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A Distributed Spatial Index for Error-Prone Wireless Data Broadcast

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A Distributed Spatial Index for Error-Prone Wireless Data Broadcast Noname manuscript No. (will be inserted by the editor) A Distributed Spatial Index for Error-Prone Wireless Data Broadcast Baihua Zheng1, Wang-Chien Lee2, Ken C. K. Lee2, Dik Lun Lee3, Min Shao2 1 Singapore Management University, Singapore. [email protected] 2 Pennsylvania State University, USA. {wlee,cklee,mshao}@cse.psu.edu 3 The Hong Kong University of Science and Technology, Hong Kong. [email protected] Abstract Information is valuable to users when it is technologies can be classified into two basic approaches: available not only at the right time but also at the right on-demand access and periodic broadcast. On-demand place. To support efficient location-based data access access employs a pull-based approach where a mobile in wireless data broadcast systems, a distributed spa- client initiates a query to the server which in turn pro- tial index (called DSI ) is presented in this paper. DSI cesses the query and returns the result to the client over is highly efficient because it has a linear yet fully dis- a point-to-point channel. On-demand access is suitable tributed structure that naturally shares links in differ- for lightly loaded systems in which wireless channels and ent search paths. DSI is very resilient to the error-prone server processing capacity is not severely contended. wireless communication environment because interrupted search operations based on DSI can be resumed eas- On the other hand, periodic broadcast requires the ily. It supports search algorithms for classical location- server to proactively push data to the clients over a ded- based queries such as window queries and kNN queries icated broadcast channel. This approach allows an ar- in both of the snapshot and continuous query modes. bitrary number of clients to access data simultaneously, In-depth analysis and simulation-based evaluation have and thus is particularly suitable for heavily loaded sys- been conducted. The results show that DSI significantly tems. Wireless data broadcast services have been avail- out-performs a variant of R-trees tailored for wireless able as commercial products for many years, e.g. Star- data broadcast environments. Band (www.starband.com) and Hughes Network (www. Keywords: mobile computing, location-based query, wire- hughesnet.com). Recently, there has been a push for such less broadcast, error resilience systems from the industry and various standard bodies. For example, born out of the International Telecommu- nication Union’s (ITU) International Mobile Telecom- munications “IMT-2000” initiative, the Third Genera- tion Partnership Project 2 (www.3gpp2.org) is develop- 1 Introduction ing Broadcast and Multicast Service in cdma2000 Wire- less IP network [36]. Another example is the MSN Direct With the ever growing popularity of smart mobile de- Service (www.msndirect.com) based on the smart per- vices and rapid advent of wireless technology, the vi- sonal objects technology (SPOT) and the DirectBand sion of pervasive information access has come closer to Network. With a continuous broadcast network using reality. While information is important to users, it is FM radio subcarrier frequencies, mobile devices (e.g., valuable only when available at the right time, right smart watches, Navigators and PDAs) can continuously place. Indeed, location is a very important requirement receive timely information such as stock quotes, airline to pervasive information access. The demand for access schedules, local news, weather, and traffic information. of location dependent data (e.g., pollution index, local Moreover, systems for Digital Audio Broadcast (DAB) traffic conditions, restaurant locations, navigation maps, (www.worlddab.org) and Digital Video Broadcast (DVB) weather condition, etc.) fosters a tremendous application (www.dvb.org) are capable of delivering wireless data base of location based services. services. News also reported that XM Satellite Radio Today, there are many wireless technologies (e.g., (www.xmradio.com) and Raytheon have jointly built a Bluetooth, WiFi, UMTS, Satellite, etc.) that could be communication system that would use XM’s satellites to integrated into a seamless, pervasive information access relay information to soldiers and emergency responders platform. Logically, information access via these wireless during a homeland security crisis. 2 Baihua Zheng et al. A number of studies have addressed various system – It provides multiple search paths for same objects issues of wireless data broadcast [3,7,8,16,18,31,41]. Re- and shares links on common search paths to minimize cently, several proposals on delivering location-based data the bandwidth overhead of index information. via wireless data broadcast have been suggested, e.g., – It is very resilient to the inherent error-prone commu- enhanced R-tree based air index by Gedik, et. al. [12], nication environment in wireless data broadcast. The and the DAYS project led by Kumar [2,10]. In this pa- clients can resume, instead of restarting, the query per, we address the demand of location-based data by processing operation shortly after an error occurs. proposing a novel on-air spatial index, called Distributed – It efficiently supports a variety of location-based queries, Spatial Index (DSI), to support location-based queries including window query and k nearest neighbor (kNN) issued by mobile users in wireless data broadcast sys- query, in both of the snapshot and continuous query tems. We focus on two classical location-based queries1: modes. window queries and k nearest neighbor searches. The The issues of developing efficient and error-resilient former is to retrieve all the queried objects that fall in- search algorithms for location-based queries are partic- side the query window centered at a query point, and ularly challenging. The designs of traditional spatial in- the latter is to retrieve the k objects located nearest dexes, e.g., R-Trees, are based on resident storage such to a query point. In both cases, the query point could as memory and disk which supports backtracking in tree be the current user location or on an anticipated user traversal. Unfortunately, the linear property of wireless moving trajectory. In the design of DSI, we paid special broadcast environments imposes a serious constraint of attentions to the following three aspects: 1) the perfor- sequential access on R-trees. Thus, our search algorithms mance requirement with respect to energy conservation for DSI are based on the principle of following the linear and access efficiency; 2) the inherently unreliable and access order but refining the search space and filtering error-prone wireless communication; and 3) the support out unqualified objects efficiently. Insights obtained from of general location-based queries. To cater for client mo- our analysis have led to a solution based on space refin- bility, we consider not only the typical snapshot queries ing and objects filtering and result in very efficient query in which a query point is fixed, but also the continuous processing algorithms. queries in which the query point moves along an antici- To the best of our knowledge, this is the first work pated movement trajectory. A continuous query is used addressing location-based query processing issues in the in a situation where future client movement is projected. error-prone wireless communication environment. It is Taking the continuous nearest neighbor (CNN) query as observed that a node in traditional tree-structured in- an example, a client may want to know the nearest gas dexes is only pointed by one parent node. As a result, the stations along an anticipated path from her current lo- only access to a node will be lost if its parent/ascendant cation to New York city. The returned result contains node could not be reached successfully. The search per- pairs of the nearest gas stations and their corresponding formance penalized by packet loss can be significant. path segments (i.e., each pair consists of a gas station g Alternatively, DSI, by converging multiple search paths and the segment where g is the nearest gas station). This into one linear index structure, enables a fast resume of information can be cached in a client and is particularly the search process. An extensive performance evaluation useful for the client to avoid continuously issuing queries in both error-free and error-prone wireless communica- while moving. Similarly, a continuous window query is tion environments shows that the proposed DSI performs to figure out pairs of qualified objects and their corre- significantly better than a variant of R-trees tailored for sponding segments. The proposed DSI is an index struc- wireless data broadcast environments. ture that facilitates highly efficient and reliable access of A preliminary version of this work has been reported location-based data in wireless data broadcast systems. in [21], where only the snapshot queries are considered. It exhibits the following properties: In this paper, for the sake of completeness, we extend the work to answer window queries and kNN queries in – It organizes data objects in a certain linear order the more challenging continuous query mode. In addi- (e.g., Hilbert Curve) that naturally fits the media of tion, we provide an analytical model to study the per- wireless data broadcast to facilitate efficient indexing formance of a primitive search algorithm, namely Energy and scheduling for broadcast. Efficient Forwarding (EEF), in both error-free and error- – It has a fully distributed structure that allows query prone environments. The simulation is also extended to processing to start immediately and thus minimize examine more performance aspects, including power con- the unnecessary waiting time for the arrival of the sumption, computation cost and error resilience, on large starting point of a search path. The property sig- datasets. Finally, we also discuss how to extend DSI to nificantly shortens the access latency of the queried support queries on multiple data types. data. The rest of this paper is organized as follows.
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