conference participation is supported by Nokia Bridging the world Program. REFERENCES [1]

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[9]

In this test, five sinks move in an 18×18 grids network as in the previous test. We varied the max velocity of sinks from 10, 20, 30, 40, 50 to 60m/s and also varied the maximum AP association distance from the default value of 250 to 300, 350, 450, 550, 650m.

[10]

Figure 6 shows the impact of the sink velocity and the max AP association distance L on protocol overhead. As we can observe from the Figure, when L is large, the impact of sinks velocity to protocol overhead is insignificant. That is because sinks may spend a long period of time in roaming within the Max AP association distance range. So increasing L will effectively reduce the update cost with a penalty in the length of data packet forwarding paths.

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Figure 7 shows the impact of the max AP association distance L on the average length extra paths taken by data packets. The extra distance will increase with L although it is much shorter than L.

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