Research and Advances
Architecture and Hardware

Information Dissemination via Wireless Broadcast

Unrestricted mobility adds a new dimension to data access methodology— one that must be addressed before true ubiquity can be realized.
  1. Introduction
  2. On-Disk versus On-Air Indexing
  3. Location-based Services
  4. Valid Scopes
  5. Object versus VS Indexing
  6. Conclusion
  7. References
  8. Authors
  9. Figures
  10. Sidebar: Glossary

The advent of sensor, wireless, and portable device technologies will soon enable us to embed computing technologies transparently in the environment to provide uninterrupted services for our daily life. With temperature and location sensors and wireless access points embedded in an environment, a person entering an environment can be automatically connected to the environment. All of his or her personal computing devices will then be adapted to the context, making all important information readily available for the user to tackle the tasks at hand. An important step toward the realization of this pervasive environment is to be able to disseminate timely and relevant information to the user anytime, anywhere. Here, we provide an overview of current research on ubiquitous data access and dissemination on wireless networks, emphasizing the differences between the traditional database and mobile environments, and describing some recent research on data broadcast on wireless channels.

Today, many wireless technologies such as Bluetooth, WiFi (802.11), and 3G global communication networks are available. Although these technologies vary in many aspects, we can look at them as an abstract cellular model where users access information through access points. The abstract model consists of a base station, a number of clients, and a number of channels. A client can acquire an uplink channel to send a request to the base station and receive the result from a downlink channel. While we assume that many users can listen to the same downlink channel to achieve broadcasting from the base station, an uplink channel is dedicated to one client for transmission at any time.

There are two basic approaches to disseminating data to mobile clients. In on-demand access, a mobile client submits a request to the server, which then returns the results to the mobile client directly via point-to-point connection. In periodic broadcast, data is broadcast periodically on a wireless channel. A mobile client listens to the broadcast channel and downloads the desired data from the channel according to a query issued from the user or a stored profile of interest on the client.

In on-demand access, the client is actively pulling data from the server. The server is responsible for processing the query and returning the answer directly to the client. On the other hand, periodic broadcast is equivalent to the server actively pushing data to the clients. The server determines what data should be broadcast and its schedule on the channel. The client listens to the channel and decides what should be retrieved.

In a lightly loaded system, where the number of queries generated from the clients is relatively small, on-demand access provides fast service because the waiting time for available channel is small. However, its performance deteriorates rapidly as the system workload increases because of the contention for bandwidth among the clients.

Data broadcast is an attractive alternative to on-demand access because it can broadcast data simultaneously to a large number of clients at a fixed cost. It is suitable for location-based services, which exhibit strong temporal and spatial locality in that clients within the vicinity and a certain time period tend to seek the same kind of information. For example, a museum may broadcast its hours or current attractions to its visitors [2], whereas on-demand, point-to-point access is used only for fulfilling occasional, ad hoc requests.

A broadcast system can be further classified as pure broadcast or on-demand broadcast. Clients in pure broadcast do not transmit any query to the server, instead they listen passively to the channel for the interesting data items. This works in a closed data space such as a stock quote system or when a profile of user interest is known in advance. Microsoft’s DirectBand Network, a low-bandwidth broadcast built on FM stations, is a pure broadcast system for subscribers who express their interests through a Web site [3]. On-demand broadcast allows the user to transmit a query as in the on-demand access, but the result to the query is broadcast together with results from other queries on the same channel. The client must filter out the designated result.

The mobile computing environment has a few characteristics that distinguish it from the traditional wired network. In addition to the traditional access latency, mobile clients have limited energy power. As a result, access techniques must take energy consumption into consideration.

In on-demand access, power consumption is dominated by the number of request transmissions because all the client must do is transmit the query and wait for the result, leaving all processing work to the server. In this regard, effective caching methods are vital in the reduction of the number of queries needed. For broadcast, since the client must scan the channel for the interested data item, its power consumption can be considered as directly proportional to the time it is listening actively to the channel, which is referred to as tuning time.

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On-Disk versus On-Air Indexing

Portable devices have limited power resources due to their small size. It is inefficient in terms of power consumption for the client to retrieve data by continuously listening to the channels until the desired data arrives. Imagine a client monitoring the price of a stock. The device must actively listen to thousands of stock symbols on the broadcast just to pick up one of them to display. Indexing can be used to guide the client in the listening process so it will activate only when the data arrives, thus reducing power consumption. To contrast, while a traditional database index maps a key to where the key value is stored in memory, an index for broadcast data maps a key to the time when the key value is broadcast.

In traditional database indexes, data is stored on disks or in main memories. The data is always available and can be accessed randomly. However, in wireless broadcast, data is available “on air” in the sense it is available on the wireless channel only transiently and must be accessed sequentially as dictated by the broadcast program. Search algorithms and index methods for wireless broadcast channels should avoid back-tracking. Existing database indexes were obviously not designed to meet this requirement and hence perform poorly on broadcast data.

When indexing is used, the broadcast cycle contains the index together with the actual data. When the client is looking for information, it tunes to the channel to locate the index for the broadcast cycle, from which it can obtain the broadcast time of the information and sleeps until it arrives. Compared to the total size of the data records, the index only contains the key names and thus is relatively small. It achieves the objective of reducing power consumption without significantly increasing the access latency.

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Location-based Services

Research on data broadcast is typically concerned with scheduling, indexing, and caching. In general, broadcast data is assumed to be location independent. For example, the price of a stock is time dependent but generally not location dependent. That is, no matter where the data is stored and where the user is located, the answer to a query on stock price is the same. However, in mobile computing where users move around, location becomes an important dimension of data. The answer to a query depends not only on the data values but also on the location where the query was issued. These queries are called location-dependent queries (LDQs). The data involved in answering LDQs is called location-dependent data (LDD). Here, we examine three types of LDQs:

  • Nearest-Neighbor Queries: Where is the nearest restaurant?
  • k-Nearest-Neighbor Queries: Where are the 10 nearest restaurants?
  • Window Queries: Where are the hotels within this county?

LDQs can be regarded as spatial queries, where location conditions are derived from the location of the user. For example, the location constraint for nearest-neighbor queries is the current location of the user and that for window queries is a rectangular region bound the country.

Spatial queries have been studied for quite a while. It is natural to assume that traditional solutions to spatial queries can be well applied to broadcast data. Unfortunately, this is not the case, as we will demonstrate.

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Valid Scopes

For LDQs, a data item has different data values in different geographic regions known as the Valid Scopes (VSs) of the data item. Take ZIP codes, for example. For the query “Give me the ZIP code of my current location,” clients in Princeton will receive NJ 08544—2087 while those in UCLA will get CA 90095—1361. A ZIP code can be regarded as a LDD item, and its VS is defined by the ZIP code boundary map (see Figure 1(a)).

VSs can be defined differently for different types of applications. The VS of ZIP code is defined by the postal office, whereas the VS of nearest-neighbor queries is defined by the Voronoi Diagram (VD) [1]. Figure 1(b) shows two international airports in New Jersey. The state is partitioned into two parts by the VD. For this simple example, it is the perpendicular bisector between the two airports. For clients in the region above the dashed line, Newark airport is the nearest one, while the Atlantic City airport is the nearest one for the clients below the dashed line.

VSs can also be defined symbolically. For example, when users move across cells in a cellular system, they must find out which cell they are in. The VS for this type of query is the radio coverage of the cells, as shown in Figure 1(c).

In general, VS does not have to be a contiguous geographical region. For example, when a user asks for the temperature at his or her current location, the answer may be 40°F. The VS for this answer could consist of disjoint regions, in which the temperatures are all 40°F (shadowed parts of Figure 1(d)).

We can see that some applications require the VSs to be known (for example, in the ZIP code and temperature examples), whereas some applications are primarily interested in object locations (for example, the locations of the airports or base stations). In the latter, queries can be computed purely based on the object locations. However, the major benefit of knowing the VS is twofold. First, it leads to the development of fast indexing and query processing algorithms, which we describe later. Second, once a query is answered, the client does not need to ask the same query again as long as he stays within the VS. This can be used as a spatial caching scheme to significantly reduce the number of queries submitted and is mandatory for the support of continuous queries [6, 7].

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Object versus VS Indexing

Indexing methods for LDD can be divided into two categories, namely, object-based and solution-based indexes. An object-based index is built on object locations, like a traditional R-tree, which records the coordinates of each object in the tree. An object-based index can be used to answer a broad range of queries, including NN, kNN, and window queries, since it records the raw location information about the objects, thus supporting any spatial queries.

A solution-based index is built for a specific type of queries. A precomputed solution space is first obtained for the type of query. Then, an index is built on the solution space. For the LDQs discussed here, the solution space is the VS, as illustrated in Figure 1. A solution-based index essentially indexes the boundaries of the VSs so that given a query point, it can identify the VS containing the query point and return the associated data (for example, the ZIP code information or temperature).

We can see that LDQs require an index to map a query point into one of the VSs and return the associated data of the VS. Since the VS of a query can be viewed as a set of polygons, the index essentially maps a query point to one of the polygons. Besides meeting the linear search requirement of wireless broadcast, the index must be small so it won’t lengthen the broadcast cycle, and thus the access latency. Most spatial indexes were developed for the traditional disk-based environment and, as such, are not optimized for any of these requirements.

A D-tree is a design motivated by the considerations noted here [5]. The basic idea is to index VSs based on the divisions that form VSs’ boundaries. For a space containing a set of VSs that are disjoint and complementary, it recursively partitions the space into two subspaces having a similar number of VSs until each space only contains one VS. Figure 2(a) shows a VD for fours objects. Polyline pl(v2,, v3, v4, v6) partitions the original space into P5 and P6, and pl(v1, v3) and pl(v4, v5) further partition P5 into P1 and P2, and P6 into P3 and P4, respectively. Each node of the D-tree contains some header information (for example, partition dimension), the pointers to the children, and the partition in the form of a polyline. The search algorithm starts from the root, and recursively follows either the left or right pointer according to the partition and the query point until a data pointer is reached. The D-tree has been shown to have a much better performance than the traditional indexes in terms of tuning time and access latency [5].

We can also see that a D-tree’s storage requirement is minimal in the sense the polylines are the minimum information needed for the reconstruction of the VS. Furthermore, given a query point, the search from the root node to the target leaf node is fixed and traverses only one branch, making it suitable for the linear broadcasting environment.

Hybrid index for NN queries. In principle, an LDQ can always be answered using a solution-based approach because for any type of query a solution space must exist and can be precomputed if we don’t consider the construction, update, and storage costs. However, the storage cost of a solution-based approach is definitely larger than that of an object-based approach. This is because a VS typically has many edges. It requires much more space to index the polygons than the objects, thus leading to poor search speed and high update cost if the index structure is not designed appropriately.

The grid-partition index is an efficient index for NN queries. Given a VD, the grid-partition index first partitions it into disjoint grid cells. For each grid cell, it identifies and stores the objects that are potential NNs of any query point that falls into the grid cell. In Figure 2(b), the VD is partitioned into grid cells of equal size, namely G1 to G4. Take G1 as an example, a query point within G1 can only have two potential NNs, namely, O1 and O2. As such, this association, <G1, O1, O2 > can be kept in the index.

The next problem for the realization of the grid-partition index is to identify the potential NNs for a grid cell. For NN search, each subspace in a VD defines the VS of that object. Hence, only the objects whose VSs overlap with that grid cell can be a potential nearest neighbor to a query point in that cell. However, this method requires the VD to be first constructed, which is expensive to create and maintain. To avoid the use of VD, a new algorithm was developed to find all potential NNs to a grid cell using the Delaunay Triangulation (DT), the straight-line dual of VDs [1]. The Delaunay Graph has the objects as vertices and edges connect two objects if and only if their VSs are adjacent. It has some nice properties: it can be computed without first obtaining the VD, thus avoiding the high cost of constructing the VD; it can be computed incrementally, thus lending itself to efficient updates.

The grid-partition index can be considered as a hybrid index, combining the advantages of solution-based and object-based indexes. It is a solution-based index because it indexes the VS of a query. However, instead of storing the boundaries of the VS, it stores the object locations. As a result, it is both fast and storage efficient.

General special queries. Although work has been done in finding VSs for kNN and window queries [6], the overheads in computing and broadcasting the VSs for different query types could be expensive. Here, we look at a different approach based on object indexing. Since VSs are not precomputed, it can support both kNN and window queries at the same time.

Since most spatial queries incur searching on objects close to each other, we can apply space-filling curves to arrange objects so that spatially near objects are put together on the broadcast. Figure 3 shows the basic Hilbert Curve of order 1 and 2. To derive a curve of order i, each vertex of the basic curve is replaced by an order (i — 1) curve, which may be strategically rotated and/or reflected to fit the new curve.

The numeric labels represent the positions of the objects on the Hilbert Curve. For example, point (1, 1) has the index value 2 in the order 2 curve shown in Figure 3(b). After the Hilbert Curve is obtained, an index (for example, a B+-tree) is built on the labels and serves as the index of the broadcast objects.

Given a query window, we can identify the first and last point within the window that the Hilbert Curve visits [4]. In the example shown in Figure 3(b), the rectangle in the dotted line is the query window, and points a and b are, respectively, the first and last point within the query window visited by the Hilbert Curve. We can conclude that points lying before a and after b along the Hilbert Curve are not inside the query window. Consequently, the client need only check the objects between a and b inclusive on the curve (that is, points between 2 and 13 inclusive), compute their actual Euclidean distances, and eliminate those not lying within the window (that is, objects 10, 11, and 12).

To answer a kNN query, we can first determine the smallest possible window containing the k nearest neighbors. Then, we can check the objects within the window to find the actual k nearest neighbors. This method requires scanning the Hilbert Curve twice. In the first scan, we identify the k objects around the query point on the Hilbert Curve. Based on these objects, we can determine the smallest window centered at q that encloses these k objects. This window is guaranteed to contain the kNN objects of q in the Euclidean space. The window approximates the search range within which further search can be done to obtain the actual answers.

In the second scan, the objects within the window are fetched and their Euclidean distances from the query point are computed to obtain the k nearest neighbors. The example in Figure 3(b) shows a 4NN query issued at point q. The first scan returns objects 5, 6, 8, and 9, which are the immediate objects before and after q on the Hilbert Curve. However, it is clear they are not actually the four nearest objects to the query point in the 2D-Euclidean space, but can be used to approximate the search range containing the results (see the dotted square circle centered at q in Figure 3(b)). The second scan will return all the objects within the search window. This is similar to the window query described earlier, but in this case the client checks the Euclidean distances to find out the four actual nearest objects (that is, objects 2, 4, 6, and 8 within the dashed circle).

We can see that space filling curves can answer these common spatial queries efficiently and are suitable for broadcast data. kNN queries require navigating the index information twice. This can be done by broadcasting the index twice within a broadcast cycle.

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Wireless networking and portable digital devices provide people with unrestricted mobility. Consequently, location becomes a very important property of data and introduces a new dimension to the design of data access methods. Traditional data access methods are not suitable for the mobile environment since they were designed for a wired environment where users do not usually move around. Here, we have analyzed the differences between data access methods in these two environments and provide an overview of recent research in dealing with spatial queries in the mobile environment with a particular emphasis on broadcast data. The combination of wireless broadcast, sensor networks, data/user mobility and context awareness will surely open up many interesting research issues toward the realization of pervasive computing environments.

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F1 Figure 1. Examples of valid scope (New Jersey).

F2 Figure 2. (a) D-tree and (b) Grid-partitioned indexes.

F3 Figure 3. Window query and kNN search based on Hilbert Curve.

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    2. Cheverst, K., Mitchell, K., and Davies, N. The role of adaptive hypermedia in a context-aware tourist guide. Commun. ACM 45, 5 (May 2002), 47—51.

    3. Microsoft Corp. What is the DirectBand Network? 2003;

    4. Moore, D. Hilbert curve;

    5. Xu, J., Zheng, B., Lee, W-C, and Lee, D.L.. Energy efficient index for querying location-dependent data in mobile broadcast environments. In Proceedings of the 19th IEEE International Conference on Data Engineering. (Bangalore, India, Mar. 2003), 239—250.

    6. Zhang, J., Zhu, M., Papadias, D., Tao, Y., and Lee, D. Location-based spatial queries. In Proceedings of the ACM SIGMOD International Conference on Management of Data. (San Diego, CA, 2003).

    7. Zheng, B. and Lee, D.L. Semantic caching in location-dependent query processing. In Proceedings of the 7th International Symposium on Spatial and Temporal Databases. (Los Angeles, CA, July 2001), 97—116.

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