An Enhanced Global Index for Location-Based Mobile Broadcast Services

Agustinus Borgy Waluyo, David Taniar, Bala Srinivasan

Faculty of Information Technology Monash University

Australia {Agustinus.Borgy.Waluyo, David.Taniar, Bala.Srinivasan}

@infotech.monash.edu.au

Abstract—This paper proposes a new global index structure and processing for location-dependent queries in mobile broadcast environments. The proposed scheme consists of two essential elements for addressing spatial queries in broadcast databases. These two elements are: (i) determine the client’s location in relevant to the spatial model adopted by the broadcast scheme, and (ii) obtain the required object, which corresponds to the location of the client as determined by the model. The global index’s concept will enhance the efficiency of the model. We explore the effectiveness of the proposed index scheme in single and multi channel environments. Performance comparisons with the earlier work through simulated-experiments have also been carried out and the results found have been promising.

Index Terms—location-dependent mobile broadcast query processing, enhanced global indexing scheme, location-based wireless data dissemination.

1. Introduction Location-dependent mobile information services

(LDMIS) have been gradually growing into maturity. Likewise, the demand of such services is expected to keep increasing in the years to come [1]. However, before the true potential of LDMIS is fully realized, there are still challenges remain to be addressed [2,3,4,5]. In LDMIS, the information requested by mobile clients is generally location-dependent; that is mobile client’s location is relevant to the information requested or the information requested is based on a particular location [1,6].

One of the most promising applications in LDMIS is nearest neighbour (NN) queries (e.g. finding the nearest petrol station). These queries can be performed in two different ways: one is to send the query to the server, and await the server to process and transmit the query results following the user’s location, and second is to listen to the broadcast channel while the server periodically disseminating the location of the objects of interest on air. In the latter, mobile clients simply select or filter the relevant data over the channel without worrying the large amount of power consumption required to send the request to the server. This method, which is commonly known as data broadcast services, has attracted much interest due to its scalability factor [7, 8]. The behaviour of the broadcast-based information services is unidirectional which means the server disseminates a set of data periodically to a multiple number of users [9,10].

Wenny Rahayu Department of Computer Science and Computer Engineering

La Trobe University Australia

W.Rahayu@latrobe.edu.au

With this mechanism, the requests from the clients are not known a priori. When clients are disconnected from the network during query processing, they can simply repeat the process when they reconnect without having to resend the request back to the server as is the case with the traditional client-server applications [11,12]. Therefore, data broadcast is a very promising mechanism for information delivery services in a wireless environment [13,14]. As the query relates to location-dependent queries, we refer this service as location-dependent data broadcast services.

The main challenge of location-dependent queries in mobile broadcast environment is to design an effective and efficient indexing method that is able to translate from multi- to one-dimensional space. It should be noted that broadcast indexing is always sequentially accessed [12].

A common metric to estimate the cost of data access in a mobile broadcast environment was introduced in [11]: (i) Access time: the time that elapses from the time a request is initiated until all data items of interest are received; (ii) Tuning time: amount of time the client spends listening for the desired broadcast data item(s). Tuning time comprises two modes: active and doze mode. Active mode is when the client listens to the channel for the desired data item which is costly in terms of power consumption, while doze mode is when clients simply turn to a power saving mode. Generally, the amount of power consumption is directly related to the tuning time [15].

We have reported our initial studies on global index model for ad-hoc queries in [16,17,18] as well as location-based queries in [19]. This paper enhances the global index structure for location-based query processing to better cope with a large number of objects in the regions. Furthermore, an extension of the model to serve NN-queries is also shown. Performance comparisons with our earlier work are presented in the experiments section.

In summary, the novel contributions of this paper are: (i) introducing a new index broadcast structure for location-dependent queries with particular on NN-queries; (ii) defining the new access and processing in the mobile client; and (iii) investigating the performance of the proposed approach and evaluating it against our earlier approach using simulated experiments.

The subsequent sections in this paper are organized as follows. Section 2 describes the related work of the

2010 24th IEEE International Conference on Advanced Information Networking and Applications

1550-445X/10 $26.00 © 2010 IEEE

DOI 10.1109/AINA.2010.59

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proposed technique; some background on the data broadcast and indexing is given in section 3; section 4 describes the proposed indexing scheme, and its performance measurements is carried out in section 5. Finally, section 6 concludes the paper.

2. Related Work

Spatial indexing schemes are designed to offer efficient location-based query performance. The existing techniques of spatial indexing can be classified into object-based and solution-based index [20]. Object-based indexing schemes will show its limitation when it comes to broadcast environment. This is mainly due to the linear access of data items in the broadcast channel [12]. Traditional databases do not have such constraint [21] and hence this object-based indexing method is not directly applicable.

For example, a R-tree indexing scheme [22] is given in Figure 1(a) and the structure in Figure 1(b). It is assumed that the query processing algorithm requires clients to visit the index nodes in the following order <Root, R2 and then R1>. However, the server may broadcast the index nodes in the order <Root, R1, and R2>. In this case, client needs to wait for the next cycle in order to obtain R1 and thus the access time will be significantly increased. This case is shown in Figure 1(c). Alternatively, clients can just simply access the index nodes sequentially as depicted in Figure 1(d). However, in this case clients might waste energy by accessing unnecessary nodes.

To Data Segment

R1 R2 root

O1 O2 O3 O4

(b) R-tree index structure

R1 R2 O1 O3 O2 O4 Data Segment R1 R2 O1 O3 O2 O4 Data Segment

broadcast cycle (x) broadcast cycle (x + 1)

R1 R2 O1 O3 O2 O4 Data Segment R1 R2 O1 O3 O2 O4 Data Segment

broadcast cycle (x) broadcast cycle (x + 1)

(c) Branch-and-Bound Search

(d) Sequential Search

O1

O2

O3

O4

R1

R2

(a) Minimum Bounding Rectangle (MBR)

Figure 1. R-tree architecture.

A solution-based index called D-tree indexing scheme for location-dependent data broadcast environment has been proposed by [23]. The basic concept of D-tree technique is to index data regions or valid scopes of data items based on the divisions that form Valid Scope’s boundary. The index is then broadcast interleaving with

relevant data item over a broadcast channel. Client can tune in to the channel, check the valid scopes with current location, find out when the correct data item will be broadcast, and retrieve the data item on air. It uses polygon as a representation of the valid scope.

Our earlier work on data broadcast spatial index was related to the solution index. The index model was called global index for location-dependent queries [19]. However, the earlier model will show its limitation when it comes to a large number of objects in the region, which makes it difficult for the client to determine the desired object. Moreover, it was not appropriate for NN-queries, which will be addressed in this paper.

3. Preliminaries In this section, we present brief preliminaries in data

broadcasting, and indexing.

A. Data Broadcast Architecture

Fig. 2 shows the general architecture of mobile broadcast services for LDMIS. There are three main entities in the broadcast server, namely (a) spatial database, (b) objects database and (c) index/data broadcast generator and scheduler.

Broadcast Server

Wireless Broadcast Channel

Listening and retrieving the desired data items

from the channel

MobileClients

Transmitter

Spatial Database

Objects Database

Data Broadcast Generator and Scheduler

Spatial Index Constructor

Figure 2. Data broadcast architecture.

The tasks of broadcast server can be defined as follows: (i) Firstly, the spatial index constructor selects objects from the database, maps these objects with the spatial information from the spatial database and constructs the associated index following the pre-defined scheme, (ii) data broadcast generator and scheduler inserts the relevant objects into the broadcast packets, and generates the broadcast program, (iii) Finally, broadcast scheduler in the server forwards the broadcast program to the transmitter, which will disseminate the data items and its indexes to the mobile clients in the cell. One complete transmission of a broadcast program is called a broadcast cycle [11].

In LDMIS, it is also required for the mobile client to be equipped with the location positioning device. The most well-known and commercially used satellite positioning system nowadays is global positioning system (GPS). The GPS provides location identifier in a form of coordinate tuple (ie. longitude and latitude). This paper assumes utilization of the GPS positioning system.

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B. Index Broadcast

The index in mobile data broadcast specifies when the requested objects will arrive in the channel, which is rather different from the traditional database system whereby the index points to a certain location memory of the data. The significance of index in data broadcast lies in its ability to help clients reducing the tuning time by providing accurate information for a client to tune in at the appropriate time for the required data [24,25]. In the case of no-index broadcast, the client has to listen to each of the data item that arrives in the channel. This will consume a lot of energy especially when there is a large number of data item to broadcast. On the other hand, when index broadcast is employed, client is able to save power while waiting for the data item to arrive. To incorporate this scheme, some form of directory is broadcast along with the data. The clients are to obtain the index directory from the channel and use it in subsequent reads. The index enables mobile clients to conserve energy by switching to “doze” mode and back to “active mode” when the data is about to be broadcast [26,27].

4. A Novel Index Structure and Processing: Proposed Scheme

There are two most important points to consider when designing the index scheme for NN-queries in wireless broadcast environment given the linear access of the data: (i) to enable users locating their current location in respect to the spatial model that is being deployed, (ii) to obtain the relevant objects associated with the user location following the model and extracts the nearest object from the user’s current location.

Our indexing approach takes into consideration the two requirements, and how we address each of them can be explained as followed. A. Server processing • Index Segment Construction

In order to be able to design an appropriate index, we need to incorporate the concept of valid scope. Valid scopes define the boundary of an area or region within which the query result is considered valid. We utilize the square as a shape representation of the valid scope, which is considerably easier to construct since it has same length and width. Consequently, the information sent to represent the valid scope boundary will be small, thus mobile clients will have a more efficient query processing.

The valid scope helps the server to translate from multi-dimensional to one-dimensional space. The geometric location, which depicts the valid scope is represented in two dimensional coordinate [28]. When client initiates a query, the validity of the broadcast objects will be checked by comparing the valid scope of the object instances with client’s current location. An example of the square valid scope is shown in Figure 3(a). In the figure, there are four regions: region 1 <P1,L1>, region 2 <P1,L2>, region 3 <P2,L1> and region

4 <P2,L2>. Each of the regions has a valid data instance that is D1, D2, D3, and D4, which is attached to region 1, 2, 3, and 4 respectively.

Having a set of data instances and their relevant valid scopes, the issue of querying location dependent-data is how to retrieve the right object efficiently.

Our index is constructed based on the division of regions. Firstly, the entire regions of interest are divided into four squared areas. Secondly, each square is sub-divided into multiple smaller squares until each square covers one valid object only. The partition of sub-spaces is represented by straight lines forming a square (x-coordinate dimensional and y-coordinate dimensional). Figure 3(a) illustrates the partition.

Figure 3(b) depicts our index construction based on the space partition in Figure 3(a). The index node and its description are given in Figure 3(c). Table 1 defines the description of each attribute in the index node.

P1, L1 P2, L2P2, L1 P1, L2

(a) Square Valid Scopes

(b) Index Structure

(c) Index Node

I_1 1 x1 x2 y1 y2 y3

To Data Segment

p_type n_level n_id x-axis y-axis upper_left_ptr upper_right_ptr lower_left_ptr lower_right_ptr root_level_ptr partition partition (if any)

P1

L1

P2

L2

x1 y2 x2

y1

y3

D1 D2

D3 D4

Figure 3. Index model.

TABLE 1. Index node: description Attribute Description p_type Packet type (i.e. index or data) n_level Node level n_id Node unique id x-coordinate Partition x-axis coordinates that indicates

the partition y-coordinate Partition y-axis coordinates that indicates

the partition Upper_left_ptr Index pointer Upper_right_ptr Index pointer Lower_left_ptr Index pointer Lower_right_ptr Index pointer Root_level_ptr Index pointer to root level (if any)

The index node contains the packet type, which indicates if the packet is either index or data segment. The node level specifies the level of the node in the index tree (from top to bottom). It is then followed by the node id, which signifies the unique id of the node within the corresponding level. Next in the node is the x-dimensional partition and y-dimensional partition. The partition always starts from x-partition followed by y-partition. Following which is the pointers, which indicates the upper-left and right region as well as the lower-left and right. Lastly, the root level pointer

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identifies the pointer to the root index node (this is only applicable for nodes other than the root node itself). This root level pointer guides the client to the beginning of the index node in the broadcast cycle.

The pointers of the non-leaf node point to the relevant child nodes, while the leaf-node pointer goes to the data segment that contains the objects in the areas. These pointers contain the time when the child-node or the related data packet will arrive in the channel.

P1, L2

p1, l1 p1, l2 p2, l1 p2, l2

P1, L1

I_1 1 x1 x2 y1 y2 y3

To Data Segment

P2, L2 P2, L1

P1

L1

P2

L2

x1 y2 x2

y1

y3

D1,1 D2

D3 D4

(a) Square Valid Scopes

(b) Index Structure

D1,2

D2,1 D2,2

y1,1

y2,1

y3,1

x1,1 x2,1

p1

p2

l1 l2

To Data Segment

I_1 1 x1 x2 y1 y2 y3

Figure 4. Index model-extended.

Our indexing scheme is also relatively simple to maintain. When there are updates of objects in the region (i.e. addition), our scheme will check if there are any other objects in the same valid scope, if so then the partition process into four squared areas will be executed and the index tree and node is updated accordingly. Otherwise, the partition remains, and only the index node is updated to include the new object. An example of this scenario is depicted in Figure 4.

As can be seen from Figure 4, region <P1,L1> contains additional valid objects. Subsequently, the initial region will be further partitioned into four squared areas until each area contains one valid object only. The index tree is then to be updated to take into account the new regions. The parent tree remains the same but the pointer to the initial region will point to a child node instead of the data segment.

Similarly, with the deletion of objects, our scheme will check if there are other objects within the four squared areas of the same node. If none, the scheme will remove the index node accordingly. The index construction process is defined in the algorithm 1.

Algorithm 1: Index node construction Input: A set of objects locations. Output: x-partition and y-partition coordinates of each index node. Procedure: 1. Sort the objects locations in an increasing

order from their leftmost x-coordinates; 2. Store the first object and end object x-

coordinate <xmin, xmax>; 3. Sort the objects locations in an increasing

order of their bottom y-coordinates; 4. Store the first object and end object y-

coordinate <ymin, ymax>; 5. construct a 2x2 squared area based on the

<xmin, xmax> and <ymin, ymax>; 6. Store the x1,x2 and y1,y2,y3 coordinates of

the square; 7. Insert the coordinates in the first level

of index node; 8. Check the number of objects in each of the

squared area; 9. Store the number of objects and the area

into array; 10. For each squared area { 11. Do while the number of object > 1 { 12. do procedure 1 to 5 inside the corresponding area; 13. store the x1,x2 and y1,y2,y3 coordinates of the square; 14. insert the coordinates in the subsequent level of the relevant index node; }} End Procedure

• Global index model The index and data segment can be broadcast in a

single channel or multiple channels. In the single channel, the index interleaves with the data segments, whereby in the multiple channel the index and data segment are assigned into separate channel. The following Figure 5 depicts the two possible scenarios.

(a) Multiple channel

Index Segment

DataSegment

Index Segment

Data Segment

Data SegmentData Segment Data Segment

Data Segment Data Segment

Index Segment Index Segment

Index Segment Index Segment Index Segment

Index Segment

Data Segment

Index Segment

Data Segment

(b) Single Channel Figure 5. Single and multiple channels.

The global index model adopts multiple channels scheme whereby the entire index structure is partitioned into a number of disjoint and smaller indices. Each of these small indices is placed in a separate channel. The global index scheme has some degree of replication. Each channel is occupied with a different part of the index structure, and the overall structure of the entire index is still preserved.

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In this case the index structure is partitioned and broadcast to multiple index channels. Each index channel is dedicated to broadcast nodes of the main four specific regions only. For example, index channel 1, 2, 3 and 4 contains the index nodes for region P1, P2, P3 and P4, respectively.

A traversal algorithm to allocate the index nodes into the index channels needs to be applied. The index is partitioned based on the data region. The Postorder traversal algorithm for the global index partition model for location-dependent queries is depicted in algorithm 2. The ownership rule of each index node is that the index channel owning a leaf node also owns all nodes from the root to that leaf. Consequently, the root node is replicated in all index channels, and non-leaf nodes may be replicated in some channels. Algorithm 2: Post Order Traversal with Index Allocation Scheme (Regions based)

Procedure TraverseGlobalIndexLDQ(T:tree) Is Begin If T = null then Return; Else TraverseGlobalIndexLDQ(T.left); TraverseGlobalIndexLDQ(T.right); VisitTree(T); End if; End Traverse GlobalIndexLDQ;

Procedure VisitTree(T:tree) is Begin If the index node is in the top level of index tree-structure Assign the index node across entire index channels; Else Check the index nodes region with the index-partitioning attribute (regions based); Assign the index node into designated index channel; End if; End VisitTree;

• Data Segment Construction Each data packet in the broadcast channel contains a

valid object in a region as well as the surrounding objects from the neighborhood regions. If the partition size is 3x3, then the number of objects in one data packet is 9. This is important as mobile client may be anywhere in the region, and the object belongs to that region may not be the closest one.

An example of this scenario is given in Figure 6. In this example, the car in Figure 6(a) is closer to object D1 rather than D2. If the data packet only contains the object of the same region with the user, then the result may be incorrect. Having contained other objects in the surrounding regions in one data packet will enable user to locate the nearest object accordingly. Figure 6(b) depicts the packet structure and Table 2 shows the description of the packet. Algorithm 3 explains the construction of the data segment.

TABLE 2. Data node: description Attribute Description p_type Packet type (i.e. index or data) p_id Packet ID objecs_location Data pointer

L2 L1

P1

P2

x1 y2 x2

y1

y3

D1

D2

D3 D4

(a) A scenario of incorrect result

p_type p_id objects_location

(b) Data node structure Figure 6. A sample scenario and data node structure.

Algorithm 3: Data node construction Input: x-partition and y-partition coordinates of each leaf-index node, objects’ location coordinates. Output: objects in the data node. Procedure: 1. For each of the smallest region given in the index leaf-node { 2. Find the associated object coordinate within the region; 3. Find <= 8 number of objects closest to the object in the region; 4. Store all the object coordinates; 5. Insert the coordinates into data node; } End Procedure

B. Client processing for NN-queries Client processing when querying NN-objects based on

our index model can be described as follows: - Client tunes into the broadcast channel. - Client finds his/her location by following the index

node and interprets the geometrical coordinates given in the index.

- The right index is determined by locating the data region that contains the query point. This index indicates a time value of the relevant objects to arrive in the channel.

- Client tunes into the appropriate channel, and switch to doze mode while waiting for the data packet.

- Client switches back to active mode just before the desired data packets arrives, retrieves the location information of the objects contained in the packet and determines the closest object in respect to the client’s current location.

Further details on this client processing mechanism for NN-queries are defined in algorithm 4.

Algorithm 4: Client processing Input: Index node, data node. Output: Nearest object Procedure: 1. Check the type of node; 2. Wait until the root index node arrives in the channel; 3. Follow the region pointer given in the

node using the current location coordinate as a reference;

4. Switch to doze mode between nodes; 5. Store the objects given in the data node; 6. Find min<current location|object location>; End Procedure

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5. Performance Evaluation This section studies the performance of our indexing

method in terms of the query access time and client’s tuning time. This includes comparison with our earlier scheme in [19] based on the simulated experiments. The simulation is carried out using Planimate, a discrete event simulation tool written in C++ [29]. The simulation environment is set to apply exponential distribution for the packet transmission rate. We run the simulation up to twenty five iterations, and derive the average result accordingly. The parameters of concern are given in Table 3.

TABLE 3. Parameters of concern Parameters Value

Size of packet 2 KB p_type 2 bytes

level size 3 bytes id size 3 bytes

Node Pointer Size (each) 5 bytes Data Pointer Size (each) 5 bytes x-axis Coordinate Size 4 bytes y-axis Coordinate Size 4 bytes

Bandwidth 64 Kbps

A. Proposed Scheme (single and multiple channels) In this case, we evaluate the query access time and

client’s tuning time performance when deploying the proposed index scheme with and without the deployment of global index. There are three different numbers of regions to study (16, 25 and 64 regions).

(a) Access time performance

(b) Tuning time performance Figure 7. Access and tuning time (proposed scheme) – single and multiple channels

The aim of this case is to compare the performance of the proposed index in a single channel as well as multiple channel environments whereby the latter adopt the concept of global index scheme. We can see from Figure 7(a), the deployment of enhanced global index in multiple channel environments offers a substantially lower access time. It also helps to minimize the impact of the increase in the number of regions. A sharp increase of access time using a single channel can be seen from the change of 25 regions to 64. On the contrary, the enhanced global index in multiple channels is able to absorb the impact of additional objects with much less increase of access time. Figure 7(b) shows the tuning time performance of the proposed scheme. Since the index structure is essentially the same between the two, which means clients traverse in the same index tree, the tuning time results the same accordingly.

B. Proposed vs. Existing Scheme (single channel)

In this second case, we would like to compare the index performance of the proposed model with the existing one [19] in the context of single channel.

Figure 8. Access and tuning time (proposed vs. existing)– single channel

This case merely focuses on the indexing structure, and so is the access time does not include the data segment and neither the global index concept. As shown in Figure 8, the proposed index occurs 2 to 3 times as lower as our existing index scheme. Similarly, our proposed index in this paper offers much less tuning time as compared to the earlier model. This will bring a significant power saving for the client when obtaining location-dependent objects on air.

C. Proposed vs. Existing Scheme (multiple channels)

This third case compares the performance of the proposed enhanced global index against the earlier global index model in [19] to serve location-dependent queries in multiple channel environments. The global index model is adopted to improve the efficiency of the scheme. The main point of this case is to observe the performance of our new enhanced global index concept as opposed to the existing model. This study concerns

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with 25 regions. As depicted in Figure 9, we can see that the enhanced global index is able to lower the access time of the earlier model with approximately two and half times. Similarly, the tuning time performance of the new index scheme is about one and a half times lower than its predecessor.

Figure 9. Access and tuning time (proposed vs. existing) – multiple channels

6. Conclusion and Future work Location-dependent queries are one of the most

important applications in mobile environments. These queries can be processed using traditional client-server or through data broadcast mechanism. In this paper, we have introduced a new index structure and access for location-dependent queries with particular attention on NN-queries in mobile broadcast environments. We have also applied the concept of global index to further improve the efficiency of the index model.

We have studied the performance of the proposed index scheme with respect to the query access time and client’s tuning time. From the experiments, we can conclude that our proposed scheme offers promising results in both access and tuning time. This is shown by significant improvements in the two performance indicators from our earlier method. With the enhanced global index, mobile client is able to obtain the NN-objects in a much faster way while preserving a considerably amount of power. For future work, we will extend the proposed scheme to accommodate other type of queries such as kNN [30,31], RNN [32] and range query [33,34], as well as context-sensitive [35].

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