Wireless Geographical Data Broadcasting for Urban Intelligent Information Infrastructures (UI 3 )

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1 Wireless Geographical Data Broadcasting for Urban Intelligent Information Infrastructures (UI 3 ) Jianting Zhang Le Gruenwald School of Computer Science, the University of Oklahoma 200 Felgar Street, Norman, OK 73019, USA jianting@ou.edu, phone: ABSTRACT This paper proposes using data broadcasting to build urban intelligent information infrastructures. It presents a conceptual framework for integrating cellular network location management into geographical data broadcast to provide efficient location dependent services. Within the context of the proposed framework, the paper then discusses the four major research issues: generic and geographic data broadcasting, location-dependent (continuous) query processing, moving object databases, and mobility management in cellular network. 1. INTRODUCTION The last decade witnessed the rapid development of computer networks and their applications. Urban areas possess the densest computer networks that have played important roles in many aspects of society. In fact, the Internet and WWW based services have been an indispensable part of our everyday lives. As the network goes to wireless and mobile-enabled which greatly free the mobility restrictions of both computing devices and people who use them, mobile computing becomes increasingly popular. However, the limited bandwidth for wireless communication is very likely to be a bottleneck for years to come. Data broadcasting has the excellent scalability and is suitable for massive information dissemination. Wireless data broadcasting will be a very important approach for building intelligent urban information infrastructure. In fact, a significant amount of public geographic information is read-only and relatively stable, and thus is suitable for push-based data broadcasting. In this paper, we propose using wireless digital broadcast for building Urban Intelligent Information Infrastructures (UI 3 ). One of the most significant features of digital broadcasting is that it allows automatic filtering and information integration. Unlike in analog-based broadcasting that only allows a mobile host to listen to one or two channels simultaneously due to human physical limitations, a mobile host can monitor hundreds of digital broadcast channels easily. Besides pre-defined filtering criteria, since current cellular systems provide cell-level location precision that ranges from hundreds of meters to a few miles ([HREF1, HREF2]), it is also possible to retrieve only location-dependent information. This will make retrieved information more relevant in addition to saving tune in time as well as handset power consumption. There are two most unique features of Geographical Data Broadcasting (GDB) in addition to generic data broadcasting schemes. First, geographical information is multidimensional and its geometric components have continuous values. To the best of our knowledge, there is very little existing work on multidimensional and continuous data broadcasting. Second, query against broadcast geographical information is location dependent. We are not aware of any previous work on location dependent query (LDQ) against broadcast data. In the near future, it is likely that most UI 3 users are cellular network subscribers. We are also interested in integrating mobile host mobility profiles in cellular network Location Management (LM) into location dependent services provided by UI 3. The advantages are two folds. First, a mobility profile can provide a client s position up to cell size precision that can be used for location dependent queries without additional positioning device support. Second, mobility profile statistics can help to decide what to broadcast, how to broadcast and when to broadcast to improve the overall performance of broadcast systems.

2 The objective of this paper are to introduce a framework that can be use to integrate location management and location dependent queries over broadcast geographical information. The rest of this paper is arranged as follows. Section 2 introduces some background on generic and geographical data broadcasting. Section 3 presents a conceptual framework on integrating cellular network location management and geographical data broadcast to provide efficient location dependent services. Section 4 provides an overview of the research issues related to the proposed framework. Finally, Section 5 summarizes the paper and discusses future work directions. 2. GENERIC AND GEOGRAPHICAL DATA BROADCASTING Data broadcasting (Imielinski, 1997) usually includes a server, channel and clients. Server is responsible for data organization that transfers possible multidimensional data into a one-dimensional sequence and puts the sequence on the broadcast channel. The channel is responsible for transmitting data to massive clients. Clients listen to the channel in one or more broadcasting cycles and filter the corresponding data units (bcasts) that they are interested in There are two very important parameters in data broadcasting systems. The Tune in Time (TT) is defined as the amount of time spent by a client listening to the channel. This will determine the power consumed by the client to retrieve the required data. The Latency (or Access Time AT) is defined as the time elapsed (on the average) from the time a client requests data to the point when all the required data is downloaded by the client. The trade off between tune in time and latency is well known (Imielinski, 1997). Two extreme strategies exist for data broadcasting. For the access opt strategy, there will be no index and only data is broadcast to minimize access time. While for the Tune Opt strategy, extensive indexing is used to minimize unnecessary active channel listening. Due to energy limits for mobile clients, often TT is more important than AT. In practice, the server often puts the index of the data along with the data itself on the channel. Having multiple copies of indices and distribute them among data segments allow a client to begin listening in the middle of a broadcast cycle and thus can reduce access time. However, adding the index increases the volume of the data to broadcast, which subsequently increases tune in time and latency in some degree. When the index is very complex, the additional tune in time needed for listenning to the index is also considerable. Numerous indexing techniques have been developed in the past thirty years (Gaede, 1998). Although many of them are originally designed for disk or memory access, some of them are used for data broadcasting. B-tree is the most popular one for one-dimensional indexing both in disk and memory based data organization. Since broadcasting is also one-dimensional, it is relatively easy to apply B-Tree to broadcast sequencing. However, when multidimensional data such as geographical information is involved, a good mechanism to transfer multidimensional attributes to one-dimensional for sequential broadcast is needed. Fig. 1 One-Dimensional and Multidimensional Index Trees and Range Queries One of the major differences between searching on one-dimensional index and multidimensional index is shown in Fig. 1. In the one-dimensional case, search is a single-path from root-to-leaf while in the

3 multi-dimensional case, a partial traversal of the index tree is often the case. For range query, in a onedimensional index (take B-Tree for example), we can search the lower end of the range first and follow the leaf nodes sequentially until the key value is outside the scope of the upper bound of the range. This is because the key values of the index nodes are one-dimensionally continuous. In a multidimensional index (take R-Tree for example), the nodes that satisfy range query criteria might be clustered and not continuous which results in increased access time. Mapping multidimensional geographical information to onedimensional broadcasting sequence is a major challenge for efficient broadcast indexing and querying processing. 3. INTEGRATING LM AND LDQ OVER GDB: A CONCEPTUAL FRAMEWORK A simple conceptual framework of integrating user mobility profiles provided by location management of cellular networks and location dependent queries over broadcast geographical information is shown in fig.2, where arrows show data flows. Mobile Host Location Update Paging Point-to-Point Communication Broadcasting Network Location Management Server Access Pattern Update and Broadcast Scheduling Application Server Geographical Data Server Profile Update Mobility Profile Database Geographical Database Fig. 2 Conceptual Framework: Integrating LM and LDQ over GDB There are four major components in the proposed framework. The Location Management Server (LMS) has the functionality similar to that of the location database in current cellular systems. In addition, it compresses streamed location update information into a mobility profile and stores it in the Mobility

4 Profile Database (MPD). The MPD will reduce paging costs by more targeted paging as well as help broadcast scheduling by identifying hot data items and access patterns. Moving Object Database techniques will play a key role in location management where mobile hosts are treated as moving objects. The Geographical Data Server (GDS) is responsible for deciding what to broadcast, how to broadcast and when to broadcast. Besides broadcasting, GDS is also responsible for individual client requests in the form of point-to-point communication. This will allow clients to retrieve data that are not scheduled to broadcast or need too long access time to download in the broadcast sequence. A mobile host can obtain its current location when it registers with a base station. This location information can be used for location dependent queries over broadcast geographical information, such as pre-fetching and caching. We will defer geographical database and MPD management issues to later studies. As the starting point, we assume no point-to-point communications are used and mobile hosts only monitor broadcast sequence. The geographical data to broadcast has been predefined and they are point type data of service locations. A mobile host knows its current geometric location but no further mobility profile is used in location dependent queries. No cache and pre-fetch is used at the mobile hosts for query processing either. These assumptions reduce our problem to location dependent queries over broadcast point data sets in a simple manner. Nevertheless, the conceptual framework is generic enough for future expansions. 4. RESEARCH ISSUES AND RELATED WORK Wireless data broadcasting as a means of building an urban intelligent information infrastructure introduces many new research issues. In our proposed conceptual framework, several hot research topics currently undertaken are seamlessly linked together. We have identified the following topics relating to our proposal: 1) Indexing, scheduling and client query processing of broadcast generic and geographical data; 2) Location-dependent (continuous) query processing; 3) Data model, storage, indexing and querying of moving object database; 4) Location and mobility management in mobile networks; 5) Public geographical information organization for scalable and efficient dissemination; and 6) Wireless location acquisition techniques for handsets with or without special positioning devices. We will present a brief overview on the first four topics in the context of our proposed framework. Additional references can be found in [HREF 4] Generic and Geographical Data Broadcasting The most significant advantage of wireless data broadcasting is its excellent scalability. Digital broadcasting of newspaper to individual subscribers can be traced back as early as 1985 when personal computer is still not powerful enough to accommodate several kbps data transfer rate (Gifford, 1985). However, little progress is made in digital data broadcast in large-scale applications during the past twenty years. People still tune to their analog radios from time to time for weather and road condition reports. Although it becomes more and more common to check such information on the Internet, digital broadcasting over wireless network is still under proposal ([HREF 3]). However, the data integration and automatic filtering capability of digital data broadcast is very tempting. The huge volume and multidimensional nature of geographical data impose additional challenges for wireless broadcasting. (Aksoy, 1998) gave an introduction on data broadcast. (Imielinski, 1997) and its related work (Imielinski, 1994a; Imielinski, 1994b) presented both a framework and several indexing methods on data broadcasting over the air. Although these indexing methods are mostly for one-dimensional data, they can be the basis for further study. Data broadcast indexing is also addressed in (Shivakumar, 1996; Khanna, 2000). A hybrid indexing technique combing a tree index and signatures is proposed in (Hu, 2001a). These three indexing techniques are further extended to multi-attribute data broadcast (Hu, 2001b). However, they proposed using one-dimensional indexing methods consecutively for multidimensional attributes indexing which is inefficient (Kriegel, 1984; Gaede, 1998). The first paper on spatial data broadcasting we found is (Hambrusch, 2001). It studied the execution of spatial queries on broadcast tree-based spatial index structures. However, it assumes the client side has very limited memory and its focus is to reduce latency caused by having to listen to multiple broadcast cycles due to insufficient memory to hold all index nodes, which is not likely a serious problem in today s handset memory configurations. None of the research on tree index broadcasting takes access frequency into consideration. Existing studies on broadcast scheduling, although not specifically on geographical data, might be used for geographical data broadcast. Several algorithms have been proposed for pull-based broadcasting under different settings (Xuan, 1997; Aksoy, 1999; Jiang, 1999; Sun 2001; Karakaya, 2001). Push-based algorithms for broadcasting in asymmetric communication environments are also presented (Vaidya, 1999;

5 Kenyon,2000). Broadcast Disk was proposed for broadcast data with different access frequencies (Acharya, 1995; Chen, 2000). The similarity between broadcast scheduling and fair queue policy had been observed and lead to efficient push-based broadcast scheduling schemes (Hameed, 1997; Hameed 1999). Special interests have been shown in balancing push-based and pull-based broadcast schemes (Acharya, 1997; Pinotti, 2001). (Su, 1999) proposed methods for scheduling data broadcast with low average response time experienced by users for both push-based and pull-based cases. Several methods for dynamically determining broadcast content at the server side have been presented in (Datta, 1999). It also proposed a number of retrieval protocols that a client might use to download data from broadcasts Location-Dependent (Continuous) Query Processing (Seydim 2001) presented preliminary work on formally defining location dependent query (LDQ) processing. (Ren 2000) proposed using semantic caching to manage location dependent data in mobile computing. Simulation results showed that their Furthest Away Replacement (FAR) cache replacement policy has better performance than conventional page caching. (Zheng, 2001) specifically addressed location-dependent queries in a multi-cell wireless environment. They used a Voronoi diagram to construct an index and a semantic cache for improving data reusability. They also proposed several scheduling methods for handoff clients. Both (Ren 2000) and (Zheng 2001) assumed a point-to-point data communication architecture and a client can provide exact location when submitting location dependent queries. Although a client can explicitly and continuously submit its LDQs and retrieve results, (Lam, 2001) addressed another kind of Location Dependent Continuous Query (LDCQ). In this case, once a client has submitted a LDQ, the system will automatically update the client s location and retrieve the updated information. It is actually a combination of location management, LDQ and cache management at the client side. Compared to naïve continuous LDQ, LDCQ can significantly save wireless bandwidth by not transferring duplicated query results over wireless channels. The difference between LDCQ and Moving Object Databases is that the former assumes data involved in queries are static and the client is moving while the latter assumes data involved are moving objects and the client might be static. To the best of our knowledge, no work has been done on LDCQ on moving objects Moving Object Databases Each mobile host can be treated as a moving object. However, in current cellular network location management, a mobile host has limited position precision that is defined by cell sizes. It has zero-length moving trajectory since its location information is overridden by a later location update. In our proposal, we actually propose a probabilistic and limited-length moving object trajectory as mobility profile that challenges existing research on moving objects. Although several data models (Güting, 2000; Forlizzi, 2000), storage (Chon, 2001), indexing (Kollios, 1999; Agarwal, 2000; Saltenis, 2000) and query processing methods (Sistla, 1997; Wolfson, 1999; Pfoser, 2000; Vazirgiannis, 2001) have been proposed for moving objects, they do not fit into our applications very well. First, none of these methods addressed probabilistic moving settings. Second, they assume moving object trajectories are incrementally added without limits while our trajectories are constantly consolidated to keep the trajectory lengths limited and constant to reduce storage overhead and ensure quick query response. Third, most existing studies assume that location information in moving objects is geometric values while it could be a string of cell IDs in our proposed framework Profile-Based Location Management Some reviews on mobility management and location management are given in (Plassmann, 1994; Akyildiz, 1998; Kruijt, 1998; Wong 2000; Campbell, 2001). We are interested in profile-based location management schemes. Generally speaking, profile-based location management schemes in wireless mobile networks can be classified into four categories. The theoretical pattern based schemes (Liu, 1995; Lei, 1998; Liang, 1999) try to fit the mobility of a mobile host into some pre-defined physical/statistical models. Position based models (Tabbane, 1995; Pollini, 1997; Wu, 2001) use simple statistics of access frequencies of location update from a moving log. Local movement based schemes (Akyildiz, 1996; Mishra, 1997; Wan, 1999; Naor, 2000) use local movement statistics, such as number of cells crossed, distance, time span, speed, direction, as mobility profile. These movement features are dynamically generated in each location update cycle and do not use movement information in the subsequent location update cycles. In

6 contrast, the global association based schemes (Scourias, 1997; Sen, 1999) make use of global association degrees between different positions. Some of the above methods require offline and multiple passes data processing and thus are not suitable for our applications. In contrast, we need online and one-pass approaches to improve efficiency. We also prefer path-based mobility profile to position-based one since it has more semantics and generally higher prediction accuracy. Other desirable features include delaying location update when possible for later bulk update and using piggyback mechanisms for delivering location update packets to the server to save bandwidth consumptions. 5. SUMMARY AND FUTURE WORK DIRECTIONS In this paper, we proposed to build an urban intelligent information infrastructure based on geographical data broadcasting. We introduced the background on generic data broadcasting and described unique features of geographical data broadcasting. We then presented a conceptual framework that integrates location management and location dependent queries over broadcast geographical information and discussed the research issues involved. One of our primary tasks for this research in the near future is to implement a prototype to demonstrate the feasibility of the proposed conceptual framework. As a first step, we are now working on performance comparisons of several geographical broadcast schemes in terms of access time. Primary results show that proper replication of index/data and prioritized sequencing based on application semantics can significantly improve location dependent query performance at the client side. We are also working towards location dependent continuous query and query over broadcast moving objects. REFERENCES 1. Swarup Acharya, Rafael Alonso, Michael J. Franklin, Stanley B. Zdonik: Broadcast Disks: Data Management for Asymmetric Communications Environments. SIGMOD Conference 1995: Swarup Acharya, Michael J. Franklin, Stanley B. Zdonik: Balancing Push and Pull for Data Broadcast. SIGMOD Conference 1997: Pankaj Agarwal, Lars Arge, Jeff Erickson, Indexing Moving Points, PODS, 2000: , 4. D. Aksoy, M. Altinel, R. Bose, U. Cetintemel, M. Franklin, J. Wang, S. Zdonik, Research in Data Broadcast and Dissemination, AMCP 1998: Demet Aksoy, Michael J. Franklin: R W: a scheduling approach for large-scale on-demand data broadcast. IEEE/ACM Transactions on Networking, 7(6), 1999: L.F.Akyildiz, J.McNair, J.Ho, H.Uzunalioglu, W.Wang, Mobility management in current and future communications networks, IEEE Network, 12(4), 1998:39-49, 7. I.F.Akyildiz, J. Ho, and Y.-B. Lin, Movement-based Location Update and Selective Paging for PCS networks, IEEE/ACM Trans. Networking, 4(4), 1996: A. T. Campbell and J. Gomez, IP Micro-Mobility Protocols, ACM SIGMOBILE Mobile Computer and Communication Review (MC2R), 4(4), 2001: Ming-Syan Chen, Wen-Chih Peng: Dynamic Generation of Data Broadcasting Programs for a Broadcast Disk Array, CIKM 2000: Hae Don Chon, Divyakant Agrawal, Amr El Abbadi, Storage and Retrieval of Moving Objects, Mobile Data Management (MDM) 2001: Anindya Datta, Debra E. Vandermeer, Aslihan Celik, Vijay Kumar, Broadcast protocols to support efficient retrieval from databases by mobile users, ACM Transactions on Database Systems (TODS), 24(1),1999: Luca Forlizzi, Ralf Hartmut Güting, Enrico Nardelli, Markus Schneider: A Data Model and Data Structures for Moving Objects Databases. SIGMOD Conference 2000: V.Gaede, O.Günther,Multidimensional access methods, ACM Survey, 1998: D. Gifford et al., The Application of Digital Broadcast Communication to Large Scale Information Systems, IEEE J. Selected Areas in Comm., 3(3), 1985: Ralf Hartmut Güting, Michael H. Böhlen, Martin Erwig, Christian S. Jensen, Nikos A. Lorentzos, Markus Schneider, Michalis Vazirgiannis: A foundation for representing and querying moving objects. TODS 25 (1), 2000:1-42

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